From 21d39fe4bad1f0cf22537ba3814d5286865b4d04 Mon Sep 17 00:00:00 2001 From: EliLawrence Date: Wed, 11 Sep 2024 20:13:19 +0000 Subject: [PATCH] deploy: 744ce03cdd6af82d56e12ac70f6c475ee4dbc7bf --- FAQ.html | 113 +++++++++++++++++++++----- access.html | 16 ++-- common_formatissues.html | 30 ++++--- common_qc.html | 14 ++-- darwin_core.html | 4 +- data_qc.html | 62 +++++++-------- data_sharing.html | 2 +- dataquality.html | 130 +++++++++++++++--------------- dna_data.html | 36 ++++----- eml.html | 10 ++- format_emof.html | 22 +++--- format_event.html | 2 +- format_occurrence.html | 2 +- identifiers.html | 9 +-- index.html | 2 +- ipt.html | 10 +-- ipt_admin.html | 2 +- manual.pdf | Bin 8824543 -> 8835865 bytes mofreports.html | 5 +- name_matching.html | 38 +++++---- search_index.json | 2 +- style.css | 2 +- vocabulary.html | 166 ++++++++++++++++++++------------------- 23 files changed, 387 insertions(+), 292 deletions(-) diff --git a/FAQ.html b/FAQ.html index 2174ae9..23f704e 100644 --- a/FAQ.html +++ b/FAQ.html @@ -347,7 +347,7 @@

1.5.0.1 GeneralWhere can I make suggestions for improvements on this Manual?
  • -Where can I find OBIS related training videos? +Where can I find OBIS related training videos?
  • What are the responsibilities of OBIS node managers? @@ -406,6 +406,15 @@

    1.5.0.3 Formatting DataHow does data flow in OBIS?

  • +
    + +Can I add polygon data to OBIS? + +

    Yes, polygons, lines, or combinations of polygon, line, and/or point data can be added to OBIS by using the footprintWKT field. This is can be used to record tracks, transects, tows, trawls, habitat extent, or when an exact location is not known. Midpoints of polygons can be added to the required fields decimalLongitude and decimalLatitude.

    +WKT strings can be generated using the OBIS Map Tool. The tool can also calculate a midpoint and a radius, which is added to decimalLongitude, decimalLatitude, and coordinateUncertaintyInMeters respectively. There is also an obistools R function to calculate the centroid and radius for WKT polygons. +
    +
    • +
  • What should I do if I do not have the data for required fields by OBIS?
  • @@ -474,15 +483,6 @@

    1.5.0.3 Formatting DataDNA data guidelines for more details.

    • -
  • -
    - -How do I document occurrences from unknown species, those new to science, or those with temporary names? e.g. Eurythenes sp. DISCOLL.PAP.JC165.674 - -

    Occurrences unknown or new to science should be documented according to recommendations by Horton et al. 2021. You should populate the scientificName field with the genus, and in identificationQualifer provide the ON sign ‘sp.’. However you must also indicate the reason why species-level identification is unavailable. To do this, supplement ‘sp.’ with either stet. (stetit) or indet. (indeterminabilis). If neither of these are applicable, (e.g. for undescribed new species), add a unique taxon identifier code after ‘sp.’ to identificationQualifer. For example Eurythenes sp. DISCOLL.PAP.JC165.674.

    -Please avoid simple alphanumeric codes (i.e. Eurythenes sp. 1, Eurythenes sp. A). Similar to creating eventIDs or occurrenceIDs, you should strive to provide more complex and globally unique identifier. Identifiers could be constructed by combining higher taxonomic information with information related to a collection, institution, museum or collection code, sample number or museum accession number, expedition, dive number, or timestamp. This ensures namestrings will remain unique within a larger repositories like OBIS. It is also recommended to include these temporary names on specimen labels for physical specimens. -
    -
    • @@ -507,6 +507,38 @@

    1.5.0.4 Vocabulary +

    Taxonomy

    +
      +
    • +
      + +How do I document occurrences from unknown species, those new to science, or those with temporary names? e.g. Eurythenes sp. DISCOLL.PAP.JC165.674 + +

      Occurrences unknown or new to science should be documented according to recommendations by Horton et al. 2021. You should populate the scientificName field with the genus, and in identificationQualifer provide the ON sign ‘sp.’. However you must also indicate the reason why species-level identification is unavailable. To do this, supplement ‘sp.’ with either stet. (stetit) or indet. (indeterminabilis). If neither of these are applicable, (e.g. for undescribed new species), add a unique taxon identifier code after ‘sp.’ to identificationQualifer. For example Eurythenes sp. DISCOLL.PAP.JC165.674.

      +Please avoid simple alphanumeric codes (i.e. Eurythenes sp. 1, Eurythenes sp. A). Similar to creating eventIDs or occurrenceIDs, you should strive to provide more complex and globally unique identifier. Identifiers could be constructed by combining higher taxonomic information with information related to a collection, institution, museum or collection code, sample number or museum accession number, expedition, dive number, or timestamp. This ensures namestrings will remain unique within a larger repositories like OBIS. It is also recommended to include these temporary names on specimen labels for physical specimens. +
      +
      • +
    • +
      + +Can scientificNameID be populated with an identifier (e.g. WoRMS LSID) representing an unaccepted taxon name? + +

      Yes. The identifier in scientificNameID should always correspond with the name that is in the scientificName field, even if the name is an unaccepted name in WoRMS. For example, the species name “Holothuria mammiculata” was provided, but this name is unaccepted in WoRMS. The accepted name is “Holothuria (Stauropora) pervicax Selenka, 1867”. In this case scientificNameID should correspond to the original name with LSID urn:lsid:marinespecies.org:taxname:529968 because the ID must correlate with the name as recorded in scientificName.

      +
      +
      • +
    • +
      + +What happens when a WoRMS taxonomy is changed? e.g. a species is reclassified + +

      When species are reclassied in WoRMS, the original scientificName and scientificNameID provided in a dataset remains unchanged. However WoRMS will list the old ID as “Unaccepted”, and link to the accepted taxon entry, and this will be reflected in the taxonomic information attached to a dataset download.

      +

      For example, if we search for Manta birostris in OBIS (https://obis.org/taxon/105857), we see that the taxon’s status in WoRMS is unaccepted. At the bottom of the page it links to the currently accepted name: https://obis.org/taxon/1026118. We can find an occurrence which shows the source scientificName as “Manta” while the interpreted scientificName is “Mobula”: https://obis.org/occurrence/0020c873-02f1-4bd7-b396-ad36600bc8b2. We can also see that originalScientificName is populated with the source name in the intepreted output.

      +

      As a user, you don’t have to trace species names. However if the datasets’s DwC-A is downloaded from the dataset page instead of obtained through R or the Mapper, all fields will contain the original value. It remains good practice to also check identifiers against WoRMS to see if any have been updated when you download data.

      +
      +
      • +
    +

    1.5.0.5 Tools

      @@ -520,14 +552,6 @@

      1.5.0.5 ToolsWhat do I do if my scientificName does not return a match from WoRMS?
    • -
      - -Can scientificNameID be populated with an identifier (e.g. WoRMS LSID) representing an unaccepted taxon name? - -

      Yes. The identifier in scientificNameID should always correspond with the name that is in the scientificName field, even if the name is an unaccepted name in WoRMS. For example, the species name “Holothuria mammiculata” was provided, but this name is unaccepted in WoRMS. The accepted name is “Holothuria (Stauropora) pervicax Selenka, 1867”. In this case scientificNameID should correspond to the original name with LSID urn:lsid:marinespecies.org:taxname:529968 because the ID must correlate with the name as recorded in scientificName.

      -
      -
      • -
    • Where can I find DNA sequences published in OBIS?
    • @@ -535,7 +559,7 @@

      1.5.0.5 ToolsExcel template generator developed by Luke Marsden & Olaf Schneider as part of the Nansen Legacy project. Note this template generator is aimed at GBIF users, so make to account for and include required OBIS terms.

      +

      Yes. There is an Excel template generator developed by Luke Marsden & Olaf Schneider as part of the Nansen Legacy project. Note this template generator is aimed at GBIF users, so make sure to account for and include required OBIS terms.

      There is also this Excel to Darwin Core macro tool developed by GBIF Norway you can use to help generate templates.

      • @@ -660,6 +684,57 @@

      1.5.0.8 Accessing data in OBISCurrently, it is not possible to filter OBIS data by trait. To do this, we recommend using the traits database of the World Register of Marine Species. For example, searching by “functional group”, you can specify benthos, plankton, nekton, etc.

      +
    • +
      + +How do I get data from multiple regions from OBIS? + +

      If the areas OBIS currently uses does not work for your use case, then it is best to first define all the boundaries for the desired regions. OBIS can be queried using WKT polygons by providing a WKT string to the geometry parameter in the robis::occurrence function. HOWEVER there are some limitations with respect to polygon complexity, and if it is too complex you will likely receive the error “The OBIS API was not able to process your request”.

      +

      For more complex spatial queries we recommend indexing OBIS and GBIF data against polygons and using (finely) gridded versions of these datasets to make the process faster. We note we have not yet properly documented this process, but see the example script produced by Pieter Provoost below. The script first indexes a polygon to the H3 spatial index, then queries a gridded version of OBIS+GBIF data on AWS to get the species list, and finally fetches taxonomy from WoRMS for every species, which may take some time.

      +
      library(readr)
+library(h3jsr)
+library(sf)
+library(duckdb)
+library(DBI)
+library(dplyr)
+
+sf_use_s2(FALSE)
+
+# Read WKT from text file, convert to sf, and index to H3 resolution 7
+# https://wktmap.com/?e6b28728
+
+wkt <- read_file("wkt_21773.txt")
+geom <- st_as_sfc(wkt, crs = 4326)
+cells <- data.frame(cell = polygon_to_cells(geom, 7)[[1]])
+
+# Set up duckdb connection and register cells table
+
+con <- dbConnect(duckdb())
+dbSendQuery(con, "install httpfs; load httpfs;")
+duckdb_register(con, "cells", cells)
+
+# Join cells list and gridded species dataset
+
+species <- dbGetQuery(con, "
+  select species, AphiaID
+  from cells
+  inner join read_parquet('s3://obis-products/speciesgrids/h3_7/*') h3 on cells.cell = h3.h3_07
+  group by species, AphiaID
+")
+
+# Add WoRMS taxonomy
+
+id_batches <- split(species$AphiaID, ceiling(seq_along(species$AphiaID) / 50))
+taxa_batches <- purrr::map(id_batches, worrms::wm_record)
+taxa <- bind_rows(taxa_batches) %>% 
+  select(AphiaID, scientificname, phylum, class, order, family, genus, scientificName = scientificname)
+
+# Get Mollusca species
+
+mollusca <- taxa %>%
+  filter(phylum == "Mollusca")
      +
      +

    diff --git a/access.html b/access.html index 34d11cc..cfff5e9 100644 --- a/access.html +++ b/access.html @@ -329,7 +329,9 @@

    11 Data accessFull data exports
  • IPT
    • -

    NOTE When you download data from the Mapper or full export, the data you will receive is flattened into one table with occurrence plus event data. eMoF data tables are separate upon request. However when you download a dataset from the OBIS homepage or dataset page, all tables (Event, Occurrence, eMoF) are separate files.

    +
    +

    When you download data from the Mapper or full export, the data you will receive is flattened into one table with occurrence plus event data. eMoF data tables are separate upon request. However when you download a dataset from the OBIS homepage or dataset page, all tables (Event, Occurrence, eMoF) are separate files.

    +

    11.1 OBIS Homepage and dataset pages

    From the OBIS homepage, you can search for data in the search bar in the middle of the page. You can search by particular taxonomic groups, common names, dataset names, OBIS nodes, institute name, areas (e.g., Exclusive Economic Zone (EEZ)), or by the data provider’s country.

    @@ -355,7 +357,7 @@

    11.2 Mapperhttps://mapper.obis.org -

    Watch this video demonstration of how to use the Mapper as well as the OBIS homepage search.

    +

    Watch this video demonstration of how to use the Mapper as well as the OBIS homepage search.

    If you would like to export Event data to the eMoF, see some example R code below. This example was provided by Abby Benson from the OBIS-USA node.

    -
    library(dplyr)
-cruise <- unique(eventCore[c("eventID")]) #create a list of all unique eventIDs from your event table
-cruise <- cruise %>% #add sampling information 
-mutate(measurementType = "Sampling platform name",
-measurementValue = "R/V Cruise Id = SR1812",
-measurementValueID = "https://doi.org/10.7284/908021",
-measurementUnit = "",
-measurementTypeID = "http://vocab.nerc.ac.uk/collection/Q01/current/Q0100001/",
-measurementUnitID = "",
-occurrenceID = "")
    +
    library(dplyr)
+cruise <- unique(eventCore[c("eventID")]) #create a list of all unique eventIDs from your event table
+cruise <- cruise %>% #add sampling information 
+mutate(measurementType = "Sampling platform name",
+measurementValue = "R/V Cruise Id = SR1812",
+measurementValueID = "https://doi.org/10.7284/908021",
+measurementUnit = "",
+measurementTypeID = "http://vocab.nerc.ac.uk/collection/Q01/current/Q0100001/",
+measurementUnitID = "",
+occurrenceID = "")

    diff --git a/format_event.html b/format_event.html index 38fdbd7..4156e7c 100644 --- a/format_event.html +++ b/format_event.html @@ -371,7 +371,7 @@

    5.4.0.1 Stepwise Guidance to Form
  • Add any other relevant fields as indicated above
  • Map fields to Darwin Core
    • -

    Watch the video tutorial of this process below.

    +

    Watch the video tutorial of this process below.

    -

    The taxon match takes into account exact matches and fuzzy matches. Fuzzy matches include possible spelling variations of a name available in WoRMS. WoRMS also identifies ambiguous matches, indicating that several potential matching options are available (e.g. homonyms). You can check these ambiguous matches and select the correct one, based on e.g., the general group information (a sponge dataset) or the authority. If this would be impossible with the available information (e.g., missing authority or very diverse dataset), then you need to contact the data provider for clarification. Watch the video below for a demonstration on how to resolve ambiguous or fuzzy matches.

    +

    The taxon match takes into account exact matches and fuzzy matches. Fuzzy matches include possible spelling variations of a name available in WoRMS. WoRMS also identifies ambiguous matches, indicating that several potential matching options are available (e.g. homonyms). You can check these ambiguous matches and select the correct one, based on e.g., the general group information (a sponge dataset) or the authority. If this would be impossible with the available information (e.g., missing authority or very diverse dataset), then you need to contact the data provider for clarification. Watch the video below for a demonstration on how to resolve ambiguous or fuzzy matches.

    For performance reasons, the limit is set to 1,500 rows for the taxon match tool. Larger files can be sent to info@marinespecies.org and will be returned as quickly as possible. In case you have recorded a taxon that is not registered in WoRMS (e.g., newly discovered species), you should contact them so the database can be updated.

    After matching, the tool will return you a file with the AphiaIDs, LSIDs, valid names, authorities, classification, and any other output you have selected.

    -
    -

    Note -The WoRMS LSID is used for DwC:scientificNameID.

    -
    +
    +

    The WoRMS LSID is used for DwC:scientificNameID.

    +

    A complete online manual is available at http://www.marinespecies.org/tutorial/taxonmatch.php. You can attach IDs obtained from WoRMS back to your own data using Excel’s vlookup function. R script to do this is shown below.

    R script for attaching Taxon Lists to ID Lists:

    If you are familiar enough with R, you can use the merge function to attach the two lists to your data. We provide a short example of how to use this function below.

    -
    #Generate example data table with species occurences, for this example we will only have one column with the scientificName
-data<-data.frame(scientificName=c("Thunnus thynnus", "Rhincodon typus", "Luidia maculata","Ginglymostoma cirratum"))
-
-# this would be your matched file from WoRMS, but for example we are generating a simple list with the species names and LSIDs
-lsids<- data.frame(scientificName=c("Ginglymostoma cirratum","Luidia maculata","Thunnus thynnus", "Rhincodon typus"),
- LSID = c("urn:lsid:marinespecies.org:taxname:105846", "urn:lsid:marinespecies.org:taxname:213112","urn:lsid:marinespecies.org:taxname:127029","urn:lsid:marinespecies.org:taxname:105847"))
-
-#merge data frames together
-matched_data<-merge(data, lsids, by = "scientificName")
-matched_data
    +
    #Generate example data table with species occurences, for this example we will only have one column with the scientificName
+data<-data.frame(scientificName=c("Thunnus thynnus", "Rhincodon typus", "Luidia maculata","Ginglymostoma cirratum"))
+
+# this would be your matched file from WoRMS, but for example we are generating a simple list with the species names and LSIDs
+lsids<- data.frame(scientificName=c("Ginglymostoma cirratum","Luidia maculata","Thunnus thynnus", "Rhincodon typus"),
+ LSID = c("urn:lsid:marinespecies.org:taxname:105846", "urn:lsid:marinespecies.org:taxname:213112","urn:lsid:marinespecies.org:taxname:127029","urn:lsid:marinespecies.org:taxname:105847"))
+
+#merge data frames together
+matched_data<-merge(data, lsids, by = "scientificName")
+matched_data

    5.2.0.2 How to fetch a full classification for a list of species from WoRMS?

    diff --git a/search_index.json b/search_index.json index fb6cb9b..86963b8 100644 --- a/search_index.json +++ b/search_index.json @@ -1 +1 @@ -[["index.html", "The OBIS manual 1 Introduction 1.1 Guidelines on the sharing and use of data in OBIS 1.2 Acknowledgements", " The OBIS manual 05 September, 2024 1 Introduction This manual provides a comprehensive walk-through on how to contribute data to OBIS and how to access data from OBIS. It is divided into topical sections that provide detailed information on: Frequently Asked Questions How to contribute data to OBIS, including information on data standards and terms of reference for OBIS groups Formatting datasets Running quality control checks Publishing data Accessing data from OBIS Data visualization tips Links to additional resources The manual is designed to provide guidelines for OBIS nodes and data providers, ensuring that data published via OBIS are of high quality and adhere to internationally recognized standards. It also serves as a resource for data users on how to access, process, and visualize data from OBIS. See the quick links page for a quick reference to various guides, tools, and resources. See the OBIS YouTube playlist for many helpful How-to videos! The OBIS manual is a dynamic document and is revised on a regular basis. We welcome suggestions for additions and changes, which can be sent to helpdesk@obis.org, the OBIS Capacity Development Task Team at training@obis.org, and/or submitted as issues at https://github.com/iobis/manual/issues. 1.1 Guidelines on the sharing and use of data in OBIS It is important that our data providers, as well as all data users, are aware and agree on the OBIS guidelines on the sharing and use of data in OBIS, which was adoped at the 12th OBIS Steering Group. NB: Data users should be aware of the licenses associated with all datasets downloaded from OBIS (e.g. CC0, CC-BY, CC-BY-NC). 1.2 Acknowledgements This manual received contributions from (in alphabetical order): Ward Appeltans, Abby Benson, Samuel Bosch,Yi-Ming Gan, Philip Goldstein,Mary Kennedy, Elizabeth Lawrence, Carolina Peralta Brichtova, Pieter Provoost, Saara Suominen, Serita van der Wal, and Leen Vandepitte. "],["policy.html", "1.3 Data Policy", " 1.3 Data Policy Guidelines on the sharing and use of data in OBIS The IOC Assembly adopted a new IOC Data Policy and Terms of Use (IOC Decision A-32/4.4, 2023) and requested that IOC programmes (like OBIS), projects, as well as other communities of practice should develop and/or apply, where applicable, detailed metadata, data and products sharing guidelines that are consistent with this new policy. The OBIS guidelines on the sharing and use of data in OBIS was agreed at SG-OBIS-IV (Feb 2015) and adopted at IODE-XXIII (March 2015) and was based on the principles of the previous IOC data exchange policy. The SG-OBIS-11 (May 2023) set up an IWG Data Policy to fulfil this task and then the SG-OBIS-12 agreed upon an updated policy. The OBIS data policy is outlined below. Text in italics represents text from the IOC Data Policy and Terms of Use. 1.3.0.1 Section 1. Preamble The OBIS data policy follows the guidelines from the Intergovernmental Oceanographic Commission (IOC) data policy. In this regard, we recognize that: the timely, open and unrestricted international sharing, in both real-time and delayed mode of ocean metadata, data and products is essential for a wide variety of purposes and benefits including scientific research, innovation and decision making, the prediction of weather and climate, the operational forecasting of the marine environment, the preservation of life, economic welfare, safety and security of society, the mitigation of human-induced changes in the marine and coastal environment, as well as for the advancement of scientific understanding that makes this possible. Innovation of specialised products can be stimulated and encouraged by timely, open and unrestricted access to metadata and data. Metadata, data, and products should be accessible, interoperable and openly shared with minimum delay and minimum restrictions. This document describes how the IOC data policy is implemented by OBIS. Although this document broadly covers the requirements for metadata, data, and data products, more detailed guidance for the OBIS community is also provided in the OBIS manual (https://manual.obis.org). 1.3.0.2 Section 2. Purpose The purpose of this data policy is to outline the requirements with respect to sharing, access, preservation, and attribution to facilitate the broad use and reuse of metadata, data and data products. 1.3.0.3 Section 3. FAIR & CARE principles To support knowledge discovery and innovation both by humans and machines and to acknowledge indigenous data governance, data should meet the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) [1] and In the case of indigenous data and information, data should meet the CARE principles (Collective Benefit, Authority to Control, Responsibility, Ethics) [2] to the greatest extent practicable. The FAIR Principles related to Findability state that data and metadata are assigned globally unique and persistent identifiers. For OBIS this means that all event and occurrence records published to OBIS should have a globally unique identifier in the appropriate field. Care must be taken to preserve these identifiers across dataset updates. Datasets should be assigned a DOI (Digital Object Identifier) upon publishing. Findability also means that datasets are described using rich metadata. In practice, this means that OBIS dataset metadata records must contain at least the following: a title, a detailed description including information on the methodology used to collect the data, contact information, a license, a citation, geographic and taxonomic scope, and may contain other relevant metadata. Regarding the Accessibility aspect of the FAIR data principles, OBIS datasets’ metadata should always be accessible via the data provider IPT instance even when the data is no longer available. In addition, OBIS datasets should be retrievable by using a DOI that leads to the data. Interoperability principles state that knowledge must be described using a shared language which has been formally specified, so that the knowledge can be interpreted not just by humans but also by machines. OBIS data are described using the Darwin Core standard, which includes a set of terms with identifiers and definitions, as well as a number of vocabularies which should be used wherever possible. In order to optimize interoperability of measurements and sampling facts data, it is recommended that OBIS datasets use terms and identifiers from the BODC NERC vocabulary server, or other vocabulary collections with machine interpretable identifiers. OBIS data must also be Reusable, that is, enough information is available so that others can use that data in different settings. In this regard, all data and metadata associated with records needs to be complete and accurate, containing at least the minimum information described in the OBIS manual. Ideally, providers should go well beyond the minimum and provide as much information as possible. Both the data and metadata should comply with the standards adopted by OBIS, for example Ecological Metadata Language (EML) for metadata and Darwin Core for biodiversity data. It is also necessary that all data provided is clearly linked to a usage license, respecting the licenses currently accepted by OBIS (i.e. CC0, CC BY, CC BY-NC), ensuring that users are able to fully understand how they can reuse the data and also to give the correct acknowledgement to data providers where required. Beyond complying with FAIR principles, OBIS data must also be in accordance with the CARE principles. That is, indigenous data and information should always be handled with sensitivity and respect. Authority of indigenous data holders must be respected, and indigenous peoples’ rights and wellbeing should be considered at all stages of the data life cycle. 1.3.0.4 Section 4. Conditions of use Data should be licensed (respecting Section 8) under a minimally restrictive and voluntary common-use licence[3] that grants permission, ensures proper attribution (for example, citable using a persistent identifier) and allows others to copy, distribute and make use of the data. Section 5. Data Repositories and the IOC ocean data and information system (ODIS) Data should be quality controlled (using community adopted and documented best practices or standards), accompanied by complete metadata and stored in an openly discoverable and accessible long-term data repository and made available through standards-based data services. Member States shall encourage convergence and interoperability and, where possible, use IODE data centres (National Oceanographic Data Centres or Associate Data Units) or other IOC programme related data centres that share metadata and data using the IOC Ocean Data and Information System (ODIS). ODIS is an interoperability layer and supporting technology, to allow existing and emerging ocean data and information systems to interoperate with one another. Data provided to OBIS must be licensed with one of the accepted licenses described in the OBIS manual. Data users should be encouraged to appropriately attribute data publishers, for example by providing a license, clear citation instructions, and a DOI. OBIS, through the work of its nodes, is also concerned in providing high-quality marine biodiversity data for users. Thus, all data submitted to OBIS needs to undergo rigorous quality assurance and validation processes before submission. Minimally, data providers must check data for locality issues, taxonomic mismatches, and proper formatting. They must also ensure that all necessary information fields are available. Guidance on how to perform data quality control is provided by the OBIS community, through its OBIS manual. An inseparable part of high quality data is that it must have complete and comprehensive metadata, as described in the Section 3. All data and products published through OBIS are freely accessible to any user, following the principles of equitable access and benefit sharing. In this regard, when licenses allow, users can build products based on OBIS data without any cost. Whenever possible, users should consider sponsoring or partnering with OBIS and its OBIS nodes in grant proposal writing. Creating a global database like OBIS cannot happen without the, often voluntary, contribution of many scientists and data managers all over the world. Several activities, such as coordination, data integration, quality control, database and website maintenance require resources including manpower at national and international level. Any use of OBIS data and/or derived products, including (but not limited to) software applications, workflows and papers, should be properly cited. The following formats are recommended: General OBIS citation: OBIS (YEAR) Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. obis.org. Use the following format to cite data retrieved from OBIS (dataset citations are available in the zip downloads as html file): [Dataset citation available from metadata] [Data provider details] [Dataset] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. obis.org. Accessed: YYYY-MM-DD. For example: Sousa Pinto, I., Viera, R. (Year: if not provided use year from dataset publication date) Monitoring of the intertidal biodiversity of rocky beaches with schools in Portugal 2005-2010. CIIMAR - Interdisciplinary Centre of Marine and Environmental Research, Porto. [Dataset] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. obis.org. Accessed: 2015-01-01) When data represents a subset of many datasets taken from the integrated OBIS database, you can, in addition to cite the individual datasets (and taking into account the restrictions set at each dataset level), also cite the OBIS database as follows: OBIS (YEAR) [Data e.g. Distribution records of Eledone cirrhosa (Lamarck, 1798)] [Dataset] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. Accessed: YYYY-MM-DD) The derived information products from OBIS are published under the CC-0 license and can be cited as follows: OBIS (YEAR) [Information product e.g. Global map showing the Hulbert index in a gridded view of hexagonal cells] [Map] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. Accessed: YYYY-MM-DD) While those citation formats cover a broad range of applications, users should always consult the OBIS manual for more detailed guidelines on how to properly attribute the data and information products provided by OBIS and its broader community. 1.3.0.4.1 Section 5. Data Repositories and the IOC ocean data and information system (ODIS) Data should be quality controlled (using community adopted and documented best practices or standards), accompanied by complete metadata and stored in an openly discoverable and accessible long-term data repository and made available through standards-based data services. Member States shall encourage convergence and interoperability and, where possible, use IODE data centres (National Oceanographic Data Centres or Associate Data Units) or other IOC programme related data centres that share metadata and data using the IOC Ocean Data and Information System (ODIS). ODIS is an interoperability layer and supporting technology, to allow existing and emerging ocean data and information systems to interoperate with one another. As explained in section 3, data providers are responsible for performing quality control on all data prior to its submission to OBIS. Details on quality control procedures are provided in the OBIS manual, which is regularly updated by the OBIS secretariat and OBIS nodes. OBIS is connected to ODIS and is an integral part of the IODE data structure. 1.3.0.5 Section 6: Secure long-term data archives To support long-term and secure archival, data and associated metadata should be submitted, to the best practicable degree, to IODE’s World Ocean Database (WOD), the Ocean Biodiversity Information System (OBIS), Global Sea Level Observing System (GLOSS), other IOC related global data archives, and data centres linked to the World Data System (WDS), their successors or other global data archives. OBIS and its nodes should ensure long-term data archival and adhere to best practices for data preservation. OBIS secretariat will archive the curated database, but nodes are responsible for archiving individual datasets. Data and data products provided by OBIS and its nodes must also be available through standards-based data services that allow users to access, query, and retrieve data in various formats. OBIS currently provides several ways through which users can access its data and follows the best practices for data sharing, and in this sense, any newly developed product must ensure maximum accessibility and interoperability. 1.3.0.6 Section 7. Access restrictions Data and associated metadata should be made available with minimal restrictions on use unless there are valid reasons to restrict access. Legitimate reasons to restrict access to, and reuse of, data include, inter alia, privacy and confidentiality, protection of species, populations, or habitats of concern, and national security. OBIS datasets should be published using any of the three accepted CC licenses, with a preference for the most permissive CC0. In case access to information must be restricted for any of the above reasons, data providers should censor or generalize the data before publishing to OBIS, and ensure that these changes are reflected in the dataset metadata. When data is made available to OBIS, OBIS is granted permission to: Distribute the data via its data and information portal Build an integrated database, use the data for data quality control purposes, complement the data with other data such as climate variables and build value-added information products and services for science and decision-making Serve the data to other similar open-access networks in compliance with the terms and conditions for use set by the data providers. In pursuance of copyright compliance, OBIS endeavors to secure permission from rights holders to ingest their datasets. In the event that the inclusion of a dataset in OBIS is challenged on the basis of copyright infringement, OBIS will follow a take-down policy until there is resolution. 1.3.0.7 Section 8. Data sharing policies of Member States This Policy acknowledges the right of Member States and data owners to determine the terms of metadata, data and products sharing in a manner consistent with national jurisdictions, international conventions, and treaties, where applicable. Whenever possible, data should be made available in its fullness. However, in any case access to data must be restricted to respect Member States jurisdictions, conventions or treaties, it is the responsibility of data providers to censor or generalize the data before publishing to OBIS, as explained in Section 7. 1.3.0.8 Section 9. Data and metadata sharing guidelines IOC programmes, projects as well as other communities of practice should develop and/or apply, where applicable, detailed metadata, data and products sharing guidelines that are consistent with this IOC Data Policy and Terms of Use. OBIS and its nodes provide detailed guidelines on how to publish and use OBIS data. This information is made available in the OBIS manual (https://manual.obis.org/) and is regularly revised and improved to adhere to best practices. 1.3.0.9 Section 10. Definitions ‘Data’ is a set of values, symbols or signs (recorded on any type of medium) that represent one or more properties of an entity[4]. ‘Metadata’ is ‘data about data’ describing the content, quality, condition, and other characteristics of data that allows their inventory, discovery, evaluation or use. ‘Timely’ in this context means the distribution of data and/or products, sufficiently rapidly to be of value for a given application. ‘Openly’ means data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and share alike. ‘Product’ means a value-added enhancement of data applied to a particular use. OBIS uses the same definitions as IOC. 1.3.0.10 Disclaimer Appropriate caution is necessary in the interpretation of results derived from OBIS. Users must recognize that the analysis and interpretation of data require background knowledge and expertise about marine biodiversity (including ecosystems and taxonomy). Users should be aware of possible errors, including in the use of species names, geo-referencing, data handling, and mapping. They should cross check their results for possible errors and qualify their interpretation of any results accordingly. Unless data are collected through activities funded by IOC/IODE, neither UNESCO, IOC, IODE, the OBIS Secretariat, nor its employees or contractors, own the data in OBIS and they take no responsibility for the quality of data or products based on OBIS, or its use or misuse. "],["gethelp.html", "1.4 Getting Help in OBIS", " 1.4 Getting Help in OBIS If you require additional assistance with OBIS we recommend you first get in touch with the most relevant OBIS node. We also have a support channel on Slack where you can communicate with the OBIS community for help. Please feel comfortable posting to this channel before reaching out to the OBIS Secretariat (helpdesk@obis.org). The OBIS community is quite active on Slack and GithHub (see below) so you are more likely to receive a quick answer to your question by posting in either place, as the Secretariat receives many requests. You can submit issues and questions on relevant Github repositories: OBIS Manual OBIS issues GitHub repo OBIS quality control issues All other OBIS repositories We strongly recommend creating a GitHub account to engage with the OBIS community, document issues, ask questions, find datasets that need endorsing, etc. GitHub gives threads a more permanent home and allows for open communication and transparency. If you are unfamiliar with GitHub, the Carpentries have these training resources which you can reference. "],["FAQ.html", "1.5 Frequently Asked Questions", " 1.5 Frequently Asked Questions Content General Darwin Core Formatting data Vocabulary Tools Quality Control Publishing Data Access 1.5.0.1 General I have data and want to publish to OBIS - what do I do? Why is it important to share and format data? How do I handle sensitive data? Where can I make suggestions for improvements on this Manual? Where can I find OBIS related training videos? What are the responsibilities of OBIS node managers? Where can I find marine datasets linked to the OBIS network by the GBIF registry, that now require endorising? I am an OBIS node and need to update the contact information listed for my node on the OBIS website, how do I do that? To update OBIS Node contact information listed at https://obis.org/contact/, you can edit the information here https://metadata.obis.org/ by signing in with your OceanExpert credentials. 1.5.0.2 Darwin Core Where can I learn about “Darwin Core”? I am having trouble understanding how Core and Extension tables relate to each other How does the OBIS format avoid redundancy in data How are extension tables (e.g. eMOF, occurrence) linked with the core table? What is the difference between Occurence Core and Event Core? What is the difference between eventID, occurrenceID, organismID, taxonID, scientificNameID, recordNumber, materialEntityID, materialSampleID, and catalogNumber? What is the difference between individualCount, and organismQuantity, and which one should I use? The DwC term individualCount is used simply for documenting the number of individuals present at the time the occurrence happened. Conversely, organismQuantity is used to record any type of quantity related to an organism or occurrence. organismQuantityType may include the number of individuals, the % cover, biomass, the cover-abundance on the Braun-Blanquet Scale, etc. While both DwC terms can be placed in the Occurrence table, OBIS recommends also populating them in the eMoF table because of the standardization of terms and because you can link to sampling events and factual sampling information. 1.5.0.3 Formatting Data Is there a checklist of all required Darwin Core fields for OBIS? How does data flow in OBIS? What should I do if I do not have the data for required fields by OBIS? How do I construct an eventID? How do I construct occurrenceID? What data goes into Occurrence core (or extension) and how do I set up this file? How do I set up an Event core table? How should I populate parent and child event information? Do I have to populate information for child events if the parent event already contains that information? Do I have to provide decimalLatitude and decimalLongitude for the Event and Occurrence tables? The answer may depend on your dataset structure, but generally, no. If you have Event core, then you do not need to repeat location information in the Occurrence table (but you can if you’d like). If you are using Occurrence core, then location information must be provided in the Occurrence table. What data goes into extendedMeasurementOrFact (eMoF) and how do I set it up? Can I have multiple eMoF tables, one for Event and one for Occurrence measurements? Yes, you can create multiple eMoF tables if it is simpler for you to separate Event and Occurrence measurement data. However, you must ensure all tables include the correct identifiers, i.e. eventID for measurements related to events, and occurrenceID and eventID for measurements associated with occurrences. When you add multiple tables of the same type in the IPT, they will be treated as if they were a single table, so there is no issue if you would like to do this. How do I format dates? How do I handle historical data? How do I convert coordinates to decimal degrees? How do I convert different geographical formats to WGS84? How do I compile acoustic, imaging, or other multimedia data for OBIS? How do I compile habitat data for OBIS? How do I compile tracking data for OBIS? How do I compile DNA and genetic data for OBIS? What does sampleSize and organismQuantity refer to for genetic data? For genetic data, sampleSizeValue and organismQuantity do not refer to the amount sampled nor the number of organisms. Instead these fields are related to either 1) the number of sequence reads for eDNA data or 2) the number of droplets/partitions for qPCR data. See DNA data guidelines for more details. How do I document occurrences from unknown species, those new to science, or those with temporary names? e.g. Eurythenes sp. DISCOLL.PAP.JC165.674 Occurrences unknown or new to science should be documented according to recommendations by Horton et al. 2021. You should populate the scientificName field with the genus, and in identificationQualifer provide the ON sign ‘sp.’. However you must also indicate the reason why species-level identification is unavailable. To do this, supplement ‘sp.’ with either stet. (stetit) or indet. (indeterminabilis). If neither of these are applicable, (e.g. for undescribed new species), add a unique taxon identifier code after ‘sp.’ to identificationQualifer. For example Eurythenes sp. DISCOLL.PAP.JC165.674. Please avoid simple alphanumeric codes (i.e. Eurythenes sp. 1, Eurythenes sp. A). Similar to creating eventIDs or occurrenceIDs, you should strive to provide more complex and globally unique identifier. Identifiers could be constructed by combining higher taxonomic information with information related to a collection, institution, museum or collection code, sample number or museum accession number, expedition, dive number, or timestamp. This ensures namestrings will remain unique within a larger repositories like OBIS. It is also recommended to include these temporary names on specimen labels for physical specimens. 1.5.0.4 Vocabulary How do I map Measurement or Fact terms in OBIS with preferred BODC vocabulary? I can’t find a suitable vocabulary, what do I do? How do I request a new vocabulary term? Should I use taxon-specific P01 codes to populate for measurementTypeID? e.g. http://vocab.nerc.ac.uk/collection/P01/current/A15985A1 No. You should never use taxon-specific P01 codes. This is because the taxa are already identified in the Occurrence table, in the fields scientificName and scientificNameID. How should I match raw data fields with Darwin Core terminology? 1.5.0.5 Tools How do I use the WoRMS taxon match tool? Can I fetch a full classification for a list of species from WoRMS? What do I do if my scientificName does not return a match from WoRMS? Can scientificNameID be populated with an identifier (e.g. WoRMS LSID) representing an unaccepted taxon name? Yes. The identifier in scientificNameID should always correspond with the name that is in the scientificName field, even if the name is an unaccepted name in WoRMS. For example, the species name “Holothuria mammiculata” was provided, but this name is unaccepted in WoRMS. The accepted name is “Holothuria (Stauropora) pervicax Selenka, 1867”. In this case scientificNameID should correspond to the original name with LSID urn:lsid:marinespecies.org:taxname:529968 because the ID must correlate with the name as recorded in scientificName. Where can I find DNA sequences published in OBIS? Is there a template generator I can use to help create my Event, Occurrence, and eMoF tables? Yes. There is an Excel template generator developed by Luke Marsden & Olaf Schneider as part of the Nansen Legacy project. Note this template generator is aimed at GBIF users, so make to account for and include required OBIS terms. There is also this Excel to Darwin Core macro tool developed by GBIF Norway you can use to help generate templates. How do I georeference locations, including text-based descriptions? 1.5.0.6 Quality Control How do I do data quality control? What are the OBIS quality control flags? Why are certain records dropped in OBIS? What do I do when I am uncertain about the: Temporal range of a dataset OR eventDate Geospatial location Taxonomic identification Individual count What do I do with freshwater species that are part of my marine dataset? 1.5.0.7 Publishing How do I add my data to the OBIS database? What metadata do I have to provide? Where? How? How do you know which license to choose? How do I access the IPT? How do I use the IPT? Are there instructions for IPT administrators? How do I add DOI to my dataset? How do I publish to both GBIF and OBIS? How do I update my already published dataset? 1.5.0.8 Accessing data in OBIS Can I get (or publish) other types of information from OBIS, besides occurences (e.g. bio-eco variables, DNA data, abiotic measurements, etc.)? Yes! OBIS allows access to not only taxa occurrences, but also all kinds of measurement data including DNA data (see Contribute for a list of data accepted by OBIS). This data is recorded in the (Extended) Measurement Or Fact and DNA-Derived Data extension tables, respectively. To access this data when downloading, be sure to select MoF or DNA from the dropdown Dataset Type menu on the advanced dataset serach page. If you are obtaining data from the OBIS Mapper, make sure to check the box for MeasurementOrFact and/or DNADerivedData from the Extensions toggle section. When using the R package robis, use the mof=TRUE and/or dna=TRUE arguments. More data filter options will be added in the future. How do I download data from OBIS? How do I load the full (.csv) export of OBIS data? Loading the entire OBIS dataset uses a lot of memory and is probably not feasible on most desktop computers. You have a few potential options depending on the use case: i) process the data in smaller batches, or ii) load the dataset into a local database such as SQLite and use SQL queries to analyze the data Otherwise, we recommend you use the parquet download which is available here, instead of the CSV. Then in R, you can use the arrow package to work with parquet files. We also have a short tutorial on working with parquet files in R here, with an example application of this approach here (see first code block). How can I use R to access OBIS data? How do I use the OBIS API to fetch and filter data? How do I contact the data provider? How can I cite OBIS datasets and downloads? What are the definitions of the field names in the downloads generated by OBIS? How do I obtain a taxon checklist for an area? There are a few possible ways to obtain a taxon checklist for a given area. We will obtain a checklist of species in the Albain EEZ as an example. To do this we will create a bounding box around our area of interest, and then apply filters to simplify the geometry. library(mregions) library(dplyr) library(robis) library(sf) #obtain Albanian EEZ as sf geom <- mr_shp(key = "MarineRegions:eez", filter = "Albanian Exclusive Economic Zone", maxFeatures = NULL) #get WKT for the bounding box wkt <- st_as_text(st_as_sfc(st_bbox(geom)), digits = 6) #fetch occurrences for bounding box occ <- occurrence(geometry = wkt) %>% st_as_sf(coords = c("decimalLongitude", "decimalLatitude"), crs = 4326) #filter using geometry occ_filtered <- occ %>% filter(st_intersects(geometry, geom, sparse = FALSE)) %>% as_tibble() %>% select(-geometry) #get taxa alb_taxa <- occ_filtered %>% group_by(phylum, class, order, family, genus, species, scientificName) %>% summarize(records = n()) The dates look unusual in the download file. What are these, how do I convert them, and/or how do I obtain separate elements from them (e.g. month)? The values in date_start, date_mid, and date_end are unix timestamps which have been calculated from the ISO date in the eventDate column. We can convert these numerical values to dates using the formula below. =(E2/86400000)+DATE(1970,1,1) If, when you apply this formula, you still see numbers, you will need to set the cell formatting to Date. Once you have dates, you can obtain, e.g. months for seasonal analyses using: =MONTH(H2) You can also use this tool to convert timestamps. How do I filter by or obtain trait information for OBIS data (e.g. all benthic organisms)? Currently, it is not possible to filter OBIS data by trait. To do this, we recommend using the traits database of the World Register of Marine Species. For example, searching by “functional group”, you can specify benthos, plankton, nekton, etc. "],["linklist.html", "1.6 Quick links to resources", " 1.6 Quick links to resources Below you will find a convenient list of important or relevant websites and resources that are mentioned in this Manual. Links are organized by category. 1.6.0.1 General links and guides OBIS home page Darwin Core term OBIS Checklist OBIS FAQ OBIS Slack OBIS GitHub helpdesk List of OBIS nodes Darwin Core term Quick Reference Guide Darwin Core text guide Publishing DNA-derived data guide OBIS Data formatting tutorial YouTube series: this series covers topics including creating identifiers (eventID, occurrenceID), creating data tables (event, Occurrence, eMoF), accessing OBIS data, taxon matching, running QC checks, etc. De Pooter et al. 2017. Toward a new data standard for combined marine biological and environmental datasets - expanding OBIS beyond species occurrences. Biodiversity Data Journal 5: e10989. hdl.handle.net/10.3897/BDJ.5.e10989 Duncan et al. (2021). A standard approach to structuring classified habitat data using the Darwin Core Extended Measurement or Fact Extension. EMODnet report. https://www.emodnet-seabedhabitats.eu/resources/documents-and-outreach/#h3298bcd0a15741a8a0ac1c8b4576f7c5 (note you must refine search to Technical Reports from 2021 to identify this report as it does not have an individual link) 1.6.0.2 Data Access & Mapping/Georeferencing Tools OBIS Mapper: map-based tool for obtaining OBIS data, allowing various filters to be applied R package robis OBIS MapTool: used for generating WKT strings, georeferencing, etc. Wellknown Text (WKT) visualization: tool to visualize WKT strings Full OBIS Data export: also includes a list of which fields are appended/modified when data is published to OBIS OBIS API: API interface, note that the result previews from executing functions are frequently incomplete and only provide the first ~10 results Marine Regions Gazetteer: a standard list of geographic names with information and maps; can be used to assist georeferencing, particularly for marine locations Coordinate Conversion Tool: used to convert coordinates from degrees minutes seconds to decimal degrees Getty Thesaurus of Geographic Names: search interface for place names 1.6.0.3 Controlled vocabulary OBIS YouTube Vocabulary series: this series demonstrates how to use the vocabulary decision tree in conjunction with the SeaDataNet P01 facet search in order to find/select vocabularies for eMoF measurements SeaDataNet P01 facet search: recommended interface to search for P01 codes used to populate measurementTypeID field. Note that the free text search is best used by supplying one search term at a time (it does not search the same way we use Google) OBIS GitHub repository for vocabulary: use this repository to request new terms for measurements related to OBIS data Darwin Core Basis of Record Vocabulary: definitions for terms used to populate basisOfRecord OBIS MoF viewer: this tool allows you to search for all measurements or facts published in OBIS, but it should NOT be used as reference to select vocabularies. See the vocabulary section of the Manual for additional help MeasurementOrFact statistics summary by Node: this page provides a summary for each dataset by OBIS node, summarizing the % of missing measurementTypeID, measurementValueID, and measurementUnitIDs NERC Vocabulary Server: interface to search through the entire NERC Vocabulary Server to find vocabulary terms. As with the SeaDataNet facet search, the search does not function the same way Google does and you may have to adjust how you search for terms. 1.6.0.4 Tools for Taxonomy & Dates WoRMS Taxon match tool: used to match a list of species names to taxonomic authority and obtain LSIDs for species GBIF Name parser: tool to divide scientific names into their components and to check them against the taxonomic backbone used by GBIF OBIS Guidelines for Historical Data Chronometric Age Extension Canadensys Date parser: tool to parse dates into component parts 1.6.0.5 Data QC Tools & Links List of Quality Checks conducted by OBIS on published data List of QC Flags implemented by OBIS obistools R package for conducting QC checks on OBIS data LifeWatch & EMODnet Biology QC tool Hmisc R package for summarizing data, useful for checking data quality and outliers 1.6.0.6 Template Tools GBIF Excel templates: Event table and Occurrence table Excel to Darwin Core Standard (DwC) Tool: a macro Excel spreadsheet for setup, data entry, data validation and file export to DwC (Darwin Core Standard) files. Creates templates for Event, Occurrence, MeasurementsOrFacts, Extended MeasurementsOrFacts (EMoF), and Simple Multimedia tables. Darwin Core Template Generator for Event and Occurrence tables: allows selection of DwC terms to generate template for Event and Occurrence tables, developed by Luke Marsden, & Olaf Schneider 1.6.0.7 Publishing Tools & Links Integrated Publishing Toolkit (IPT) User Manual: manual with more details on how to use the IPT for publishing datasets Ecological Metadata Language (EML) guide EML R package: assists with creating EML files in R, assumes some existing familiarity with EML ezEML: form-like interface for generating EML files. Offers dataset attachment but this is not required to generate the EML. Note that this is also an repository which offers data Jinja EML template generator: can be used to create tailored EML templates that you can reuse for your datasets. A template may be a good option for you if there are several EML elements which will not need to be changed each time. Code used is similar to Python Become a GBIF publisher: crucial step for publishing your dataset to GBIF as well as OBIS "],["contribute.html", "2 What can you contribute and how? 2.1 Why publish data to OBIS 2.2 How to handle sensitive data", " 2 What can you contribute and how? Since 2000, OBIS has accepted, curated and published marine biodiversity data obtained by varied sources and methods. There is a common misconception that OBIS only accepts species occurrence data - however this is not true! OBIS can accept many types of marine data including: Presence/Absence Abundance, individual count Biomass Abiotic measurements Biotic measurements Sampling methods Sample processing methods Genetic data including DNA sequences Data originating from historical records Tracking data Habitat data Acoustic data Imaging data Metadata describing the dataset and any project or programme related metadata So if you have any of these types of marine data linked to your occurrence data and also want to contribute to OBIS - great! OBIS accepts data from any organization, consortium, project or individual who wants to contribute data. OBIS Data Sources are the authors, editors, and/or organisations that have published one or more datasets through OBIS. They remain the owners or custodians of the data, not OBIS! OBIS harvests and publishes data from recognized IPTs from OBIS nodes or GBIF publishers. If you own data or have the right to publish data in OBIS, you can contact the OBIS secretariat or one of the OBIS nodes, or additionally a GBIF publisher. Your organization or programme can also become an OBIS node. An OBIS node usually publishes data from multiple data holders, effectively being a node in a network of data providers. So you may have to first find a relevant node before you get your data ready to publish. To publish a dataset to OBIS, there are five main steps you must go through. First, you must identify which OBIS node is best suited to host your published data. If you would like to publish to GBIF at the same time, that is also possible. If your organization is already affiliated with a GBIF node with which you must publish from, OBIS can also harvest from GBIF nodes. Second, you must determine the structure of your data and which format will best suit your dataset. OBIS follows Darwin Core Archive (DwC-A) standards for datasets, and currently follows a star schema format. This format is based on relational databases. If you are unfamiliar with such database structures, or would like to refamiliarize yourself with them, please read here Then, you need to actually format your data according to OBIS and DwC-A standards and guidelines Once formatted, you should run a series of quality control measures to ensure you are not missing any required information and that all standards are being met. This helps ensure all data published in OBIS is formatted in a standardized way. When published in OBIS, OBIS provides a quality report to inform data owners and users of any quality control issues. By completing quality control before you publish your dataset you ensure there are fewer errors to fix later. Now that your dataset is ready for publishing, the relevant metadata must be filled in, and then published on the previously identified IPT. Each of these steps are covered in detail in the relevant sections of the manual. For an overview of this process see data management flow in OBIS. 2.1 Why publish data to OBIS It is important to publish and ensure your dataset follows a universal standard for several reasons. The FAIR guiding principles for scientific data management and stewardship provide a good framework to understand the reasoning behind publishing data. FAIR stands for Findable, Accessible, Interoperable, and Reusable. Let’s understand each aspect within the FAIR framework and how it is linked to publishing data in OBIS. F - Findable Even if you publish your dataset on its own, publishing your data with OBIS will make your data more Findable (and Accessible) to a wider audience you might not have otherwise reached. By publishing your dataset to OBIS you are adding to a global database where your data can be found and analyzed alongside thousands of other datasets. For example, a dataset on marine invasive species in Venezuela was published July 20, 2022 and as of October 5, 2022 records of this dataset were included in 1,873 data download requests. This can save you time rather than handling individual data requests. A - Accessible Similar to being Findable, OBIS makes your datasets more Accessible. Each dataset is given an identifier when you upload it on an IPT. Thus when users obtain data from OBIS, the original dataset can easily be identified and accessed. Data from OBIS is accessible in numerous ways, giving data users multiple avenues to potentially access your data. I - Interoperable Using a standardized data format with controlled vocabularies will ensure your data are more Interoperable - more easily interpreted and processed by computers and humans alike. Increasingly, scientists use computer programs to conduct e-Science and collect data with algorithms. Formatting your data for OBIS will ensure it can be read and accessed by such programs as well as understood by users. R - Reusable Publishing your data allows it to be Reused according to your chosen data usage license. Very likely you expended resources to collect your data and it would be a waste of those resources to leave your unique data unpublished and inaccessible for current and future generations. Likewise, it is better to preserve any data processing done to ensure your dataset is reproducible and/or verifiable. Finally, data in OBIS is often used in several assessment processes and used as information to support policy makers around the globe making informed decisions. There are many other benefits of publishing in OBIS, even if you haven’t published any work on it yet. This includes: Your dataset can be associated with a DOI, allowing for your dataset to be more easily cited. By ensuring your dataset citation is complete you will ensure you are being cited properly. Publishing your dataset with OBIS makes it easier to set it up as a Data paper, which generates value for you and other researchers. There are social benefits to data publishing as your work becomes integrated into a wider dataset. It gives both you and your data more visibility. This can lead to more opportunities for collaboration and further career development as a researcher or professional. Your data can be incorporated into larger analyses to better understand global ocean biodiversity, helping to shape regional and international policies. 2.2 How to handle sensitive data We recognize that sometimes your dataset may contain sensitive information (e.g., location data on endangered or poached species), or perhaps your organization does not want certain details publicly accessible. Types of sensitive data include: Location data on endangered or protected species Information regarding a commonly poached species Species or locations that have an economic impact (positive or negative) To accommodate sensitivity but still be able to contribute to OBIS, we suggest: Generalizing location information by: Obtaining regional coordinates using MarineRegions, Getty Thesaurus of Geographic Names, or Google Maps Using the OBIS Map tool to generate a polygon area with a Well-Known Text (WKT) representation of the geometry to paste into the footprintWKT field. Delay timing of publication (e.g., to accommodate mobile species) Submit your dataset, but mark it as private in the IPT so it is not published right away (i.e., until you set it as public). Alternatively, you can set a password on your dataset in order to share with specific individuals. Note that setting passwords will require some coordination with the IPT manager. By submitting your data to an IPT but not immediately publishing it, you can ensure that the dataset will be in a place to be incorporated at a later date when it is ready to be made public. This not only saves time and helps retain details while relatively fresh in your mind, but also ensures the dataset is still ready to be mobilized in case jobs are changed at a later date. GBIF has created the following Best Practices for Generalizing Sensitive data which can provide you with additional guidance. Chapman AD (2020) Current Best Practices for Generalizing Sensitive Species Occurrence Data. Copenhagen: GBIF Secretariat. https://doi.org/10.15468/doc-5jp4-5g10. "],["data_standards.html", "2.3 OBIS Data Life Cycle 2.4 Biodiversity data standards", " 2.3 OBIS Data Life Cycle The basic data life cycle for contributions to OBIS can be broken down into six step-by-step phases: Data structure Data formatting Quality control Publishing Data access (downloading) Data visualization Each of these phases are outlined in this manual and are composed of a number of steps which are covered in the relevant sections. After you have decided on your data structure and have moved to the Data Formatting stage, you must first match the taxa in your dataset to a registered list. In formatting your dataset you will ensure the required OBIS terms and identifiers are mapped correctly to your data fields and records. Depending on your data structure, you will then format data into a DwC-A format with the appropriate Core table (Event or Occurrence) with any applicable extension tables. Any biotic or abiotic measurements will be moved into the extendedMeasurementOrFact table. Before proceeding to the publishing stage, there are a number of quality control steps to complete. Once your data has been published, you and others can access datasets through various avenues and it becomes part of OBIS’ global database! This may seem like a daunting process at first glance, but this manual will walk you through each step, and the OBIS community is full of helpful resources. Throughout the manual you will find tutorials and tools to guide you from start to finish through the OBIS data life cycle. 2.3.0.1 Who is responsible for each phase? Phases 1 through 3 are the responsibilities of the data provider, while Phases 3 and 4 are shared between the data provider and the node manager. Data users are involved in Phases 5 and 6. The OBIS Secretariat is responsible for data processing and harvesting published resources. 2.4 Biodiversity data standards From the very beginning, OBIS has championed the use of international standards for biogeographic data. Without agreement on the application of standards and protocols, OBIS would not have been able to build a large central database. OBIS uses the following standards: Darwin Core Ecological Metadata Language Darwin Core Archive and dataset structure The following pages of this manual review each of these in turn. We show you how to apply these standards to format your data in the Data Formatting section. We also provide some dataset examples for your reference. "],["darwin_core.html", "", " 2.4.1 Darwin Core Terms & Guidelines Contents Introduction to Darwin Core Darwin Core terms Darwin Core guidelines Taxonomy and identification Occurrence Record level terms Location Event Time Sampling 2.4.1.1 Introduction to Darwin Core Darwin Core is a body of standards (i.e., identifiers, labels, definitions) that facilitate sharing biodiversity informatics. It provides stable terms and vocabularies related to biological objects/data and their collection. Darwin Core is maintained by TDWG (Biodiversity Information Standards, formerly The International Working Group on Taxonomic Databases). Stable terms and vocabularies are important for ensuring the datasets in OBIS have consistently interpretable fields. By following Darwin Core standards, both data providers and users can be certain of the definition and quality of data. 2.4.1.1.1 History of Darwin Core and OBIS The old OBIS schema was an OBIS extension to Darwin Core 1.2., which was based on Simple Darwin Core, a subset of Darwin Core which does not allow any structure beyond rows and columns. This old schema added some terms which were important for OBIS, but were not supported by Darwin Core at the time (e.g., start and end date and start and end latitude and longitude, depth range, lifestage, and terms for abundance, biomass and sample size). In 2009, the Executive Committee of TDWG announced their ratification of an updated version of Darwin Core as a TDWG Standard. Ratified Darwin Core unifies specializations and innovations emerging from diverse communities, and provides guidelines for ongoing enhancement. The Darwin Core Quick Reference Guide links to TDWG’s term definitions and related practices for Ratified Darwin Core. We will discuss the relevance of terms in this guide further below. In December 2013, the 3rd session of the IODE Steering Group for OBIS agreed to transition OBIS globally to the TDWG-Ratified version of Darwin Core, and the mapping of the (old) OBIS specific terms to Darwin Core can be found here. 2.4.1.2 Darwin Core (DwC) terms DwC terms correspond to the column names of your dataset and can be grouped according to class type for convenience, e.g., Taxa, Occurrence, Record, Location, etc. It is important to use DwC field names because only columns using Darwin Core terms as headers will be recognized. A list with definitions of all possible Darwin Core terms can be found on TDWG. However, OBIS does not parse all terms (note this doesn’t mean you cannot include them, they just will not be parsed when you publish to OBIS). Below is an overview of the most relevant Darwin Core terms to consider when contributing to OBIS, with guidelines regarding their use. We have also compiled a convenient checklist of OBIS-accepted terms, their DwC class type, and which OBIS file (Event Core, Occurrence, eMoF, etc.) it is likely to be found in. Note that OBIS currently has seven required and one strongly recommended DwC term: occurrenceID, eventDate, decimalLongitude, decimalLatitude, scientificName, occurrenceStatus, basisOfRecord, scientificNameID (strongly recommended). The following DwC terms are related to the Class Taxon: scientificName scientificNameID scientificNameAuthorship kingdom taxonRank taxonRemarks The following DwC terms are related to the Class Identification: identifiedBy dateIdentified identificationReferences identificationRemarks identificationQualifier typeStatus The following DwC terms are related to the Class Occurrence: occurrenceID occurrenceStatus recordedBy individualCount (OBIS recommends to add measurements to eMoF) organismQuantity (OBIS recommends to add measurements to eMoF) organismQuantityType (OBIS recommends to add measurements to eMoF) sex (OBIS recommends to add measurements to eMoF) lifeStage (OBIS recommends to add measurements to eMoF) behavior associatedTaxa occurrenceRemarks associatedMedia associatedReferences associatedSequences catalogNumber preparations The following DwC terms are related to the Class Record level: basisOfRecord institutionCode collectionCode collectionID bibliographicCitation modified dataGeneralizations type The following DwC terms are related to the Class Location: decimalLatitude decimalLongitude coordinateUncertaintyInMeters geodeticDatum footprintWKT minimumDepthInMeters maximumDepthInMeters minimumDistanceAboveSurfaceInMeters maximumDistanceAboveSurfaceInMeters locality waterBody islandGroup island country locationAccordingTo locationRemarks locationID The following DwC terms are related to the Class Event: parentEventID eventID eventDate eventType eventRemarks habitat samplingProtocol (OBIS recommends to add sampling facts to eMoF) sampleSizeValue (OBIS recommends to add sampling facts to eMoF) SampleSizeUnit (OBIS recommends to add sampling facts to eMoF) samplingEffort (OBIS recommends to add sampling facts to eMoF) The following DwC terms are related to the Class MaterialSample: materialSampleID 2.4.1.3 Darwin Core guidelines 2.4.1.3.1 Taxonomy and identification scientificName (required term) should always contain the originally recorded full scientific name, even if the name is currently a synonym. This is necessary to be able to track back records to the original dataset. The name should include authorship and date information if known and should be the lowest possible taxonomic rank that can be determined, preferably at species level or lower, but higher ranks, such as genus, family, order, class etc. are also acceptable. You may still populate scientificNameAuthorship for authorship, but it is preferred to include authorship in scientificName as well. Additionally, scientificName should only contain the name and not identification qualifications (such as ?, confer, or affinity), which should instead be supplied in the IdentificationQualifier term, see examples below. taxonRemarks can capture comments or notes about the taxon or name. verbatimIdentification should be used to record the taxon identification as it appeared in the original record, unaltered, with hybrid formulas, informal descriptions, misspellings, etc. A WoRMS LSID should be added in scientificNameID (strongly recommended term). The LSID used for scientificNameID should correspond to the name provided to the scientificName field, even if it is not the currently accepted scientific name. OBIS will use this identifier to pull the taxonomic information from the World Register of Marine Species (WoRMS) into OBIS and attach it to your dataset. This information includes: Taxonomic classification (kingdom through species) The accepted name in case of invalid names or synonyms AphiaID IUCN red list category LSIDs are persistent, location-independent, resource identifiers for uniquely naming biologically significant resources. More information on LSIDs can be found at www.lsid.info. For example, the WoRMS LSID for Solea solea is: urn:lsid:marinespecies.org:taxname:127160, and can be found at the bottom of each WoRMS taxon page, e.g. Solea solea. kingdom and taxonRank can help us in identifying the provided scientificName in case the name is not available in WoRMS. kingdom in particular can help us find alternative genus-species combinations and avoids linking the name to homonyms. Please contact the WoRMS data management team (info@marinespecies.org) in case the scientificName is missing in WoRMS. kingdom and taxonRank are not necessary when a correct scientificNameID is provided. OBIS recommends providing information about how an identification was made, for example by which ID key, species guide or expert; and by which method (e.g morphology vs. genomics), etc. The person’s name who made the taxonomic identification can go in identifiedBy and when in dateIdentified. Use the ISO 8601:2004(E) standard for date and time, for instructions see Time. A list of references, such as field guides used for the identification, can be listed in identificationReferences. Any other information, such as identification methods, can be added to identificationRemarks. Examples: scientificNameID scientificName kingdom phylum class order family genus specificEpithet scientificNameAuthorship urn:lsid:marinespecies.org:taxname:142004 Yoldiella nana Animalia Mollusca Bivalvia Nuculanoida Yoldiidae Yoldiella nana (Sars M., 1865) urn:lsid:marinespecies.org:taxname:140584 Ennucula tenuis Animalia Mollusca Bivalvia Nuculoida Nuculidae Ennucula tenuis (Montagu, 1808) urn:lsid:marinespecies.org:taxname:131573 Terebellides stroemii Animalia Annelida Polychaeta Terebellida Trichobranchidae Terebellides stroemii Sars, 1835 Data from Benthic fauna around Franz Josef Land. If the record represents a nomenclatural type specimen, the term typeStatus can be used, e.g. for holotype, syntype, etc. In case of low confidence identifications, and the scientific name contains qualifiers such as cf., ? or aff., then this name should go in identificationQualifier, and scientificName should contain the name of the lowest possible taxon rank that refers to the most accurate identification. E.g. if the specimen was accurately identified down to genus level, but not species level, then the scientificName should contain the name of the genus, the scientificNameID should contain the LSID the genus and the identificationQualifier should contain the low confidence species name combined with ? or other qualifiers. The table below shows a few examples: The use and definitions for additional ON signs (identificationQualifier) can be found in Open Nomenclature in the biodiversity era, which provides examples for using the main Open Nomenclature qualifiers associated with physical specimens. The publication Recommendations for the Standardisation of Open Taxonomic Nomenclature for Image-Based Identifications provides examples and definitions for identificationQualifiers for non-physical specimens (image-based). Examples: scientificName scientificNameAuthorship scientificNameID taxonRank identificationQualifier verbatimIdentification Pelagia Péron & Lesueur, 1810 urn:lsid:marinespecies.org:taxname:135262 genus gen. nov. Pelagia gen. nov. Pelagia benovici Piraino, Aglieri, Scorrano & Boero, 2014 urn:lsid:marinespecies.org:taxname:851656 species sp. nov Pelagia benovici sp. nov Gadus Linnaeus, 1758 urn:lsid:marinespecies.org:taxname:125732 genus cf. morhua Gadus cf. morhua Polycera Cuvier, 1816 urn:lsid:marinespecies.org:taxname:138369 genus cf. hedgpethi Polycera cf. hedgpethi Tubifex Lamarck, 1816 urn:lsid:marinespecies.org:taxname:137392 genus ? Tubifex tubifex(Müller, 1774)? Tubifex Lamarck, 1816 urn:lsid:marinespecies.org:taxname:137392 genus sp. inc. Tubifex tubifex(Müller, 1774)sp. inc. Brisinga Asbjørnsen, 1856 urn:lsid:marinespecies.org:taxname:123210 genus gen. inc. Brisinga gen. inc. Uroptychus compressus Baba & Wicksten, 2019 urn:lsid:marinespecies.org:taxname:1332465 genus sp. inc. Uroptychus compressus sp. inc. Eurythenes S. I. Smith in Scudder, 1882 urn:lsid:marinespecies.org:taxname:101607 genus sp. DISCOLL.PAP.JC165.674 Eurythenes sp.DISCOLL.PAP.JC165.674 Paroriza Hérouard, 1902 urn:lsid:marinespecies.org:taxname:123467 genus sp.[unique123]aff.pallens Paroriza sp.[unique123]aff. pallens Aristeidae Wood-Mason in Wood-Mason & Alcock, 1891 urn:lsid:marinespecies.org:taxname:106725 family stet. Aristeidae stet. Nematocarcinus Milne-Edwards, 1881 urn:lsid:marinespecies.org:taxname:107015 genus sp.indet. Nematocarcinus sp.indet. Brisinga Asbjørnsen, 1856 urn:lsid:marinespecies.org:taxname:123210 genus gen.inc. Brisinga gen.inc. Brisinga costata Verrill, 1884 urn:lsid:marinespecies.org:taxname:17825 species sp.inc. Brisinga costata sp.inc. 2.4.1.3.2 Occurrence occurrenceID (required term) is an identifier for the occurrence record and should be persistent and globally unique. If the dataset does not yet contain (globally unique) occurrenceIDs, then they should be created. Guideline for ID creation can be found here occurrenceStatus (required term) is a statement about the presence or absence of a taxon at a location. It is an important term, because it allows us to distinguish between presence and absence records. It is a required term and should be filled in with either present or absent. A few terms related to quantity: organismQuantity and organismQuantityType, have been added to the TDWG ratified Darwin Core. This is a lot more versatile than the older individualCount field. However, OBIS recommends to use the Extended MeasurementorFact extension for quantitative measurements because of the standardization of terms and the fact that you can link these measurements to sampling events and factual sampling information. Please take note that OBIS recommends all quantitative measurements and sampling facts to be placed in the ExtendedMeasurementOrFact extension and not in the Darwin Core files. In the case specimens were collected and stored (e.g. museum collections), the catalogNumber and preparations terms can be used to provide the identifier for the record in the collection and to document the preparation and preservation methods. The term typeStatus see above (under identification) can be used in this context too. Both associatedMedia, associatedReferences and associatedSequences are global unique identifiers, URLs, or URIs pointing to respectively associated media (e.g. online image or video), associated literature (e.g. DOIs) or genetic sequence information (e.g. GenBANK ID). It is recommended to ensure URLs include the domain name (e.g. NCBI) when possible. associatedTaxa include a list (concatenated and separated) of identifiers or names of taxa and their associations with the Occurrence, e.g. the species occurrence was associated to the presence of kelp such as Laminaria digitata. Data columns recording an organism’s sex, life stage, and/or behaviour should be populated with controlled vocabulary. It is recommended to include the columns in preferably both the Occurrence table and the extendedMeasurementOrFact table. The recommended vocabulary for sex can be found in BODC vocabulary collection S10, for lifeStage in BODC vocabulary collection S11, and for behavior in ICES Behaviour collection. occurrenceRemarks can hold any comments or notes about the Occurrence. recordedBy can hold a list (concatenated and separated) of names of people, groups, or organizations responsible for recording the original Occurrence. The primary collector or observer, especially one who applies a personal identifier (recordNumber), should be listed first. Example: collectionCode occurrenceID catalogNumber occurrenceStatus SluiceDock_benthic_1976/1981 SluiceDock_benthic_1976_1 SluiceDock_benthic_1976_1 present SluiceDock_benthic_1976/1981 SluiceDock_benthic_1976_2 SluiceDock_benthic_1976_2 present SluiceDock_benthic_1976/1981 SluiceDock_benthic_1979-07/1980-06_1 SluiceDock_benthic_1979-07/1980-06_1 present Data from A summary of benthic studies in the sluice dock of Ostend during 1976-1981. 2.4.1.3.3 Record level terms basisOfRecord (required term) specifies the nature of the record, i.e. whether the occurrence record is based on a stored specimen or an observation. A full list of vocabularies with definitions for this term can be found here. In case the specimen is collected and stored in a collection (e.g. at a museum, university, research institute), the options are: PreservedSpecimen e.g. preserved in ethanol, tissue etc. FossilSpecimen a fossil, which allows OBIS to make the distinction between the date of collection and the time period the specimen was assumed alive LivingSpecimen an intentionally kept/cultivated living specimen e.g. in an aquarium or culture collection. In case no specimen is deposited, the basis of record is either HumanObservation (e.g bird sighting, benthic sample but specimens were discarded after counting), MachineObservation (e.g. for occurrences based on automated sensors such as image recognition, etc), or MaterialSample (e.g. physical sample was taken, and may have been preserved or destroyed). For records pertaining to genetic samples, basisOfRecord can be MaterialSample (e.g. in the DNA-derived data extension). When the basisOfRecord is either a preservedSpecimen, LivingSpecimen or FossilSpecimen please also add the institutionCode, collectionCode and catalogNumber, which will enable people to visit the collection and re-examine the material. Sometimes, for example in case of living specimens, a dataset can contain records pointing to the origin, the in-situ sampling position as well as a record referring to the ex-situ collection. In this case please add the event type information in eventType (see OBIS manual: event). institutionCode identifies the custodian institute (often by acronym), collectionCode identifies the collection or dataset within that institute. Collections cannot belong to multiple institutes, so all records within a collection should have the same institutionCode. The collectionID is an identifier for the record within the dataset or collection. bibliographicCitation allows for providing different citations on record level, while a single citation for the entire dataset can and should be provided in the metadata (see EML). The citation at record level can have the format of a chapter in a book, where the book is the dataset citation. The record citation will have preference over the dataset citation. We do not, however, recommend to create different citations for every record, as this will explode the number of citations and will hamper the re-use of data. modified is the most recent date-time on which the resource was changed. It is required to use the ISO 8601:2004(E) standard, for instructions see Time. dataGeneralizations refers to actions taken to make the shared data less specific or complete than in its original form. Suggests that alternative data of higher quality may be available on request. This can be the case for occurrences of vulnerable or endangered species and there positions are converted to the center of grid cells. 2.4.1.3.4 Location decimalLatitude and decimalLongitude (required terms) are the geographic latitude and longitude (in decimal degrees), using the spatial reference system given in geodeticDatum of the geographic center of a Location. The number of decimals should be appropriate for the level of uncertainty in coordinateUncertaintyInMeters (at least within an order of magnitude). coordinateUncertaintyInMeters is the radius of the smallest circle around the given position containing the whole location. Regarding decimalLatitude, positive values are north of the Equator, negative values are south of it. All values lie between -90 and 90, inclusive. Regarding decimalLongitude, positive values are east of the Greenwich Meridian, negative values are west of it. All values lie between -180 and 180, inclusive. In OBIS, the spatial reference system to be documented in geodeticDatum is EPSG:4326. Coordinates in degrees/minutes/seconds can be converted to decimal degrees using our coordinates tool. We also provide a tool to check coordinates or to determine coordinates for a location (point, transect or polygon) on a map. This tool also allows geocoding location names using marineregions.org. The name of the place or location can be provided in locality, and if possible linked by a locationID using a persistent ID from a gazetter, such as the MRGID from MarineRegions. If the species occurrence only contains the name of the locality, but not the exact coordinates, we recommend using a geocoding service to obtain the coordinates. Marine Regions has a search interface for geographic names, and provides coordinates and often precision in meters, which can go into coordinateUncertaintyInMeters. Another option is to use the Getty Thesaurus of Geographic Names or Google Maps: after looking up a location, the decimal coordinates can be found in the page URL. Additional information about the locality can also be stored in DwC terms such as waterBody, islandGroup, island and country. locationAccordingTo should provide the name of the gazetteer that is used to obtain the coordinates for the locality. When country is provided, countryCode should also be populated and must adhere to ISO 3166-1-alpha-2 (i.e. 2 letter country codes defined by ISO 3166-1). For locations in international waters, we recommend the code XZ. locationID is an identifier for the set of location information (e.g. station ID, or MRGID from marineregions), for example the Balearic Plain has MRGID: http://marineregions.org/mrgid/3956. A Well-Known Text (WKT) representation of the shape of the location can be provided in footprintWKT. This is particularly useful for tracks, transects, tows, trawls, habitat extent or when an exact location is not known. WKT strings can be created using our WKT tool. This tool also calculates a midpoint and a radius, which can then be added to decimalLongitude, decimalLatitude, and coordinateUncertaintyInMeters respectively. There is also an R tool to calculate the centroid and radius for WKT polygons. wktmap.com can be used to visualize and share WKT strings. Some examples of WKT strings: LINESTRING (30 10, 10 30, 40 40) POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10)) MULTILINESTRING ((10 10, 20 20, 10 40),(40 40, 30 30, 40 20, 30 10)) MULTIPOLYGON (((30 20, 45 40, 10 40, 30 20)),((15 5, 40 10, 10 20, 5 10, 15 5))) Example: decimalLatitude decimalLongitude geodeticDatum coordinateUncertaintyInMeters footprintWKT footprintSRS 38.698 20.95 EPSG:4326 75033.17 LINESTRING (20.31 39.15, 21.58 38.24) EPSG:4326 42.72 15.228 EPSG:4326 154338.87 LINESTRING (16.64 41.80, 13.82 43.64) EPSG:4326 39.292 20.364 EPSG:4326 162083.27 LINESTRING (19.05 40.34, 21.68 38.25) EPSG:4326 Data from Adriatic and Ionian Sea mega-fauna monitoring employing ferry as platform of observation along the Ancona-Igoumenitsa-Patras lane, from December 2014 to December 2018. Keep in mind while filling in minimumDepthInMeters and maximumDepthInMeters that this should be the depth at which the sample was taken and not the water column depth at that location. When filling in any depth fields (minimumDepthInMeters, maximumDepthInMeters, minimumDistanceAboveSurfaceInMeters, and maximumDistanceAboveSurfaceInMeters), you should also consider which information is needed to fully understand the data. In most cases (e.g. scenario 1 and 4 in the figure below), providing minimumDepthInMeters and maximumDepthInMeters is sufficient for observations of organisms at particular depths. However, in cases where an occurrence is above the sea surface, e.g. flying birds (scenario 2 and 5), you should populate minimumDistanceAboveSurfaceInMeters, maximumDistanceAboveSurfaceInMeters, and, where relevant, you should also include minimumElevationInMeters and maximumElevationInMeters. The minimumDistanceAboveSurfaceInMeters and maximumDistanceAboveSurfaceInMeters is the distance, in meters, above or below a reference surface or reference point. The reference surface is determined by the depth or elevation. If the depth and elevation are 0, then the reference surface is the sea surface. If a depth is given (i.e. value >0), the reference surface is the location of the depth. This can be especially useful for sediment cores taken from the sea bottom (scenario 3 in figure and table below). In these cases the minimumDistanceAboveSurfaceInMeters/maximumDistanceAboveSurfaceInMeters would be a negative value. If no depth is given, then the elevation is the reference surface (scenario 5). Example values for each depth, distance above surface, and elevation field for the scenarios in the figure above are provided in the table below: Scenario minimumDepthInMeters maximumDepthInMeters minimumDistanceAboveSurfaceInMeters maximumDistanceAboveSurfaceInMeters minimumElevationInMeters maximumElevationInMeters 1 fish 40 40 - - 0 0 1 crab 90 90 - - 0 0 2 0 0 10 15 0 0 3 100 100 0 -1.5 0 0 4 20 22 - - 0 0 5 0 0 10 15 10 10 2.4.1.3.5 Event eventID is an identifier for the sampling or observation event. parentEventID is an identifier for a parent event, which is composed of one or more sub-sampling (child) events (eventIDs). See identifiers for details on how these terms can be constructed. eventType should be used to describe the type of event and best practice is to use controlled vocabulary (e.g. Sample, Observation, Site Visit, Survey, Project, etc.). This can help distinguish event hierarchy, e.g. Sample nested within Site Visit, Site Visited nested within Survey, Survey nested within Expedition. Note that previous recommendation was to use type or eventRemarks to hold this information. With the ratification of the eventType term in 2023, we now recommend using eventType. type should only be used to describe the record or resource type (e.g. Sound, MovingImage, etc.). habitat is a category or description of the habitat in which the Event occurred (e.g. benthos, seamount, hydrothermal vent, seagrass, rocky shore, intertidal, ship wreck etc.) samplingProtocol, sampleSizeValue, sampleSizeUnit, and samplingEffort can be included in the Event table, but OBIS also recommends to add these sampling facts to eMoF. Terms are used to respectively describe: the names/descriptions/references to sampling methods or protocols used in the event; a numeric value associated with the sample size in an event (e.g. describing the time duration, length, area, or volume); the unit associated with the sampleSizeValue; the effort expended during an event (e.g. 5 observer hours). 2.4.1.3.6 Time The date and time at which an occurrence was recorded goes in eventDate. This term uses the ISO 8601 standard and OBIS recommends using the extended ISO 8601 format with hyphens. More specific guidelines on formatting dates and times can be found in the Common Data formatting issues page 2.4.1.3.7 Sampling Information on sampleSizeValue and sampleSizeUnit is very important when an organism quantity is specified. However, with OBIS-ENV-DATA it was felt that the extended MeasurementorFact (eMoF) extension would be better suited than the DwC Event Core to store the sampled area and/or volume because in some cases sampleSize by itself may not be detailed enough to allow interpretation of the sample. For instance, in the case of a plankton tow, the volume of water that passed through the net is relevant. In case of Niskin bottles, the volume of sieved water is more relevant than the actual volume in the bottle. In these examples, as well as generally when recording sampling effort for all protocols, eMoF enables greater flexibility to define parameters, as well as the ability to describe the entire sample and treatment protocol through multiple parameters. eMoF also allows you to standardize your terms to a controlled vocabulary. The next chapter deals with the metadata (description of the dataset) in Ecological Metadata Language. "],["data_format.html", "", " 2.4.2 Darwin Core Archive Contents Darwin Core Archive OBIS holds more than just species occurrences: the ENV-DATA approach ExtendedMeasurementOrFact Extension (eMoF) eDNA & DNA derived data Extension A special case: habitat types Recommended reading 2.4.2.1 Darwin Core Archive Darwin Core Archive (DwC-A) is the standard for packaging and publishing biodiversity data using Darwin Core terms. It is the preferred format for publishing data in OBIS and GBIF. The format is described in the Darwin Core text guide. A Darwin Core Archive contains a number of text files, including data tables formatted as CSV. The conceptual data model of the Darwin Core Archive is a star schema with a single core table, for example containing occurrence records or event records, at the center of the star. Extension tables can optionally be associated with the core table. It is not possible to link extension tables to other extension tables (to form a so-called snowflake schema). There is a one-to-many relationship between the core and extension records, so each core record can have zero or more extension records linked to it, and each extension record must be linked to exactly one core record. Definitions for the core and extension tables can be found here. Besides data tables, a Darwin Core Archive also contains two XML files: one file which describes the archive and data file structure (meta.xml), and one file which contains the dataset’s metadata (eml.xml). Figure: structure of a Darwin Core Archive. 2.4.2.2 OBIS holds more than just species occurrences: the ENV-DATA approach Data collected as part of marine biological research often include measurements of habitat features (such as physical and chemical parameters of the environment), biotic and biometric measurements (such as body size, abundance, biomass), as wel as details regarding the nature of the sampling or observation methods, equipment, and sampling effort. In the past, OBIS relied solely on the Occurrence Core, and additional measurements were added in a structured format (e.g. JSON) in the Darwin Core term dynamicProperties inside the occurrence records. This approach had significant downsides: the format is difficult to construct and deconstruct, there is no standardization of terms, and attributes which are shared by multiple records (think sampling methodology) have to be repeated many times. The formatting problem can be addressed by moving measurements to a MeasurementOrFacts extension table, but that doesn’t solve the redundancy and standardization problems. With the release and adoption of a new core type Event Core it became possible to associate measurements with nested events (such as cruises, stations, and samples), but the restrictive star schema of Darwin Core archive prohibited associating measurements with the event records in the Event core as well as with the occurrence records in the Occurrence extension. For this reason an extended version of the existing MeasurementOrFact extension was created. 2.4.2.2.1 ExtendedMeasurementOrFact Extension (eMoF) As part of the IODE pilot project Expanding OBIS with environmental data OBIS-ENV-DATA, OBIS introduced a custom ExtendedMeasurementOrFact or eMoF extension, which extends the existing MeasurementOrFact extension with 4 new terms: occurrenceID measurementTypeID measurementValueID measurementUnitID The occurrenceID term is used to circumvent the limitations of the star schema, and link measurement records in the ExtendedMeasurementOrFact extension to occurrence records in the Occurrence extension. Note that in order to comply with the Darwin Core Archive standard, these records still need to link to an event record in the Event core table as well. Thanks to this term we can now store a variety of measurements and facts linked to either events or occurrences: organism quantifications (e.g. counts, abundance, biomass, % live cover, etc.) species biometrics (e.g. body length, weight, etc.) facts documenting a specimen (e.g. living/dead, behaviour, invasiveness, etc.) abiotic measurements (e.g. temperature, salinity, oxygen, sediment grain size, habitat features) facts documenting the sampling activity (e.g. sampling device, sampled area, sampled volume, sieve mesh size). Figure: Overview of an OBIS-ENV-DATA format. Sampling parameters, abiotic measurements, and occurrences are linked to events using the eventID (full lines). Biotic measurements are linked to occurrences using the new occurrenceID field of the ExtendedMeasurementOrFact Extension (dashed lines). 2.4.2.2.2 eDNA & DNA derived data Extension DNA derived data are increasingly being used to document taxon occurrences. To ensure these data are useful to the broadest possible community, GBIF published a guide entitled Publishing DNA-derived data through biodiversity data platforms. This guide is supported by the DNA derived data extension for Darwin Core, which incorporates MIxS terms into the Darwin Core standard. eDNA and DNA derived data is linked to occurrence data with the use of occurrenceID and/ or eventID. Refer to the Examples: ENV-DATA and DNA derived data for use case examples of eDNA and DNA derived data. 2.4.2.2.3 A special case: habitat types Including information on habitats (biological community, biotope, or habitat type) is possible and encouraged with the use of Event Core. However, beware the unconstrained nature of the terms measurementTypeID, measurementValueID, and measurementUnitID which can lead to inconsistently documented habitat measurements within the Darwin Core Archive standard. To ensure this data is more easily discoverable, understood or usable, refer to Examples: habitat data and/or Duncan et al. (2021) for use case examples and more details. 2.4.2.2.4 Recommended reading De Pooter et al. 2017. Toward a new data standard for combined marine biological and environmental datasets - expanding OBIS beyond species occurrences. Biodiversity Data Journal 5: e10989. hdl.handle.net/10.3897/BDJ.5.e10989 Duncan et al. (2021). A standard approach to structuring classified habitat data using the Darwin Core Extended Measurement or Fact Extension. EMODnet report. (Note you must refine search to Technical Reports from 2021 to identify Duncan et al.’s report) "],["relational_db.html", "", " 2.4.3 Relational databases: the underlying framework of OBIS If you are not familiar with relational databases, it can be difficult to understand the underlying framework OBIS relies on. This section will help you understand relational databases, how they relate to OBIS, the data you will format for OBIS, and the data you may download from OBIS. Why do we use relational databases in the first place? You are probably familiar with flat databases which contain all data in one table - this is likely how your own data are formatted. Relational databases instead consist of multiple data tables that each contain related information. When all this information is presented in one table, the table becomes larger, very complicated, and the likelihood of data duplication increases. Relational databases seek to simplify complexities and reduce redundancy by allowing information to be self-contained, but linked to each other. You can think of a relational database as separate Excel sheets or data tables that are related to each other. One data table could be a “core” table, whereas others are “extensions”. Sometimes the relationships between core and extension tables are hierarchical, but this is not always the case. There is, however, always a relationship linking core and extension tables. Let’s review core and extension tables and how we use them for OBIS. Core tables contain information that is applicable to all extension tables, and extension tables contain more information about the records within the Core table. Each table, whether core or extension, contains records and attributes. Each row is a record (e.g., a sampling event, a species’ occurrence), whereas each column is an attribute (e.g., a date, a measurement). Records between tables are linked to each other by the use of identifiers. A description of measurements pertaining to a record in an Extension table will have the same identifier as the record it is describing in the Core table. By using identifiers to link records, we reduce data repetition, see below for examples. In the Darwin Core format that OBIS uses, the core table is either Event or Occurrence, and datasets can have one, none, or more extension tables. Further explanation of data formatting in OBIS is covered in the Data Formatting section of the OBIS manual. Let’s review an example to fully understand how relational databases work. We will look at a simple relational database used by a fictional country that tracks student performance in three different courses between three schools. Rather than trying to contain information about each school, course, and student performance in one place, this information is split into three separate tables. We see that the pink table gives us information about each school - its name, and the district it belongs to. Each school also has a schoolID, an identifier linking to the blue table where we can see student performance (course mean) in each course, the class size, and year. You will notice that the course mean and class size are bundled under columns called measurementType and measurementValue. These are similar to the eMoF vocabularies and are integral to reducing repeated data, especially when one dataset has reoccurring information. Finally we see that the courseID in the blue table links to the yellow one with the courseID identifier, giving us information about each course. A fourth table could easily be created to track total school population size through time. In contrast, if this information was only presented in the pink Schools in Country table, the school information would be duplicated as you add rows for each year. In this way, you can easily see how useful relational databases are. Of course, this is a simplified example but it demonstrates how related tables can be linked by identifiers to reduce table complexity and data replication. We elaborate on how this structure is applied within OBIS here. An example of how a relational database works. Three tables show the (1) student performance (blue table) in (2) different schools (pink table) in a fictional country, and (3) the names of the courses (yellow table). Information between each table is linked by the use of identifiers, indicated by the arrows Note that when OBIS harvests data, datasets are flattened - i.e., all separate data tables are combined into one. This is the kind of file you will receive when you download data from OBIS. The reason for this is that querying relational databases significantly reduces computational time, as opposed to querying a flat database. Relational databases also facilitate requests for subsets that meet particular criteria - e.g., all data from Norway for one species above a certain depth. 2.4.3.1 How to avoid redundancy Avoiding redundancy and data duplication within your dataset is built into the OBIS data structure. Utilizing the ENV-DATA approach, which delineates relationships between the core table and extension tables, we can limit the repetition of data. For example, let us consider the dates of a ship cruise where a series of bottom trawls were taken. The sampling information (e.g., date range, equipment used, etc.) for each species collected in these trawls is the same. Because of this, we know we are dealing with unique sampling events and thus we will use Event core. So, our Event core table will contain all information related to the sampling events (e.g., date, location). Then, information pertaining to each collected species (e.g., abundance, biomass, sampling methods, etc.) will be placed in an extension, the (Extended)MeasurementOrFact table. Here, each measurement for each species and sample will occur on a separate record. These records will be linked to the correct sampling event in the Event core by an identifier - the eventID. If we were to put this data in one file, the fields related to date and location (e.g., eventDate, decimalLongitude, decimalLatitude, etc.) would be repeated for each species. Let’s consider another example. If you took one temperature measurement from the water column where you took your sample, each species found in that sample would have the same temperature measurement. By linking such measurements to the event instead of each occurrence, we are able to reduce the amount of data being repeated. Example of how sample data is distributed to core and extension tables, and how these tables are connected in OBIS An advantage of structuring data this way is that if any mistakes are made, you only need to correct it once! So you can see that using relational event structures (when applicable) in combination with extension files can really simplify and reduce the number of times data are repeated. Caveat: However we would like to note that in some cases, data duplication may occur due to the star schema structure. For example, when publishing DNA-derived data, Occurrence core will have to be used, which necessitates the repetition of event data for each occurrence record. "],["eml.html", "", " 2.4.4 Ecological Metadata Language Content Introduction Gudiance on specific metadata sections Title Abstract People and Organizations License and IP Rights Coverage (geographic, taxonomic, temporal) Keywords Project Sampling methods Citations Bibliography Collection Data External Links 2.4.4.1 Introduction to EML OBIS (and GBIF) uses the Ecological Metadata Language (EML) as its metadata standard, which is specifically developed for the earth, environmental and ecological sciences. It is based on prior work done by the Ecological Society of America and associated efforts. EML is implemented as XML. See more information on EML. When publishing on the Integrated Publishing Toolkit (IPT), you can either upload XML files with your metadata or it will be generated for you after filling in metadata sections directly on the IPT. There are a few resources available to help you generate XML EML files, including: EML R package, ezEML, and Jinja EML template generator. OBIS uses the GBIF EML profile (version 1.1). In case data providers use ISO19115/ISO19139, there is a mapping available here. For OBIS, the following 4 terms are the bare minimum required: Title, Citation, Contact and Abstract. Below is an overview of all the EML terms used to describe datasets: title [xml:lang=\"...\"]: A good descriptive title is indispensable and can provide the user with valuable information, making the discovery of data easier. Multiple titles may be provided, particularly when trying to express the title in more than one language (use the “xml:lang” attribute to indicate the language if not English/en). creator ; metadataProvider ; associatedParty ; contact : These are the people and organizations responsible for the dataset resource, either as the creator, the metadata provider, contact person or any other association. The following details can be provided: individualName givenName surName organizationName: Name of the institution. positionName: to be used as alternative to persons names (leave individualName blank and use positionName instead e.g. data manager). address deliveryPoint city administrativeArea postalCode country phone electronicMailAddress onlineUrl : personal website role: used with associatedParty to indicate the role of the associated person or organization. userID: e.g. ORCID. directory pubDate: The date that the resource was published. Use ISO 8601. language: The language in which the resource (not the metadata document) is written. Use ISO language code. abstract : Brief description of the data resource. para keywordSet keyword : Note only one keyword per keyword field is allowed. keywordThesaurus : e.g. ASFA additionalInfo : OBIS checks this EML field for harvesting. It should contain marine, harvested by iOBIS. para coverage geographicCoverage geographicDescription: a short text description of the area. E.g. the river mounth of the Scheldt Estuary. boundingCoordinates westBoundingCoordinate eastBoundingCoordinate northBoundingCoordinate southBoundingCoordinate temporalCoverage : Use ISO 8601 singleDateTime rangeOfDates beginDate calendarDate endDate calendarDate taxonomicCoverage: taxonomic information about the dataset. It can include a species list. generalTaxonomicCoverage taxonomicClassification taxonRankName taxonRankValue commonName intellectualRights: Statement about IPR, Copyright or various Property Rights. Also read the guidelines on the sharing and use of data in OBIS. para purpose: A description of the purpose of this dataset. para methods methodStep: Descriptions of procedures, relevant literature, software, instrumentation, source data and any quality control measures taken. sampling: Description of sampling procedures including the geographic, temporal and taxonomic coverage of the study. studyExtent: Description of the specific sampling area, the sampling frequency (temporal boundaries, frequency of occurrence), and groups of living organisms sampled (taxonomic coverage). samplingDescription: Description of sampling procedures, similar to the one found in the methods section of a journal article. para qualityControl: Description of actions taken to either control or assess the quality of data resulting from the associated method step. project title identifier personnel: The personnel field is used to document people involved in a research project by providing contact information and their role in the project. description funding: The funding field is used to provide information about funding sources for the project such as: grant and contract numbers; names and addresses of funding sources. para studyAreaDescription designDescription: The description of research design. maintenance description para maintenanceUpdateFrequency additionalMetadata metadata dateStamp: The dateTime the metadata document was created or modified (ISO 8601). metadataLanguage: The language in which the metadata document (as opposed to the resource being described by the metadata) is written hierarchyLevel citation : A single citation for use when citing the dataset. The IPT can also auto-generate a citation based on the metadata (people, title, organization, onlineURL, DOI etc). bibliography: A list of citations that form a bibliography on literature related / used in the dataset resourceLogoUrl: URL of the logo associated with a dataset. parentCollectionIdentifier collectionIdentifier formationPeriod: Text description of the time period during which the collection was assembled. E.g., “Victorian”, or “1922 - 1932”, or “c. 1750”. livingTimePeriod: Time period during which biological material was alive (for palaeontological collections). specimenPreservationMethod physical objectName characterEncoding dataFormat externallyDefinedFormat formatName distribution: URL links online url function=\"download\" url function=\"information\" alternateIdentifier: It is a Universally Unique Identifier (UUID) for the EML document and not for the dataset. This term is optional. 2.4.4.2 Metadata Sections There are several categories/pages for metadata you must provide, which includes basic information about the: Dataset and data provider Geographic/taxonomic/temporal coverage Keywords Hosting institution information Information regarding associated project(s) Sampling methods How to cite the dataset Museum collection (if applicable) Other external links (e.g. a homepage) or additional metadata We review each of these sections below. 2.4.4.2.1 Title The IPT requires you to provide a Shortname. Shortnames serve as an identifier for the resource within the IPT installation (so should be unique within your IPT), and will be used as a parameter in the URL to access the resource via the Internet. Please use only alphanumeric characters, hyphens, or underscores. E.g. largenet_im in http://ipt.vliz.be/eurobis/resource?r=largenet_im. After creating a new dataset resource, the field title will be filled out with the short name you provided earlier. Please make sure you provide a dataset title following the guidelines below. Dataset titles provided to OBIS node managers are often very cryptic, such as an acronym, and often only understandable by the data provider. However, to increase the discoverability and be useful for a larger audience, the dataset title should be as descriptive and complete as possible. OBIS recommends titles to contain information about the taxonomic, geographic and temporal coverage. If the dataset title does not meet these criteria and you believe the title should be changed, then contact the data provider with a suggestion or ask for a more descriptive title. If the dataset has already been published (made publicly available) - and therefore known by that title elsewhere, then the same title should be kept (even if it would not meet the proposed guidelines)! Changing the title of an already published dataset cannot be done, as this will generate confusion and possible duplicates in systems like OBIS or GBIF in a later stage. The acronym or working title could still be documented in the metadata, so there is no confusion about how the full title is linked to the originally provided acronym or working title. Caution: Always consult the data provider when changing a dataset title to a more workable and descriptive version. Originally received title Title Recommended by Node Manager BIOCEAN BIOCEAN database on deep sea benthic fauna Biomôr Benthic data from the Southern Irish Sea from 1989-1991 Kyklades Zoobenthos of the Kyklades (Aegean Sea) REPHY Réseau de Surveillance phytoplanctonique 2.4.4.2.2 Abstract The abstract or description of a dataset provides basic information on the content of the dataset. The information in the abstract should improve understanding and interpretation of the data. It is recommended that the description indicates whether the dataset is a subset of a larger dataset and – if so – provide a link to the parent metadata and/or dataset. If the data provider or OBIS node require bi- or multilingual entries for the description (e.g. due to national obligations) then the following procedure can be followed: Indicate English as metadata language Enter the English description first Type a slash (/) Enter the description in the second language Example: The Louis-Marie herbarium grants a priority to the Arctic-alpine, subarctic and boreal species from the province of Quebec and the northern hemisphere. This dataset is mainly populated with specimens from the province of Quebec. / L’Herbier Louis-Marie accorde une priorité aux espèces arctiques-alpines, subarctiques et boréales du Québec, du Canada et de l’hémisphère nord. Ce jeu présente principalement des spécimens provenant du Québec. 2.4.4.2.3 People and Organizations The EML has several possible roles/functions to describe a contact, creator, metadata provider and associated party. The contact is the person or organization that curates the resource and who should be contacted to get more information or to whom questions with the resource or data should be addressed. Although a number of fields are not required, we strongly recommend providing as much information as possible, and in particular the email address. This will also be the contact information that appears on the OBIS metadata pages. The creator is the person or organization responsible for the original creation of the resource content. When there are multiple creators, the one that bears the greatest responsibility is the resource creator, and other people can be added as associated parties with a role such as ‘originator’, ‘content provider’, ‘principal investigator’, etc. Possible functions/roles: Originator (person/organization that originally gathered/prepared the dataset) Content provider (principal person/organization that contributed content to the dataset) If the resource contact and the resource creator are identical, the IPT allows you to easily copy the information. The metadata provider is the person or organization responsible for producing the resource metadata. If the metadata are provided by the original data provider, then his/her contact details should be filled in. If no metadata are available (e.g. for historical datasets, with no contact person), then the metadata can be completed by e.g. the OBIS node manager and the OBIS node manager becomes the metadata provider. The Associated Parties contains information about one or more people or organizations associated with the resource in addition to those already covered on the IPT Basic Metadata page. For example, if there would be multiple contact persons or metadata creators, they can be added in this IPT section. The principal contact/creator should, however, be added in the IPT Basic Metadata section, not the Associated Parties section. It is recommended to complete this section together with the IPT Basic Metadata page, to avoid confusion or overlap in added information. Possible functions/roles for associated parties are: Custodian steward (person/organization responsible for/takes care of the dataset paper) Owner (person/organization that owns the data – may or may not be the custodian) Point of contact (person/organization to contact for further information on the dataset) principal investigator (primary scientific contact associated with the dataset) Notes: The owner of a dataset will, in most cases, be an institute, and not an individual person. Although the fields ‘last name’, and ‘position’ are indicated as mandatory fields, it is possible to just add the institute name in the ‘last name’ field for the role ‘owner’. The contact persons in the metadata (contact, creator, metadata creator) are used in the dataset citation (auto-generation) and those added as ‘associated parties’ are not included as “co-authors”. 2.4.4.2.4 License and IP Rights OBIS has published its guidelines on the sharing and use of data here. The recommended licenses for datasets published in OBIS are the Creative Commons Licenses (CC-0, CC-BY, CC-BY-NC), of which CC-0 is the most preferred and CC-BY-NC is least preferred. A Creative Commons license means: You are free: to share => to copy, distribute and use the database to create => to produce works from the database to adapt => to modify, transform and build upon the database In case of CC-0: public domain: CC-0 is the preferred option identified by the OBIS steering group. You waive any copyright you might have over the data(set) and dedicate it to the public domain. You cannot be held liable for any (mis)use of the data either. Although CC-0 doesn’t legally require users of the data to cite the source, it does not take away the moral responsibility to give attribution, as is common in scientific research. A good blog on why using CC-0 can be found here. In case of CC-BY: Attribution: You must attribute any public use of the database, or works produced from the database, in the manner specified in the license. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database. In case of CC-BY-NC: non-commercial: like CC-BY but commercial use is not allowed. This licence can be problematic when the data is re-used in scientific journals. 2.4.4.2.5 Coverage 2.4.4.2.5.1 Geographic Coverage The IPT allows you to enter the geographic coverage by dragging the markers on the given map or by filling in the coordinates of the bounding box. In the description field, a more elaborate text can be provided to describe the spatial coverage indicating the larger geographical area where the samples were collected. For the latter, the sampling locations can be plotted on a map and – by making use of a Gazetteer – the wider geographical area can be derived: e.g. the relevant Exclusive Economic Zone (EEZ), IHO, FAO fishing area, Large Marine Ecosystem (LME), Marine Ecoregions of the World (MEOW), etc. The Marine Regions’ Gazetteer might prove to be a useful online tool to define the most relevant sea area(s). There are also LifeWatch Geographical Services that translate geographical positions to these wider geographical areas. The information given in this section can also help the OBIS node manager in geographic quality control. If the geographic coverage in the EML e.g. is “North Sea”, but a number of data points are outside of this scope, then this may indicate errors, and should be checked with the data provider. If the dataset covers multiple areas (e.g. samples from the North Sea and the Mediterranean Sea), then this should clearly be mentioned in the geographicDescription field. Note that the IPT only allows one bounding box, and you have to uncheck the “Set global coverage” box to change box bounds. Screenshot of the Geographical Coverage section of the metadata, emphasizing how to change the bounds of the coverage box in the map. 2.4.4.2.5.2 Taxonomic Coverage This section can capture two things: A description of the range of taxa that are addressed in the data set. OBIS recommends to only add the higher classification (Kingdom, Class or Order) of the involved groups (e.g. Bivalvia, Cetacea, Aves, Ophiuroidea…). You can easily draw a list of higher taxonomic ranks from the WoRMS taxon match service (or ask the data provider). The taxonomic coverage is not a mandatory field, but the information stored here can be very useful as background information. The description can also contain common names, such as e.g. benthic foraminifera or mussels. An overview of all the involved taxa (not recommended, as all the taxa are already listed in the dataset). Note: OBIS also recommends to add information on the (higher) taxonomic groups in the (descriptive) dataset title and abstract. Example of the Taxonomic Coverage section of the metadata 2.4.4.2.5.3 Temporal Coverage The temporal coverage will be a date range, which can easily be documented. If it is a single date, the start and end date will be the same. The information added here can be used as a quality check for the actual dates in the datasets. You can also document the Formation Period or the Living Time Period in this section for specimens that may not have been alive during the collection period, or to indicate the time during which the collection occurred. Example of the Temporal Coverage section of the metadata 2.4.4.2.6 Keywords Relevant keywords facilitate the discovery of a dataset. An indication of the represented functional groups can help in a general search (e.g. plankton, benthos, zooplankton, phytoplankton, macrobenthos, meiobenthos …). Assigned keywords can be related to taxonomy, habitat, geography or relevant keywords extracted from thesauri such as the ASFA thesaurus, the CAB thesaurus or GCMD keywords. As taxonomy and geography are already covered in previous sections, there is no need to repeat related keywords here. Please consult your data provider which (relevant) keywords can be assigned. Example of the Keywords section of the metadata, showing input for a marine fishes dataset 2.4.4.2.7 Project If the dataset in this resource is produced under a certain project, the metadata on this project can be documented here. Part of the information entered here, can partly overlap with information given in other sections of the metadata (e.g. study area description can have lot of parallel with the geographic coverage section). Personnel involved in the project can be documented or repeated here as well. This is not a problem. 2.4.4.2.8 Sampling Methods The EML can contain descriptions of the sampling and data processing methods. Study extent can be documented here as well to report a more specific geographic area as well as the sampling frequency. Descriptions of sampling procedures, quality control, and steps (sample or data processing) can be given in the same way as the methods section of a scientific paper. Note that OBIS best practice is to add sampling facts to the extended MeasurementorFact extension, linked to the sampling events in the Event core via eventID. 2.4.4.2.9 Citations The dataset citation allows users to properly cite your dataset in further publications or other uses of the data. When users download datasets from the OBIS download function, a list of the dataset citations packaged with the data in a zipped file is provided. A dataset citation is different from the data source citation (in case the data is digitized from a publication), and these references can be added to the additional metadata (see bibliography below). A dataset citation can have the same format of a journal article citation, and should include the authors (contact, creator, principle investigator, data managers, custodians, collectors…), the title of the dataset, the name of the data publisher (or custodian institute), and the access point URL to the resource. GBIF’s IPT has an auto-generation - Turn On/Off - tool to let the IPT auto-generate the resource citation for you. The citation includes a version number, which is especially important for datasets that are continuously updated. The dataset citation can also include a Citation Identifier - a DOI, URI, or other persistent identifier that resolves to an online dataset web page. The OBIS node data managers should try to implement a certain degree of format standardization for the dataset citations. The IPT provides an option to auto-generate a citation based on the EML and is formatted as follows: {dataset.authors} ({dataset.pubDate}) {dataset.title}. [Version {dataset.version}]. {organization.title}. {dataset.type} Dataset {dataset.doi}, {dataset.url} 2.4.4.2.10 Bibliography The EML can include the citation of the publications that are related to the described dataset. They can describe the dataset, be based on the dataset or be used in this dataset. Publications can be scientific papers, reports, PhD or master theses. If available, the citation should include the DOI at the end. This overview will contribute to a better understanding of the data as these publications can hold important additional information on the data and how they were acquired. 2.4.4.2.11 Collection Data This IPT section should only be filled out if there are specimens held in a museum. If relevant, it is strongly recommended that this information is supplied by the data provider or left blank. The collection name, specimen preservation method, and curatorial units should be provided, as applicable. Screenshot of the Collection Data page showing what information can be provided for museum specimens 2.4.4.2.12 External Links This section can include URLs to the resource homepage, to download or find additional information. You can also provide links to your resource if it is hosted elsewhere in different formats. Links to the online dataset on the OBIS website can be added once the data is available there. For these OBIS links, the required fields should be completed as follows: Name: online dataset Character set: UTF-8 Data format: html If other links are added, then the data format for web-based data is ‘html’. If the link refers to a file, the data format of the file will need to be added (e.g. .xlsx, .pdf …). The character set for all Darwin Core files is UTF-8, whereas for other web pages this can vary, so you may need to confirm. 2.4.4.2.13 Additional Metadata Any remaining information that could not be catalogued under any of the other metadata, can be mentioned here. This may include logos, purpose of the dataset, a description of how the dataset will be maintained, etc. "],["nodes.html", "3 OBIS Terms of Reference: Nodes, Coordination Groups, Secretariat 3.1 TOR of OBIS nodes 3.2 TOR of OBIS Steering Group 3.3 TOR of OBIS Co-Chairs 3.4 TOR of OBIS Secretariat 3.5 TOR of OBIS Coordination Groups 3.6 TOR of OBIS Executive Committee", " 3 OBIS Terms of Reference: Nodes, Coordination Groups, Secretariat Content OBIS Node Terms of Reference OBIS Steering Group Terms of Reference OBIS Co-Chair Terms of Reference OBIS Secretariat Terms of Reference Terms of Reference for OBIS Coordination Groups OBIS Executive Committee Terms of Reference 3.1 TOR of OBIS nodes OBIS Nodes are either national projects, programmes, institutes, or organizations, National Ocean Data Centers or regional or international projects, programmes and institutions or organizations that carry out data management functions. OBIS nodes Terms of Reference are detailed below. Required OBIS nodes can be national, regional or thematic Be an IODE National Oceanographic Data Centre (NODC), or IODE Associate Data Unit (ADU) Be responsible for ensuring the timely delivery of high-quality data (and metadata) and data flows from marine data providers to OBIS and/or other global aggregators (e.g. GBIF) following the principle of “publish once“, the guidelines in the OBIS manual (with respect to obtaining permission, licenses, QC…) and the FAIR Data Principles At least one staff member holds a current OBIS certification (completing recognized training OBIS/OTGA course “Contributing and publishing datasets to OBIS”) with a 12 month grace period for acquiring said certification after a Node is established Be compliant with the IOC data policy (http://iode.org/policy) and implementation guidelines from OBIS (Data Policy) with a 12 month grace period for achieving compliance after a Node is established Be a member of the Nodes Coordination Group (Nodes CG), and other OBIS coordination groups if desirable Provide an annual work plan to the Nodes CG and report on activities to the Nodes CG Provide user/community support (e.g data queries, data analyses, and feedback) Optional May maintain a presence on the Internet (e.g., a web page, social media or data portal) representing their specific area of responsibility May establish and/or join OBIS collaboration agreements with OBIS partners, and report on these collaborations to the Nodes CG Encouraged to undertake outreach and communication activities (e.g. social media, conferences), in accordance with the OBIS Communication Plan Encouraged to engage in capacity development activities in collaboration with the OBIS secretariat. These activities may include offering expertisel, conducting academic and professional training sessions, and providing support in various aspects of data management. Additionally, assistance in publishing data to global aggregators like OBIS and GBIF, utilizing relevant technologies, and applying our community standards and best practices Encouraged to engage in adjacent and affiliated user communities and actively seek new partners (e.g. data providers, data using partners) Notes: OBIS nodes are eligible to become a member of the SG and have voting rights. A formal letter expressing their commitment to contribute according to Terms of Reference of the SG must be renewed every 2 years OBIS nodes can become a member of and contribute to the various OBIS coordination groups 3.1.1 How to become an OBIS node OBIS nodes now operate under the IODE network as either National Oceanographic Data Centres (NODCs) or Associate Data Unites (ADUs). Prospective nodes are required to apply to the IODE for membership. The procedure to become an OBIS node is as follows: If you are an existing NODC (within the IODE network) and the OBIS node activities fall under the activities of the NODC: Send a letter expressing your interest to become an OBIS node (including contact information of the OBIS node manager, and geographical/thematic scope of your OBIS node) If you are not an existing NODC: Email your application form to become an IODE Associate Data Unit (ADU), with a specific role as OBIS node. Applications for ADU membership in OBIS shall be reviewed by the IODE Officers in consultation with the IODE Steering Group for OBIS. OBIS Node Health Status Check and Transition Strategy OBIS nodes should operate under IODE as either IODE/ADU or IODE/NODC. As such OBIS nodes are a member of the IODE network. The IODE Steering Group (SG) for OBIS evaluates the health status of OBIS nodes at each annual SG meeting, and considers an OBIS node as inactive when it meets any of the following conditions: The OBIS node manager recurrently fails to answer the communications from the project manager or the SG co-chairs in the last 12 months The OBIS node manager or a representative fails to attend (personally or virtually) the last 2 SG meetings without any written reason The OBIS node does not have an IPT The OBIS node has an IPT, but it has not been running for the last 12 months The datasets in the OBIS node’s IPT have been removed and not restored in the last 12 months (without any explanation) The OBIS node has not provided new data for the last 2 years The OBIS Secretariat prepares a health status check report of each OBIS node based on the six items above and informs the OBIS node manager on their status 3 months before the SG meeting. At the SG meeting, the SG-OBIS co-chair will present the results of the OBIS nodes health status check report including a listing of the inactive OBIS nodes. The SG-OBIS members representing active OBIS Nodes will make one of the following decisions: Request the inactive OBIS node to submit a plan with actions, deliverables and times to improve their performance, within 3 months, to the OBIS Secretariat. This plan is reviewed and accepted by the OBIS-Executive Committee Or Provide a recommendation to the IOC Committee on IODE to remove the OBIS node from the IODE network. In either case, the OBIS Secretariat will inform the OBIS node manager of the SG-OBIS decision, with a copy to the IODE officers and the IODE national coordinator for data management of the country concerned. The IODE Committee is requested to consider the recommendation from the OBIS Steering Group and it may either accept the recommendation or request the inactive OBIS node to submit an action plan (option 1). When the inactive OBIS node is removed from the IODE network, the SG-OBIS will ask whether another OBIS node is interested in taking over the responsibilities of the removed OBIS node, until a new OBIS node in the country/region is established. 3.2 TOR of OBIS Steering Group Required Propose, and revise as necessary, the vision, mission, objectives, strategies (e.g. sustainability), management structure, work plans, budgets and timetables for OBIS Review and respond to reports from OBIS coordination groups Monitor the implementation of the adopted work plan, identify any technical, scientific or capacity challenges and suggest appropriate responses Seek to support each other with resource mobilisation to advance the mission of OBIS together Report to the IODE Management Group (every year) and IODE Committee (every two years) Notes Regular meetings of the Steering Group will be held at least once a year. The Co-Chairs can decide, in consultation with the members, to have additional meetings if needed OBIS Steering Group decisions are made by consensus. Voting will only be organized if co-chairs identify that all means have been exhausted and no consensus can be reached with decisions accepted by two-thirds majority of members in attendance, with one vote per node in attendance Co-chairs can act on behalf of their Nodes for portions of these meetings if they explicitly state the role in which they are acting Membership Two Co-Chairs (2 seats), who are elected from the membership of the Steering Group Co-Chairs of the OBIS coordination groups (6 seats). Appointments shall be for a term of 2 years, renewable for one further term. OBIS nodes (up to 16 seats): OBIS node managers (or one alternate) who are not already represented as co-chairs or CG co-chairs. Each OBIS node should inform the OBIS secretariat of any changes to their representative(s) as soon as possible. OBIS nodes need reconfirmation after two 2-year terms OBIS secretariat (2 seats) OBIS Programme manager as SG-OBIS technical secretary, and OBIS technical coordinator 3.3 TOR of OBIS Co-Chairs The role of (Co-)Chair(s) is: To (Co-)Chair sessions of the OBIS Steering Group and OBIS Executive Committee To call for meetings of the OBIS Steering Group, in close consultation with the Programme Manager and/or IODE/OBIS Secretariat To assist the Programme Manager and/or IODE/OBIS Secretariat with the preparation of OBIS Programme Component reports To represent OBIS and its Steering Group, and to promote OBIS at meetings and other events Notes: Co-Chairs must act independently of other affiliations and will not represent their institution, node or country while performing co-chair duties. Co-Chairs may focus (in addition to the tasks outlined above) on particular elements of the OBIS work plan if they have specific expertise in those elements. Co-Chairs should expect to dedicate a minimum of 160 working hours per year to their duties, including monthly video conferences, one or more representations at IODE management group and Committee meetings, major international conferences or special events on behalf of OBIS, and leadership duties in preparation and execution of annual SG-OBIS meetings. Co-Chairs positions are not funded by IODE/OBIS, and SG-OBIS members seeking nomination to become Co-Chair are encouraged to obtain full support from their home institutions to cover time and expenses of the role as part of core duties. Travel support may be provided by the IODE OBIS Programme component when available. It is required that Programme Managers, IODE/OBIS Secretariat and OBIS Co-Chairs communicate frequently. Co-Chairs are elected based on their level of activity in the work of the Steering Group over a period of time, the respect they have gained from the membership of the Group and their professional expertise related to the subject of OBIS. Proficiency in the working language of the group is essential. Experience in mobilizing resources is an additional element but not a requirement. Elections of Co-Chairs can only be held during regular meetings of the Steering Group during which a quorum of the membership is present. Co-Chairs are elected for one inter-sessional period of the Steering Group (regular meeting), with the possibility of re-election for an additional term. In exceptional circumstances Co-Chairs (or one of them) can be re-elected for a third term. If more than one regular meeting is organized within a calendar year then the term of office will be one year. Exceptions (e.g. further extensions) are possible by a decision of the IODE Committee. In exceptional cases one or more Co-Chairs can be invited from outside the Steering Group if they can bring in exceptional expertise or experience or if no other candidates have come forward. In such cases the Steering Group will need to invite them as members first after which he/she/they can be elected (Co-)Chair(s). Non-voting members are ineligible to serve as Co-Chair. When (Co-)Chair(s) step down after one or two terms they may remain as members of the Steering Group and Executive Committee for the subsequent term. They should inform the OBIS Programme Manager at the time they inform of their decision to step down as co-chair. When Co-Chairs step down after one or two terms, they will become Past Co-Chairs and will be invited to provide guidance to the incoming Co-Chairs during the first term of the new Co-Chairs. This will be a voluntary service to the new Co-Chairs and the outgoing Co-Chairs may decline. Co-Chairs should inform the OBIS Programme Manager and IODE Secretariat of their desire to step down at least 6 months before the expiry of their term of office or next regular meeting of the Steering Group, whichever comes first. This will allow the Programme Manager and IODE Secretariat to start the election procedure for the new Co-Chairs. The Co-Chairs are not involved in the day-to-day implementation of OBIS which is the remit of the OBIS Programme Manager. Nominations for Co-Chairs shall be accepted from members of the SG-OBIS as either nomination on behalf of another or self-nominations. 3.4 TOR of OBIS Secretariat The OBIS secretariat, hosted at the UNESCO/IOC project office for IODE in Oostende (Belgium), provides training and technical assistance to its network of partners including OBIS nodes and data providers, guides and advices on the development of new data standards and technical developments, and encourages international cooperation and implement the OBIS work plan and oversees the budget, to foster the group benefits of the network. The OBIS Secretariat currently has 2 fixed term regular programme positions: an OBIS programme manager and an OBIS technical coordinator with the following tasks and objectives: The OBIS programme manager will: Coordinate the day-to-day planning and implementation of the OBIS work plan, including organizing meetings of the OBIS steering group, OBIS executive committee, OBIS coordination groups (data, products, and OBIS nodes), and supervising the OBIS secretariat staff. Coordinate the planning and implementation of other Extra Budgetary projects. Support the implementation of the IODE work plan and assist with the planning and implementation of the IODE/OBIS contribution to the UN Decade of Ocean Science for Sustainable Development. Establish, maintain, and strengthen (international) partnerships (e.g., GBIF, MBON, GOOS, MarineLife2030, CBD, BBNJ, IPBES etc). Ensure a performance management culture within the secretariat team. Through regular staff team meetings: listen, seek views, monitor performance, solve problems, and meet needs of colleagues and collaborators. More specifically: set clear objectives for the team and ensure effective/timely completion of all performance management related tasks. provide regular performance feedback on the implementation of set task objectives, including expected behavior and development needs. address all performance related issues in a timely manner and propose corrective measures for any under-performance. In cases of strong performance, commend staff and seek ways to leverage for improved overall team performance. Set professional and inter-personal development objectives. The OBIS technical coordinator will: Be responsible for the technical and scientific coordination, implementation and maintenance of the OBIS data system and planning of related programme activities. More specifically: In close collaboration with the Head of the IOC Project Office for IODE, OBIS programme component manager and OBIS team, establish technical programme goals, plan of activities, budget proposals, and performance measures - in accordance with technical guidance from the IODE Committee and OBIS coordination and steering groups. Coordinate and provide technical maintenance and further development of the OBIS technology stack and infrastructure; Manage the technical implementation of the OBIS work plan; Manage and provide support to the OBIS data and products coordination groups; Provide terms of reference for, and monitor/evaluate progress of, contractors hired to provide technical work; Collaborate closely with the Ocean Data and Information System (ODIS) team to ensure interoperability between ODIS and OBIS. Resource mobilization: Manage drafting technical and scientific aspects of extra-budgetary project proposals, as well as the technical and scientific implementation of approved extra-budgetary projects. Networking and Outreach: Participate in meetings of technical groups as well as the OBIS steering group and other relevant events; Assist with the promotion and communication of the OBIS programme component. Provide assistance with technical support of IODE databases and other IT services of, or hosted by, the IOC Project Office for IODE, Oostende, Belgium. 3.5 TOR of OBIS Coordination Groups Each of the coordination groups will have one or two Co-Chair(s) who will: Chair meetings according to the group’s agreed-upon schedule Ensure that tasks are appropriately distributed amongst the group members Coordinate their activities with the other coordination groups Report to and represent their group at the OBIS SG Participate/report in OBIS Executive Committee meetings Each group should also maintain a secretary position to promote consistent notetaking across meetings. All coordination groups should use the same collaborative tools, proposed by the Secretariat and available in all countries, to maintain a clear record of group activities and progress accessible to the Secretariat and members of the OBIS Coordination Groups. The Coordination Group reports will be made public. OBIS Nodes Coordination group The Nodes Coordination group (NCG) provides a forum for all OBIS nodes to discuss ongoing activities, priorities, and barriers they may be facing. Membership to this group is mandatory for all OBIS nodes, with at least one representative from each node attending. Meetings will occur every two months. Chairs will lead the membership in pursuing methods to promote communication from all parts of the world and overcome the challenge of time-zone coordination (e.g. leveraging asynchronous communication methods). Group activities will include: Facilitate inter-Node communication and exchange of expertise Define priority objectives to guide the efforts of OBIS Nodes Coordinate and distribute the work related to mobilisation of datasets Provide advice to the OBIS Steering Group on the OBIS science mission, policy, and management relevance and strategic priorities Identify and pursue new directions, potential pilot projects, potential resources, and areas of development for data-driven research and ocean policy and management applications, as well as investigate timely topics to help set future strategic directions This will be done in coordination with the Data Coordination Group and Products Coordination Group Coordinate communication of Node successes and achievements Provide annual OBIS node activity reports and mutual support for the challenges of individual nodes Identify and resolve training needs within the community Coordinate and implement any inter-Node related activities agreed-upon by the SG to achieve the OBIS mission OBIS Data Coordination group The OBIS Data Coordination Group (DCG) will focus on topics/issues related to OBIS Priority Area 1: Data mobilisation and input. The OBIS data coordination group will consist of two Co-Chairs and a body of voluntary members. There will be no limit to the number of members who can join. Members can include OBIS Sec, node staff, as well as interested or invited experts. The OBIS data coordination group will: Identify, prioritise, and propose solutions for issues around the following topics: Data and metadata standards and formats Data QC Taxonomy Vocabularies Methods of bringing new data into the system Identify data gaps and, in collaboration with the OBIS Nodes Coordination group, prioritise and coordinate data mobilisation efforts Ensure the fitness of quality metrics for assessing the current status of OBIS data (e.g. spatial and taxonomic completeness) Maintain the OBIS Manual; especially regarding standards or methods Engage with activities of other relevant bodies (e.g. TDWG, GBIF, GOOS, SCOR) by identifying representatives who will report back to the group OBIS Products Coordination group The OBIS Products Coordination group (PCG) is a collaborative and interdisciplinary group that is driven by the importance of creating data and information products (i.e. indicators) that are scientifically sound, practical, and relevant to decision-makers in government, industry, and civil society. PCG focuses on OBIS Priority Area 2: Data application and output. The PCG will consist of one or two co-chairs and a body of voluntary members. There will be no limit to the number of members who can join. Members can include OBIS Sec, node staff, as well as interested or invited experts. We consider data and information products any type of analysis (description, data visualisation, etc.) that synthesises and generates new information from data hosted on OBIS and other sources. The OBIS Products Coordination Group will: Identify, prioritise, and coordinate the development of data and information products that are of interest to our user community Advise on how best to showcase and catalogue data and information products developed by the wider OBIS community, in line with the OBIS Data Policy, including proper acknowledgment of other formats of resources (e.g. software applications, workflows, papers, etc.) Set minimum metadata and quality requirements for data and information products Propose and develop tools, pipelines, and documentation that can bolster the development of products based on OBIS data. Support groups/institutions working on products development (e.g. early warning systems, ecological synthesis groups, etc.) to identify potential collaborations Propose a process for frequent expert validation of data and information products by consulting with local scientific experts and end-users (including local communities and indigenous people) Engage with activities of other relevant bodies by identifying representatives and report back to the group 3.6 TOR of OBIS Executive Committee The OBIS Executive Committee will: Support the secretariat and assist with the management of OBIS Address issues that arise intersessionally Work with the secretariat to draft documents, monitor the work plan, and otherwise ensure that the work of the Steering Group and Coordination Groups progresses To monitor the implementation of the adopted work plan, identify any technical, scientific and capacity challenges and suggest appropriate responses Notes: The OBIS EC meets 4 times per year, online and in-person if necessary The OBIS EC can invite experts to join a meeting as required Members: SG-OBIS Co-Chairs and the most recent outgoing Co-Chair(s) Coordination groups Co-Chairs Secretariat/ Programme Manager "],["formatting.html", "4 Dataset structure 4.1 When to use Event Core 4.2 When to use Occurrence Core 4.3 Extensions in OBIS 4.4 Data formatting tools", " 4 Dataset structure Note: OBIS guidelines have not yet been updated to reflect changes that allow DNA Derived data to be linked to Event core tables. We will update this page soon. Formatting data can be challenging. This section of the manual deals with how to format data for OBIS, beginning with an overview of dataset structure. Determining how your dataset will be structured is one of the first steps towards getting your data ready for publishing. At this first step it is important to determine which structure best suits your dataset before proceeding because it will determine which Darwin Core fields will need to be included in your data. Once you have decided on the dataset structure, you can continue formatting the dataset. We have created the following flow chart for an overview on how to determine what structure best suits your data. Flow chart to help determine which dataset structure will suit your data For more guidance, see the sections below. 4.1 When to use Event Core Event Core describes when and where a specific sampling event happened and contains information such as location and date. Event Core is often used to organize your data tables when there are more than one sampling occasion and/or location, and different occurrences linked to each sampling. This organization follows the rationale of most ecological studies and typical marine sampling design. It covers: When specific details are known about how a biological sample was taken and processed. These details can then be defined in the eMoF Extension with the Q01 vocabulary When the dataset contains abiotic measurements, or other biological measurements which are related to an entire sample (not a single specimen). For example a biomass measurement for an entire sample, not each species within the sample Event Core can be used in combination with the Occurrence and eMoF extensions. The identifier that links Event Core to the extension is the eventID. parentID can also be used to give information on hierarchical sampling. occurrenceID can also be used in datasets with Event Core in order to link information between the Occurrence extension and the eMoF extension. 4.2 When to use Occurrence Core Occurrence Core datasets describe observations and specimen records and cover instances when: No information on how the data was sampled or how samples were processed is available No abiotic measurements are taken or provided You have eDNA and DNA-derived data Biological measurements are made on individual specimens (each specimen is a single occurrence record) Occurrence Core is also often the preferred structure for museum collections, citations of occurrences from literature, and sampling activities. Datasets formatted in Occurrence Core can use the eMoF Extension for when you have biotic measurements or facts about your specimen. The DNA derived data extension can also be used to link to DNA sequences. The identifier that links Occurrence Core to the extension(s) is the occurrenceID. 4.3 Extensions in OBIS Currently OBIS accepts the following extensions: Occurrence MeasurementOrFact extendedMeasurementOrFact DNADerivedData How are extensions linked to core tables in OBIS? As established in the relational database section, OBIS relies on datasets being formatted according to a relational database structure. The ENV-DATA approach that OBIS implements means your dataset will have a Core table and (optional) Extension tables. As a review, a core file contains information relevant and applicable to each record in the extension(s). An extension file then contains records that link back to a record in the core file with more specific information (e.g., methods, measurements, facts, DNA sequences, etc.). The extension file(s) accepted by OBIS ((e)MoF, Occurrence, DNA) are linked to your core tables by the use of identifying ID codes. These codes could be either eventID or occurrenceID. For details on how to construct these IDs, click here. See the diagram below for an example of how core and extension tables can be linked. Linking extensions via identifiers If your core file is based on occurrences (e.g., a record of one or more taxa specimens), then any extensions are linked with occurrenceID. If your core file is based on events (e.g., a sampling event, cruise, observation, etc.), then the linking identifier is eventID. In the Core tables, identifiers are always unique, which means, they do not repeat and each line has a different identifier. On the other hand, multiple records in an extension file can have the same identifier which will link them to the same event or occurrence record (depending on which is the Core). The different linking identifiers are shown in the figure below. Diagram of how the different core tables are linked to their extensions by different identifiers. Let us consider a fictional plankton trawl sampling event to demonstrate how identifiers link Core and Extension tables in OBIS. This trawl used two types of nets, occurred in March 2013, and has an eventID plankton-northsea-2013-03. Suppose we have information about the types of trawl used and the species abundance from this trawling event. The information (e.g., date) of the sampling event itself would be found in the Event Core, whereas the abundance data and sampling methods would be in the eMoF table. How do we ensure the abundance and sampling method data is properly linked to the correct event? By using the same eventID for each record in the eMoF table, plankton-northsea-2013-03, the information is properly linked between the Event Core and the eMoF extension. 4.4 Data formatting tools The GBIF Norwegian Node created the DwC Excel Template Generator. This tool will generate four different types of blank Excel spreadsheets: Occurrence Core, MeasurementOrFact, Metadata, and a README. This tool works best if you already know which Darwin Core fields you need, although a default template can be generated. Another tool from Norway is the Excel to Darwin Core Standard (DwC) Tool. This is a macro Excel spreadsheet that helps create templates for Event (aka Sampling-Event) and Occurrence core tables, as well as MeasurementsOrFacts, Extended MeasurementsOrFacts, and Simple Multimedia extensions. GBIF provides an Occurrence core template and an Event core template. If you use these templates from GBIF, be aware that GBIF’s required terms are different from OBIS. There are also some tools that can help you unpivot (or flatten) data tables. These can be used to flatten many columns into one, particularly useful for the eMoF table. GBIF Norway’s crosstab to list converter. Note that this tool is not completely automated Excel’s built-in unpivot function "],["identifiers.html", "4.5 Constructing and using identifier codes", " 4.5 Constructing and using identifier codes Content eventID occurrenceID 4.5.1 eventID Using a unique identifier for each physical sample or subsample in your dataset taken at each location and time is highly recommended to ensure sample traceability and data provenance. eventID is an identifier for an individual sampling or observation event, whereas parentEventID is an identifier for a parent event, which is composed of one or more sub-sampling (child) events (eventIDs). eventID can be used for replicated samples or sub-samples. It is important to make sure each replicate sample receives a unique eventID, which could be based on the unique sample ID in your dataset. Sample ID can also be recorded in materialSampleID, as OBIS does not need to have separate eventIDs and materialSampleIDs. Rather OBIS can treat these two terms as equivalent. Be sure to still fill in the eventID field if you want to use materialSampleID, as OBIS only uses eventID and parentEventID for structuring datasets, not sample ID. This does not prevent you from using the field if you would like to. If you do not already have a materialSampleID, creating a unique eventID for your data records can be as straightforward as combining different fields from your data. Note You should consider carefully what combination of fields will generate a unique event. Combinations including date, time, location, and depth are common elements to help generate such unique codes. Including the event type can also be useful for datasets with hierarchical sampling methods (e.g., samples taken from a station within a cruise). Repeating the parentEventID in the child event (use : as delimiter) can make the structure of the dataset easier to understand. Nesting event information in this way also allows you to reduce redundancy and still provide information relevant to each level of sampling. Broadly, an eventID can take the form of [parentEventID]:[sample type]_[sample ID] Thus to construct a unique eventID for parent and child events, you join relevant sampling information. Possible configurations (with examples) could include: Project_cruise_station_date_sample STAR_arcticsea_st3520_1989-04-04_s01 Project_habitat_Genus_species_year_sampletype_samplenumber BEE_seamount_Genus_species_2013_cruise_s123 Institution_year_location_samplemethod_sample Concordia_2003_Coast_Station1_seine_s01 Concordia_2003_Coast_Station1_trap_s01 These examples are not exhaustive and other similarly structured variations that fit your data are acceptable. Consider also including year within your eventIDs to ensure codes remain globally unique in subsequent years, which is particularly useful if your sampling protocol is repeated temporally. Remember, what is the main information about a sampling event that helps you identify it? For instance, it is helpful when we know the location, date, project, habitat. So you can build your eventID code based on this information and ensuring they will not repeat (e.g., will result in a unique identifier). Information related to your sampling events can be assigned to the highest relevant event level in order to avoid repetition of information. For example, if all samples taken from a station occurred at the same depth, this information can be listed once. Variation between samples (e.g., exact time or coordinates) can also be easily reflected for each event. See the table below for a demonstration. eventID parentEventID eventRemarks eventDate maximumDepthInMeters cruise_1 cruise cruise_1:station_1 cruise_1 station 15 cruise_1:station_1:core_1 cruise_1:station_1 sample 2011-03-06T08:35 cruise_1:station_1:core_2 cruise_1:station_1 sample 2011-03-06T08:52 cruise_1:station_1:core_1:subsample_1 cruise_1:station_1:core_1 subsample We recommend using controlled vocabulary for the “type” column. Although no standards have been agreed upon yet, commonly used terms for event type included are cruise, stationVisit, transect, quadrat, sample, subSample. Consider another example from a real dataset below: eventID parentEventID eventDate eventRemarks IOF_benthos_Plominski_zaljev_2000_crs cruise IOF_benthos_Plominski_zaljev_2000_stat1 IOF_benthos_Plominski_zaljev_2000_crs 2000-08 stationVisit IOF_benthos_Plominski_zaljev_2000_stat2 IOF_benthos_Plominski_zaljev_2000_crs 2000-08 stationVisit IOF_benthos_Plominski_zaljev_2000_s01 IOF_benthos_Plominski_zaljev_2000_stat1 sample IOF_benthos_Plominski_zaljev_2000_s02 IOF_benthos_Plominski_zaljev_2000_stat2 sample Data from Environmental impact assessments in the eastern part of Adriatic sea - species list of benthic invertebrates and phytobenthos (2000-2010). We can see that each record has a similar eventID structure, except for the last part which indicates the event type - documented in the eventRemarks column. In this dataset, records with the eventID IOF_benthos_Plominski_zaljev_2000_crs has information applicable for records with eventIDs ending with _stat1, _stat2, _s01, and _s02 because _crs is their parent event. Similarly, information (e.g., date of station visit, coordinates) documented in records with eventID IOF_benthos_Plominski_zaljev_2000_stat1 is applicable for the two sample records (eventID _s01 and _s02), because these samples were taken at Station 1 (indicated by the parentEventID). These eventIDs could have been nested in another way, such as IOF_benthos_Plominsku_zaljev_2000_crs:stat1:s01 which would embed the parentEventID into the identifier. See also De Pooter et al. 2017 for an example of an event hierarchy in a complex benthos dataset. Watch this video for a demonstration on how to construct eventIDs: 4.5.2 occurrenceID occurrenceID is an identifier for occurrence records. Each occurrence record should have a globally unique identifier. Because occurrenceID is a required term, you may have to construct a persistent and globally unique identifier for each of your data records if none already exists (e.g., if records were not labeled with unique identifiers before, such as during sample processing or image/sensor detection). There are no standardized guidelines yet on designing the persistence of this ID, the level of uniqueness (from within a dataset to globally in OBIS), and the precise algorithm and format for generating the ID. But in the absence of a persistent globally unique identifier, one can be constructed by combining the institutionCode, the collectionCode and the catalogNumber (or autonumber in the absence of a catalogNumber). This is similar to how eventID is constructed. You may also follow Life Science Identifiers guidelines. Note that the inclusion of occurrenceID is also necessary for datasets in the OBIS-ENV-DATA format. An important consideration for museum specimens: there is the possibility that the institution a specimen is housed at may change. Therefore you may consider omitting institution identifiers within an occurrenceID, because occurrenceID should not change over time. See the example below: modified institutionCode collectionCode 2017-02-27 15:47:31 Ugent Vegetation_Gazi_Bay(Kenya)1987 2017-02-27 15:47:31 Ugent Vegetation_Gazi_Bay(Kenya)1987 2017-02-27 15:47:31 Ugent Vegetation_Gazi_Bay(Kenya)1987 basisOfRecord occurrenceID catalogNumber HumanObservation Ugent_Vegetation_Gazi_Bay(Kenya)1987_7553 Ugent_Vegetation_Gazi_Bay(Kenya)1987_7553 HumanObservation Ugent_Vegetation_Gazi_Bay(Kenya)1987_7554 Ugent_Vegetation_Gazi_Bay(Kenya)1987_7554 HumanObservation Ugent_Vegetation_Gazi_Bay(Kenya)1987_7555 Ugent_Vegetation_Gazi_Bay(Kenya)1987_7555 Data from Algal community on the pneumatophores of mangrove trees of Gazi Bay in July and August 1987. "],["checklist.html", "5 Formatting data tables 5.1 Darwin Core Term Checklist for OBIS", " 5 Formatting data tables 5.1 Darwin Core Term Checklist for OBIS There are many Darwin Core terms listed in the TDWG quick reference guide. However, not all these terms are necessary for publishing data to OBIS. For your convenience, we have created a checklist of all the Darwin Core terms relevant for OBIS data providers. You can reference this list to quickly see which terms are required by OBIS, which data table (Event, Occurrence, eMoF, DNA) they can be found in, and which Darwin Core class each term belongs to. These terms correlate with the IPT vocabulary mapping you will do when it comes time to publish your dataset. You may notice some terms are accepted in multiple data tables (e.g., Event and Occurrence) - this is because it depends on your dataset structure. If you have an Event Core, you will include some terms that would not be included if you had Occurrence Core. For guidance on specific class terms (e.g., location, taxonomy, etc.), see the Darwin Core section of the manual. Note that when you publish your dataset on the IPT, if you use a term not listed below it will be an unmapped field and will not be published alongside your data. You may still wish to include such fields in your dataset if you are publishing to other repositories, just know that they will not be included in your OBIS dataset. You may include this information either by putting it in the dynamicProperties field in JSON format, or putting the information into the eMoF. Alternatively, you may have fields that you do not wish to be published and that do not correspond to one of these terms (e.g. personal notes). This is okay - if they are not mapped to one of the terms, that column in your dataset will not be published. Table Legend: Terms marked with ^ indicate that the specified term is accepted in the Event or Occurrence table, but preference is that they be recorded in the eMoF table to link with controlled vocabulary For the Occurrence table, distinction is made if the term could be used in the core or the extension table x/x = term can be used in either Occurrence core or extension x/- term is recommended only for the Occurrence core -/x term is recommended only for the Occurrence extension Click here to download the checklist. Term OBIS Required Term’s DarwinCore Class Event Table Occurrence Table (Core/Extension) eMoF Table DNA Table eventDate required event x x/- eventID required event x -/x x decimalLatitude required location x x/- decimalLongitude required location x x/- occurrenceID required occurrence x/x x x occurrenceStatus required occurrence x/x basisOfRecord required record x/x scientificName required taxon x/x scientificNameID strongly recommended taxon x/x DNA_sequence strongly recommended dna x env_broad_scale strongly recommended dna x env_local scale recommended dna x env_medium strongly recommended dna x lib_layout recommended dna x nucl_acid_amp recommended dna x nucl_acid_ext recommended dna x otu_class_appr recommended dna x otu_db recommended dna x otu_seq_comp_appr recommended dna x pcr_primer_forward strongly recommended dna x pcr_primer_name_forward strongly recommended dna x pcr_primer_name_reverse strongly recommended dna x pcr_primer_reference strongly recommended dna x pcr_primer_reverse strongly recommended dna x samp_name recommended dna x samp_vol_we_dna_ext recommended dna x seq_meth recommended dna x sop recommended dna x target_gene strongly recommended dna x target_subfragment strongly recommended dna x day recommended event x x/- endDayOfYear recommended event x x/- eventType strongly recommended event x eventRemarks optional event x x/- eventTime recommended event x x/- fieldNotes optional event x fieldNumber optional event x habitat recommended event x x month strongly recommended event x x/- parentEventID required (if exists) event x -/x sampleSizeUnit strongly recommended event x^ x^/- x sampleSizeValue strongly recommended event x^ x^/- x samplingEffort strongly recommended event x^ x^/- x samplingProtocol strongly recommended event x^ x^/- x startDayOfYear recommended event x verbatimEventDate recommended event x year strongly recommended event x x/- bed optional geologicalContext x x/x earliestAgeOrLowestStage optional geologicalContext x x/x earliestEonOrLowestEonothem optional geologicalContext x x/x earliestEpochOrLowestSeries optional geologicalContext x x/x earliestEraOrLowestErathem optional geologicalContext x x/x earliestPeriodOrLowestSystem optional geologicalContext x x/x formation optional geologicalContext x x/x group optional geologicalContext x x/x highestBiostratigraphicZone optional geologicalContext x x/x latestAgeOrHighestStage optional geologicalContext x x/x latestEonOrHighestEonothem optional geologicalContext x x/x latestEpochOrHighestSeries optional geologicalContext x x/x latestEraOrHighestErathem optional geologicalContext x x/x latestPeriodOrHighestSystem optional geologicalContext x x/x lithostratigraphicTerms optional geologicalContext x x/x lowestBiostratigraphicZone optional geologicalContext x x/x member optional geologicalContext x x/x dateIdentified optional identification x/x identificationID optional identification x/x identificationQualifier recommended identification x/x identificationReferences optional (required for imaging data) identification x/x identificationRemarks recommended identification x/x identificationVerificationStatus optional (required for imaging data) identification x/x identifiedBy optional (required for imaging data) identification x/x identifiedByID optional identification x/x typeStatus optional identification x/x continent strongly recommended location x x/- coordinatePrecision strongly recommended location x x/- coordinateUncertaintyInMeters strongly recommended location x x/- country recommended location x x/- countryCode optional location x x/- county optional location x x/- footprintSpatialFit optional location x x/- footprintSRS optional location x x/- footprintWKT recommended location x x/- geodeticDatum recommended location x x/- georeferencedBy optional location x x/- georeferencedDate optional location x x/- georeferenceProtocol optional location x x/- georeferenceSources optional location x x/- higherGeography optional location x x/- higherGeographyID optional location x x/- island optional location x x/- islandGroup optional location x x/- locality recommended location x x/- locationAccordingTo recommended location x x/- locationID strongly recommended location x x/- locationRemarks recommended location x x/- maximumDepthInMeters strongly recommended location x x/- maximumDistanceAboveSurfaceInMeters optional location x x/- maximumElevationInMeters optional location x x/- minimumDepthInMeters strongly recommended location x x/- minimumDistanceAboveSurfaceInMeters optional location x x/- minimumElevationInMeters optional location x x/- municipality optional location x x/- pointRadiusSpatialFit optional location x x/- stateProvince optional location x x/- verbatimCoordinates optional location x x/- verbatimCoordinateSystem optional location x x/- verbatimDepth optional location x x/- verbatimElevation optional location x x/- verbatimLatitude optional location x x/- verbatimLocality optional location x x/- verbatimLongitude optional location x x/- verbatimSRS optional location x x/- waterBody recommended location x x/- materialSampleID recommended materialSample x/x measurementAccuracy recommended measurementOrFact x measurementDeterminedBy optional measurementOrFact x measurementDeterminedDate optional measurementOrFact x measurementID recommended measurementOrFact x measurementMethod recommended measurementOrFact x measurementRemarks recommended measurementOrFact x measurementType strongly recommended measurementOrFact x measurementTypeID strongly recommended measurementOrFact x measurementUnit strongly recommended measurementOrFact x measurementUnitID strongly recommended measurementOrFact x measurementValue strongly recommended measurementOrFact x measurementValueID strongly recommended measurementOrFact x associatedMedia recommended occurrence x/x associatedReferences optional occurrence x/x associatedSequences recommended occurrence x/x associatedTaxa optional occurrence x/x behavior optional occurrence x/x x catalogNumber recommended occurrence x/x disposition optional occurrence x/x establishmentMeans optional occurrence x/x georeferenceVerificationStatus recommended occurrence x x/- organismQuantity (preferred over individualCount) strongly recommended occurrence x/x x organismQuantityType (preferred over individualCount) strongly recommended occurrence x/x x individualCount recommended occurrence x/x x lifeStage optional occurrence x/x x occurrenceRemarks recommended occurrence x/x otherCatalogNumbers optional occurrence x/x preparations optional occurrence x/x recordedBy recommended occurrence x/x recordedByID recommended occurrence x/x recordNumber recommended occurrence x/x reproductiveCondition optional occurrence x/x x sex optional occurrence x/x x associatedOccurrences optional organsim x/x associatedOrganisms optional organsim x/x organismID recommended organsim x/x organismName optional organsim x/x organismRemarks optional organsim x/x organismScope optional organsim x/x previousIdentifications recommended organsim x/x accessRights recommended record x x/- bibliographicCitation recommended record x x/- collectionCode optional record x x/x collectionID optional record x x/x dataGeneralizations optional record x x/x datasetID recommended record x x/x datasetName recommended record x x/x dynamicProperties recommended record x x/x informationWithheld optional record x x/x institutionCode optional record x x/x institutionID optional record x x/x language recommended record x x/x license strongly recommended record x x/x modified recommended record x x/x ownerInstitutionCode optional record x x/x references recommended record x x/x rightsHolder recommended record x x/x type strongly recommended record x x/x x acceptedNameUsage recommended taxon x/x acceptedNameUsageID recommended taxon x/x higherClassification recommended taxon x/x infraspecificEpithet recommended taxon x/x nameAccordingToID recommended taxon x/x namePublishedInID optional taxon x/x namePublishedInYear optional taxon x/x nomenclaturalCode optional taxon x/x nomenclaturalStatus optional taxon x/x verbatimIdentification recommended identification x/x originalNameUsage recommended taxon x/x originalNameUsageID recommended taxon x/x parentNameUsage recommended taxon x/x parentNameUsageID recommended taxon x/x phylum recommended taxon x/x scientificNameAuthorship recommended taxon x/x specificEpithet recommended taxon x/x subgenus recommended taxon x/x taxonConceptID optional taxon x/x taxonID optional taxon x/x taxonomicStatus optional taxon x/x taxonRank strongly recommended taxon x/x taxonRemarks recommended taxon x/x verbatimTaxonRank recommended taxon x/x vernacularName recommended taxon x/x class recommended taxon x/x family recommended taxon x/x genus strongly recommended taxon x/x kingdom strongly recommended taxon x/x order strongly recommended taxon x/x "],["name_matching.html", "5.2 Name matching for taxonomic quality control", " 5.2 Name matching for taxonomic quality control OBIS requires all your specimens to be classified and matched against an authoritative taxonomic register. This effectively attaches unique stable identifiers (and digitally traceable) to each of your species. Meaning, if a taxonomic ranking or a species name changes in the future, there will be no question as to which species your dataset is actually referring to. Matching to registers also helps to avoid misspelled or unused terms. OBIS currently accepts identifiers from three authoritative lists: World Register Marine Species (WoRMS) LSIDs Integrated Taxonomic Information System (ITIS) TSNs Barcode of Life Data Systems (BOLD) and NCBI identifiers The identifiers (LSID, TSN, ID) from these registers will be used to populate the scientificNameID field. OBIS can accept other LSIDS besides WoRMS, as long as they are mapped in WoRMS. If you would like to include multiple identifiers, please use a concatenated list where each register is clearly identified (e.g. urn:lsid:itis.gov:itis_tsn:12345, NCBI:12345, BOLD:12345). Note You should prioritize using LSIDs because they are unique identifiers that indicate the authority the ID comes from.WoRMS LSIDs are also the taxonomic backbone that OBIS relies on, as it is built on marine systems and is linked to the other taxonomic authoritative lists. You can also use the Interim Register of Marine and Nonmarine Genera (IRMNG) to distinguish marine genera from freshwater genera. Taxon Matching Workflow The OBIS node managers have agreed to match all the scientific names in their datasets according to the following Name Matching workflow: Workflow for matching a list of taxon names to WoRMS 5.2.0.1 Step 1: Match with WoRMS The procedure for matching to WoRMS and then attaching successful matches back to your data can be simplified to: Prepare a file (.csv, .txt, .xlsx, etc.) with the list of your specimens/taxa Upload the file to WoRMS taxon match tool Check relevant boxes Review returned file Resolve any ambiguous matches Download file and identify data to include in your Occurrence data table for OBIS LSIDs, taxonomic fields, etc. Attach LSIDs back to your data using e.g.: R (merge) Excel (vlookup) The taxon match tool of the World Register of Marine Species (WoRMS) is an automatic way to download the taxonomic information about your occurrence records, without having to look for each name in the site. It is available at http://www.marinespecies.org/aphia.php?p=match. The WoRMS taxon match will compare your taxon list to the taxa available in WoRMS. The following video demonstrates the basic steps for using the WoRMS Taxon match. The taxon match takes into account exact matches and fuzzy matches. Fuzzy matches include possible spelling variations of a name available in WoRMS. WoRMS also identifies ambiguous matches, indicating that several potential matching options are available (e.g. homonyms). You can check these ambiguous matches and select the correct one, based on e.g., the general group information (a sponge dataset) or the authority. If this would be impossible with the available information (e.g., missing authority or very diverse dataset), then you need to contact the data provider for clarification. Watch the video below for a demonstration on how to resolve ambiguous or fuzzy matches. For performance reasons, the limit is set to 1,500 rows for the taxon match tool. Larger files can be sent to info@marinespecies.org and will be returned as quickly as possible. In case you have recorded a taxon that is not registered in WoRMS (e.g., newly discovered species), you should contact them so the database can be updated. After matching, the tool will return you a file with the AphiaIDs, LSIDs, valid names, authorities, classification, and any other output you have selected. Note The WoRMS LSID is used for DwC:scientificNameID. A complete online manual is available at http://www.marinespecies.org/tutorial/taxonmatch.php. You can attach IDs obtained from WoRMS back to your own data using Excel’s vlookup function. R script to do this is shown below. R script for attaching Taxon Lists to ID Lists: If you are familiar enough with R, you can use the merge function to attach the two lists to your data. We provide a short example of how to use this function below. #Generate example data table with species occurences, for this example we will only have one column with the scientificName data<-data.frame(scientificName=c("Thunnus thynnus", "Rhincodon typus", "Luidia maculata","Ginglymostoma cirratum")) # this would be your matched file from WoRMS, but for example we are generating a simple list with the species names and LSIDs lsids<- data.frame(scientificName=c("Ginglymostoma cirratum","Luidia maculata","Thunnus thynnus", "Rhincodon typus"), LSID = c("urn:lsid:marinespecies.org:taxname:105846", "urn:lsid:marinespecies.org:taxname:213112","urn:lsid:marinespecies.org:taxname:127029","urn:lsid:marinespecies.org:taxname:105847")) #merge data frames together matched_data<-merge(data, lsids, by = "scientificName") matched_data 5.2.0.2 How to fetch a full classification for a list of species from WoRMS? When setting up your WoRMS taxon match, to obtain the full classification for your list of species, simply check the box labeled “Classification”. This will add classification output in addition to the requested identifiers to your taxon match file, including Kingdom, Phylum, Class, Order, Family, Genus, Subgenus, Species, and Subspecies. WoRMS classification box 5.2.0.3 What to do with non-matching names? If your scientificName does not find an exact match to the WoRMS database, you may get an ambiguous match. According to WoRMS guidelines, ambiguous matches can be marked as one of the following: phonetic near_1 near_2 near_3 match_quarantine match_deleted See https://www.marinespecies.org/tutorial_taxonmatch.php for definitions of each of these terms. In each of these cases, WoRMS will try to suggest a species to match your uncertain taxon. Take care to ensure the correct species name is selected given the information in the original documentation. This is especially true for near_2 or near_3 matches. When checking a potential matched name, we recommend referencing the authority and higher taxonomic levels of a given suggestion when possible. For example, if you know the ambiguous species is a sponge, but one of the suggestions is for a mammal, you know that is not the correct name. Sometimes the species name as is it was originally documented is a name that is not accepted in WoRMS (i.e. Status = unaccepted). In these cases scientificNameID should reflect the name in the scientificName field, even if this name is not accepted. If you or the data provider can confirm with certainty that the name in scientificName can be changed to an accepted name, then verbatimIdentification must be populated with the scientific name as it was originally documented. In general, it is good practice to populate verbatimIdentification with the original taxon identification. Example of choices from an abiguous match In cases where no match can be found, WoRMS will indicate none. For these cases you should follow these steps: Ensure the name was entered correctly and any other information (e.g., authority, year, identification qualifiers) are included in separate columns, not the same cell as the name, as this can sometimes prevent matching Match with LifeWatch or another register (see Step 2 below) Check that the species is marine If a scientific name does not appear in any register, you should contact the original data provider, where possible, to confirm taxonomic spelling, authority, and obtain any original description documents, then attempt to match again. If even after this there are no matches, please contact the WoRMS data management team at info@marinespecies.org to see if the taxon should be added to the WoRMS register. 5.2.0.4 Step 2: Check name in other registers If you do not find a match with WoRMS, you should next check other registers. The LifeWatch taxon match compares your taxon list to multiple taxonomic standards. Matching with multiple registers gives an indication of the correct spelling of a name, regardless of its environment. If a name would not appear in any of the registers, this could indicate a mistake in the scientific name and the name should go back to the provider for additional checking/verification. Contrary to the WoRMS taxon match, when several matching options are available, the LifeWatch taxon match only mentions “no exact match found, multiple possibilities” instead of listing the available options. If multiple options are available, these should be looked up and matched manually. Currently, this web service matches the scientific names with the following taxonomic registers: World Register of Marine Species – WoRMS Catalogue of Life – CoL Integrated Taxonomic Information System – ITIS Pan-European Species-directories Infrastructure – PESI Index Fungorum – IF International Plant Names Index – IPNI Global Names Index - GNI Paleobiology Database - PaleoDB 5.2.0.5 Step 3: Is taxon marine? The Interim Register of Marine and Non-marine Genera (IRMNG) matching services are available through http://www.irmng.org/, as well as through the LifeWatch taxon match. This service allows you to search for a genus (or other taxonomic rank when you uncheck the “genera” box) to check if it is known to be marine, brackish, freshwater, or terrestrial. You can find this information in the row labeled “Environment”. If the taxa is marine, you may have to contact the WoRMS data management team (info@marinespecies.org) to have the taxon added to the WoRMS register (note you may have to provide supporting information confirming taxonomic and marine status). R packages for taxon matching If you are familiar with R, you may use the obistools function match_taxa to conduct taxon matching for your dataset. There is also a WoRMS package called worrms that has a function called wm_records_taxamatch you can use to conduct taxon matching. The output will be the same as that from the WoRMS tool, so you should check ambiguous matches as described above, confirming with other registers as necessary. Taxon Match Tools Overview See the table below for a summary of the different tools available. Tool Advantage Disadvantage WoRMS taxon match Accessible online, Does not require coding knowledge Requires rematch information back to your data obistools::match_taxa Produces same output as WoRMS taxon match, Already in R so easier to merge back with data Requires knowledge of R or python worrms::wm_records_taxamatch Outputs all WoRMS matching information Outputs a tibble for each taxa name specified; Requires knowledge of R or python "],["format_occurrence.html", "5.3 How to format Occurrence tables", " 5.3 How to format Occurrence tables If your dataset structure is based on Occurrence core, or has an Occurrence extension (remember that all OBIS data have at least one occurrence record associated, regardless of what organization structure you have chosen), there are several terms that are required in your dataset by OBIS. These required data fields include the following eight terms: occurrenceID eventID (required for Occurrence extension, not required for Occurrence Core) occurrenceStatus basisOfRecord scientificName scientificNameID (strongly recommended) eventDate (not required for Occurrence extension, required for Occurrence Core) decimalLatitude (not required for Occurrence extension) decimalLongitude (not required for Occurrence extension) While these are the bare minimum, you should strongly consider adding other terms if you have the corresponding information/data in your dataset or documentation. Other terms you should consider adding are identified by their associated Darwin Core class below. See the term checklist for a more complete list of potential terms for Occurrence table. Class Occurrence| DwC: associatedMedia Class Occurrence| DwC: associatedReferences Class Occurrence| DwC: associatedSequences Class Occurrence| DwC: associatedTaxa Class Occurrence| DwC: preparations Class Occurrence| DwC: recordedBy Class Occurrence| DwC: materialSample Class Occurrence| DwC: materialSampleID Class Record | DwC: bibliographicCitation Class Record | DwC: catalogNumber Class Record | DwC: collectionCode Class Record | DwC: collectionID Class Record | DwC: dataGeneralizations Class Record | DwC: datasetName Class Record | DwC: institutionCode Class Record | DwC: modified Class Taxon | DwC: kingdom Class Taxon | DwC: scientificNameAuthorship Class Taxon | DwC: taxonRank Class Taxon | DwC: taxonRemarks Note that any terms related to measurements, either biotic (e.g., sex, lifestage, biomass) or abiotic will also be included in the extendedMeasurementOrFact table. Measurements can remain in the Occurrence table as long as they can be mapped to the appropriate DwC term (e.g. DwC:Occurrence:sex). Nnot every data aggregator outside of OBIS indexes the eMoF table, so otherwise this information may be lost. 5.3.0.1 Stepwise Guidance to Format an Occurrence Table (with spreadsheets) Before proceeding with formatting the Occurrence table, be sure you have completed taxon matching to obtain WoRMS LSIDs for the scientificNameID field. Identify columns in your raw data that match with Occurrence fields Include columns with measurements for now, but they will be moved to an eMoF table(s) Copy these columns to a new sheet named Occurrence (note it is good practice to never make changes to your original datasheet) Create and add occurrenceIDs for each unique occurrence record Add and fill basisOfRecord and occurrenceStatus fields Ensure your column names map to Darwin Core terms scientificName + scientificNameID Watch our video tutorial for a demonstration of this procedure: After formatting your Occurrence Core or Extension table, you can format your extendedMeasurementOrFact table. "],["format_event.html", "5.4 How to format Event tables", " 5.4 How to format Event tables If your dataset uses an Event Core structure, data fields included in your dataset should include the following required terms: eventDate eventID parentEventID (if applicable) decimalLatitude decimalLongitude Other terms you should consider adding are grouped by their associated Darwin Core class in the table below. See the term checklist for a more complete list of DwC terms for the Event table. Class Event | DwC:parentEventID Class Event | DwC:eventType Class Event | DwC:eventRemarks Class Event | DwC:year Class Event | DwC:month Class Event | DwC:day Class Location | DwC:country Class Location | DwC:island Class Location | DwC:coordinateUncertaintyInMeters Class Location | DwC:countryCode Class Location | DwC:footprintWKT Class Location | DwC:geodeticDatum Class Location | DwC:islandGroup Class Location | DwC:locality Class Location | DwC:locationAccordingTo Class Location | DwC:locationID Class Location | DwC:locationRemarks Class Location | DwC:maximumDepthInMeters Class Location | DwC:minimumDepthInMeters Class Location | DwC:stateProvince Class Location | DwC:verbatimCoordinates Class Location | DwC:verbatimDepth Class Location | DwC:waterBody Terms related to measurements, either biotic (e.g., sex, lifestage) or abiotic will be included in extendedMeasurementOrFact table not the Event Core or Occurrence extension table. 5.4.0.1 Stepwise Guidance to Format Event Table (with spreadsheets) Before proceeding with the below, make sure each record already has an eventID. Add and fill the parentEventID, eventType, and eventRemarks fields as applicable Identify the hierarchical event structure in your data, if present, and create new records for parent events, filling in any relevant fields Identify all columns in your data that will match with Darwin Core Event fields Include any relevant abiotic measurements (ENV-DATA) related to sampling events (e.g. sampling protocols). We will add these to the eMoF table later Copy these columns to a new sheet and name it Event Delete duplicate data so only unique events are left Ensure dates and time are formatted according to ISO 8601 standards in the eventDate field Add any other relevant fields as indicated above Map fields to Darwin Core Watch the video tutorial of this process below. After completing the formatting of your Event Core table, you can next format your Occurrence and extendedMeasurementOrFact tables. To format the Occurrence extension table, see the Occurrence table section of this manual. 5.4.1 Populating parent and child events In OBIS, child events will inherit information from the parent event when the field is left empty. For example, if you provided the information “1x1m quadrat intertidal sampling” to samplingProtocol for a parent event but nothing was provided to the child event, as below: eventID parentEventID samplingProtocol site123 1x1m quadrat intertidal sampling site123_quad1 site123 the child event will automatically be populated with the same text “1x1m quadrat intertidal sampling” once published to OBIS. eventID parentEventID samplingProtocol site123 1x1m quadrat intertidal sampling site123_quad1 site123 1x1m quadrat intertidal sampling This can be convenient so that you do not have to fill in the same information repeatedly. However, it is important to understand that GBIF does not currently implement inheritance! In OBIS, child events inherit information from parent events as described above. However, if the parentEvent contains data such as latitude/longitude coordinates, and child events do not, occurrences associated with the child events will have blank latitude/longitude coordinates when data is accessed from GBIF. This is again because child event fields are not currently inherited from parent events in GBIF. If you plan for your dataset to be additionally accessed through GBIF, it is recommended to populate information in both parent and child events. A discussion of this and the implications can be found here. Therefore, in cases where the data will be accessible by both OBIS and GBIF, we recommend populating important information (e.g. decimalLongitude, decimalLatitude) in the child events to ensure the data will be useful for both user bases. "],["format_emof.html", "5.5 How to format extendedMeasurementOrFact tables", " 5.5 How to format extendedMeasurementOrFact tables 5.5.0.1 What data goes into eMoF Any data related to abiotic or biotic measurements, including sampling information and protocols can be included in the eMoF table. Measurement data can also go into the MeasurementOrFact extension, however OBIS recommends using the extendedMeasurementOrFact instead, particularly if your data is based on an Event core table. Required terms for eMoF include: eventID (this links the record to the Event Core table) occurrenceID (this links the record to the Occurrence Core or Occurrence Extension table) Other potential fields are shown in the table below (also listed in the checklist): Class Event | DwC:habitat Class Event | DwC:sampleSizeUnit Class Event | DwC:sampleSizeValue Class Event | DwC:samplingEffort Class Event | DwC:samplingProtocol Class Occurrence | DwC:behavior Class Occurrence | DwC:individualCount Class Occurrence | DwC:lifeStage Class Occurrence | DwC:organismQuantity Class Occurrence | DwC:organismQuantityType Class Occurrence | DwC:sex Class Occurrence | DwC:type Class Measurement | DwC:measurementAccuracy Class Measurement | DwC:measurementDeterminedBy Class Measurement | DwC:measurementDeterminedDate Class Measurement | DwC:measurementID Class Measurement | DwC:measurementMethod Class Measurement | DwC:measurementRemarks Class Measurement | DwC:measurementType* Class Measurement | DwC:measurementTypeID* Class Measurement | DwC:measurementUnit* Class Measurement | DwC:measurementUnitID* Class Measurement | DwC:measurementValue Class Measurement | DwC:measurementValueID *For measurementTypeID, measurementUnitID, and measurementValueID you must use controlled vocabulary terms. We know choosing the correct vocabulary term can be challenging, so we have provided some guidance on how to select the correct vocabulary and are continuously working to improve guidelines. It is strongly recommended to ensure these fields are filled as correctly as possible. Missing or incorrect terms will be documented in the measurementOrFact reports. 5.5.0.2 How to structure eMoF Structuring data for the eMoF extension may be one of the more confusing extensions in the data formatting process. It may help to think of this extension as the table that contains all information related to any kind of measurement. Rather than documenting each of your measurements in separate columns (e.g., columns for biomass, abundance, length, gear size, percent cover, etc.), these measurements will be condensed into one column: measurementValue. measurementType describes what the measurement actually is, for example whether it is an abundance value, length, percent cover, or any other biotic/abiotic measurement. measurementUnit is used to indicate the unit of the measurement. By linking measurementType and measurementValue with the identifiers eventID and/or occurrenceID, you can have measurements linked to one event (e.g. temperature), measurements link to occurrence records (e.g. length), as well as sampling facts that are linked to events (size, gear, etc.). Information specifically related to how samples were taken will have the measurementTypes: sampleSizeValue, sampleSizeUnit, samplingEffort, and samplingProtocol. 5.5.0.3 Stepwise Guidance to Format eMoF Table (in Excel) Create a blank sheet and name it eMoF Add 9 column headers for: eventID, occurrenceID, measurementType, measurementValue, measurementUnit, measurementTypeID, measurementValueID, measurementUnitID, measurementRemarks Copy eventID values from your Occurrence table and paste into the eventID field in your new, blank eMoF table Repeat for occurrenceID from the Occurrence table Copy the first column of measurement values, paste into the measurementValue field Fill measurementType with the name of the variable (e.g., count, length, etc.) Add unit of measurements where applicable to the measurementUnit field For any other measurements related to occurrences, repeat steps 3-5, pasting additional measurements below the preceding ones Be sure to copy and paste the associated occurrenceIDs and/or eventIDs for the additional measurements Fill the fields measurementTypeID, measurementUnitID, and measurementValueID with controlled vocabularies that suit your data (see vocabulary guidelines) Repeat steps 3-7 for any measurements in the Event table Note the fields sampleSizeValue, samplingEffort, and samplingProtocol from the Occurrence table can be documented as separate measurements on different rows in the eMoF table. E.g., measurementType = samplingProtocol, measurementValue = description of protocol. Any values in sampleSizeUnit fields should be placed in the measurementUnit field when transferred to the eMoF. Watch the video tutorial for how to format the eMoF table below. If you would like to export Event data to the eMoF, see some example R code below. This example was provided by Abby Benson from the OBIS-USA node. library(dplyr) cruise <- unique(eventCore[c("eventID")]) #create a list of all unique eventIDs from your event table cruise <- cruise %>% #add sampling information mutate(measurementType = "Sampling platform name", measurementValue = "R/V Cruise Id = SR1812", measurementValueID = "https://doi.org/10.7284/908021", measurementUnit = "", measurementTypeID = "http://vocab.nerc.ac.uk/collection/Q01/current/Q0100001/", measurementUnitID = "", occurrenceID = "") "],["dna_data.html", "5.6 DNA derived data", " 5.6 DNA derived data Note: OBIS guidelines have not yet been updated to reflect changes that allow DNA Derived data to be linked to Event core tables. We will update this page soon. Contents: Introduction How to find genetic data in OBIS Guidelines for compiling eDNA and metabarcoding data eDNA & DNA Derived use cases 16S rRNA metabarcoding example Unknown sequences Guidelines for compiling qPCR data Introduction to DNA data DNA derived data are increasingly being used to document taxon occurrences. This genetic data may come from a sampling event, an individual organism, may be linked to physical material (or not), or may result from DNA detection methods e.g., metabarcoding or qPCR. Thus genetic data may reflect a single organism, or may include information from bulk samples with many individuals. Still, DNA-derived occurrence data of species should be documented as standardized and as reproducible as possible. To ensure DNA data are useful to the broadest possible community, a community guide entitled Publishing DNA-derived data through biodiversity data platforms was published by GBIF, OBIS, and others. This guide is supported by the DNA derived data extension for Darwin Core, which incorporates MIxS terms into the Darwin Core standard. There are 5 categories for which genetic data could fall into: DNA-derived occurrences Enriched occurrences Targeted species detection Name references Metadata only For a guide and decision tree on determining which category your data falls into, see the Data packaging and mapping section of the GBIF guide. Refer to the examples below for use case examples of eDNA and DNA derived data (Category 1). Currently, genetic data must be published with Occurrence core, not Event core. eDNA and DNA derived data are then linked to the Occurrence core data table with the use of occurrenceID and/or eventID. See below for further guidance on compiling genetic data. A new data model is being developed by GBIF and the OBIS community that may change this, however as it is not implemented yet, we focus on the current Darwin Core recommendations here. To format datasets, you will need to have information on the sequence and possible taxonomy for each occurrence record associated with a DNA sample. Genetic data is often recorded in multiple different files, and this might be the type of format received from data providers. Important data tables can include: an OTU-table, a taxonomy table, a sample information table, and a .fasta file with sequences. The OTU-table is a sequence by sample table, which records the quantity of each unique sequence found in each sample. Sequences are usually referred to by an ID, which is unique only in the dataset (e.g. asv1, asv2, asv3 …). The taxonomy table is a sequence by taxonomy table, which records the taxonomy linked to each unique sequence, as defined by the annotation method. The sample information table records the metadata of each sample (e.g. location, time, and collection method). Finally the .fasta file records the actual DNA sequence that is linked to each sequence id. Although this data is in multiple files, each unique sequence by sample combination is considered one occurrence. Therefore the data from these tables will need to be formatted to the “long format”, including a row for each sequence in each sample. See the figure below for a demonstration of how this can be done. Combining multiple DNA data tables into one “long format” table Doing this will help you when following the guidelines below. How to find genetic data in OBIS To find genetic data in OBIS we recommend using the R package robis, using the occurrence function. You must set extensions and/or hasextensions to “DNADerivedData” to ensure extension records are included in the results. hasextensions will exclude any occurrence that does not have the specified extension, in our case, DNADerivedData. The extensions parameter specifies which extensions to include. To obtain the DNA data, you have to extract the information from the extension using the unnest_extension() function. You can specify as many fields from the Occurrence table to be included, and pass them to the fields parameter. See the code below for an example. See also this vignette for a more detailed example, including how you can work further with these sequences in R. dna_occ<-occurrence("Dinophyceae",hasextensions="DNADerivedData", extensions="DNADerivedData") dnaseqs <-unnest_extension(dna_datasets, "DNADerivedData", fields = c("id", "phylum", "class", "family", "genus", "species")) dnaseqs["DNA_sequence"] # A tibble: 706 × 1 DNA_sequence <chr> 1 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 2 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 3 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG… 4 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 5 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 6 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG… 7 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 8 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 9 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG… 10 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… # … with 696 more rows Additionally, a prototype Sequence Search tool is in development that allows you to search for sequences or related sequences in OBIS. Note that the tool is not always up to date so use caution until the prototype is fully developed. To use the tool: Copy your sequence in the provided box (an example sequence is provided for testing) Press the Search button View results below You can also change the Minimum Alignment Score slider in the map view to see location of sequences The search result will show you taxonomic information for species sequences that align to your sequence, the alignment score, and a link to the respective datasets. OBIS Mapper You can use the OBIS Mapper to obtain records that include the DNADerivedData extension by addding a filter for the extension when using the tool. To do this: From the OBIS Mapper, navigate to the Criteria tab (the plus (+) sign) Open the Extensions dropdown section Check the box for DNADerivedData Add any other filters you want, e.g. taxonomic, then click save to create the layer Switch to the Layers tab Download the data from the layer by clicking the green button (see Data Access for more on using the OBIS Mapper) 5.6.1 Compiling eDNA and metabarcoding datasets As mentioned above, you will need to have information on the taxonomy and sequences for each occurrence record associated with a DNA sample. You should first fill in the Occurrence core table, and then complete the DNA Derived Data extension (as well as the eMoF extension, if applicable, for any measurements taken). Occurrence core table In addition to the usual required terms for Occurrence datasets, you should consider the following additional terms: Class Occurrence | DwC: organismQuantity Class Occurrence | DwC: OrganismQuantityType Class Occurrence| DwC: associatedSequences Class Event | DwC: sampleSizeValue Class Event | DwC: sampleSizeUnit Class Event | DwC: samplingProtocol Class Identification | DwC: identificationRemarks Class Identification | DwC: identificationReferences Class Identification | DwC: verbatimIdentification Class Taxon | DwC: taxonConceptID Class Material Sample | DwC:materialSampleID For organismQuantity and sampleSizeValue in eDNA datasets, the quantities recorded with sequencing studies always represent relative abundance to the total reads in the sample, and cannot be directly compared across samples. This is due to the nature of the sample processing protocol and the amplification of DNA with PCR, which biases the original quantities. In organismQuantity, record the amount of a unique sequence in a specific sample (i.e. 33 reads). In sampleSizeValue, record the total number of all reads in that specific sample (i.e. 15310 reads). This information will allow people accessing the data to calculate the relative abundance of that sequence in the sample. The fields organismQuantityType, and sampleSizeUnit, should be populated with “DNA sequence reads”, as it is of high importance that sequence abundances are not confused with organism abundances recorded by traditional methods. The abundance information can usually be found in the “OTU-table”. associatedSequences should contain a reference to the URL domain where genetic sequence information associated with the Occurrence can be found, e.g. a link, identifier, or list (concatenated and separated) of identifiers. Can link to archived raw barcode reads and/or associated genome sequences, like a public repository. It is recommended that links contain the domain name (e.g. NCBI) in the URL, for example: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA887898/. The actual sequence of the occurrence will be documented in the DNA Derived Data extension. identificationRemarks should be used to record information on how the taxonomic information of the occurrence was reached against which reference database, and, if possible, with which confidence. For example “RDP annotation confidence: 0.96, against reference database: GTDB”. This information should be recorded in the bioinformatic protocol of the study. Note: this information will also be recorded in the DNA derived extension in the fields otu_seq_comp_appr and otu_db. identificationReferences should include a link to the bioinformatic pipeline or publication where the identification process is explained in detail. taxonConceptID should include the taxonomic ID of the sequence (non-Linnean). Often genetic sequences can be assigned an ID linked to a reference database that is not a linnean name. These taxonomic names or IDs from other taxonomic databases (like the NCBI taxonomic database) can be recorded in this field. For example: NCBI:txid9771. The name linked to this ID can then be recorded in the field verbatimIdentification. samplingProtocol can contain free-text that briefly describes the methods used to obtain the sample, or a link to a protocol that is recorded elsewhere. DNA Derived Data extension The DNADerivedData extension is meant to capture information related to the sampled DNA, including sampling, processing, and other bioinformatic methods. The following (free-text) terms are required or highly recommended for eDNA and metabarcoding datasets. Note that some terms will be different for qPCR data (see below) DNA Derived | DwC: DNA_sequence DNA Derived | DwC: sop DNA Derived | DwC: target_gene DNA Derived | DwC: target_subfragment DNA Derived | DwC: pcr_primer_forward DNA Derived | DwC: pcr_primer_reverse DNA Derived | DwC: pcr_primer_name_forward DNA Derived | DwC: pcr_primer_name_reverse DNA Derived | DwC: pcr_primer_reference DNA Derived | DwC: Pcr_cond DNA Derived | DwC: annealingTemp DNA Derived | DwC: annealinTempUnit DNA Derived | DwC: ampliconSize DNA Derived | DwC: env_broad_scale DNA Derived | DwC: env_local_scale DNA Derived | DwC: env_medium DNA Derived | DwC: lib_layout DNA Derived | DwC: seq_meth DNA Derived | DwC: otu_class_appr DNA Derived | DwC: otu_seq_comp_appr DNA Derived | DwC: otu_db For a complete list of terms you can map to, see the DwC DNA Derived Data extension page. See the examples below for use case examples. The Marine Biological Data Mobilization Workshop also has a tutorial for this type of data. DNA_sequence is the most important field, where the ASV/OTU sequence will be recorded. This field can then be searched with sequence alignment methods to, for example, find closely related sequences recorded in other studies, and will allow very powerful data comparison and analysis in the future. It will also make your sequence available in the OBIS sequence search tool. The remaining metadata fields will help the person accessing the data to filter data of interest (e.g. specific genetic region with target_gene, target_subfragment, or pcr_primer fields), link to the public sequence databases with the MixS specific fields (e.g. env_ fields), and evaluate the reliability of the sequence annotation method (e.g. otu_ fields). Environmental systems are described in the two fields env_broad_scale and env_local_scale and it is recommended to use Environment Ontology (ENVO)’s biome classes to describe the environmental system from which the sample was extracted. Like other identifiers, provide the exact ENVO reference identifier. env_broad_scale provides a coarse resolution for which environment your sample came from. Likely this will be marine biome (ENVO:00000447) for OBIS data. For local scale, identify the specific environment your sample was obtained from (e.g., coastal water, benthic zone, etc.). When data tables are formatted and you are ready to publish it on the IPT, it will follow the same process for publishing on an IPT. You will upload your source files, and add the Occurrence core Darwin Core mappings, and then the DNA Derived Data Darwin Core mappings. However the extension must first be installed by the IPT administrator (often the node manager). Once the extension is installed, you can add the Darwin Core DNA Derived Data mapping for that file. 5.6.1.1 eDNA and DNA derived data example The following example use cases draw on both the DNA-derived data guide and the DNA derived data extension to illustrate how to incorporate a DNA derived data extension file into a Darwin Core archive. Note: for the purposes of this section, only required Occurrence core terms are shown, in addition to all eDNA & DNA specific terms. For additional Occurrence core terms, refer to Occurrence. 5.6.1.1.1 eDNA data from Monterey Bay, California The data for this example is from the use case “18S Monterey Bay Time Series: an eDNA data set from Monterey Bay, California, including years 2006, 2013 - 2016’. The data from this study originate from marine filtered seawater samples that have undergone metabarcoding of the 18S V9 region. Occurrence core: We can populate the Occurrence core with all the required and highly recommended fields, as well as considering the eDNA and DNA specific fields. The Occurrence core contain the taxonomic identification of each ASV observed; its number of reads, as well as relevant metadata including the sample collection location, references for the identification procedure, and links to archived sequences. OccurrenceID and basisOfRecord are some of the required Occurrence core terms, in addition to the highly recommended fields of organismQuantity and organismQuantityType. A selection of samples from this plate were included in another publication (Djurhuus et al., 2020),which is recorded in identificationReferences along with the GitHub repository where the data can be found. occurrenceID basisOfRecord organismQuantity OrganismQuantityType associatedSequences 11216c01_12_edna_1_S_occ1 MaterialSample 19312 DNA sequence reads NCBI BioProject acc. nr. PRJNA433203 11216c01_12_edna_2_S_occ1 MaterialSample 16491 DNA sequence reads NCBI BioProject acc. nr. PRJNA433203 11216c01_12_edna_3_S_occ1 MaterialSample 21670 DNA sequence reads NCBI BioProject acc. nr. PRJNA433203 sampleSizeValue sampleSizeUnit identificationReferences identificationRemarks 147220 DNA sequence reads GitHub repository Djurhuus et al. 2020 unassigned, Genbank nr Release 221 September 20 2017 121419 DNA sequence reads GitHub repository Djurhuus et al. 2020 unassigned, Genbank nr Release 221 September 20 2017 161525 DNA sequence reads GitHub repository Djurhuus et al. 2020 unassigned, Genbank nr Release 221 September 20 2017 DNA Derived Data extension: Next, we can create the DNA Derived Data extension which will be connected to the Occurrence core with the use of occurrenceID. This extension contains the DNA sequences and relevant DNA metadata, including sequencing procedures, primers used and SOP’s. The recommended use of ENVO’s biome classes were applied to describe the environmental system from which the sample was extracted. The samples were collected by CTD rosette and filtered by a peristaltic pump system. Illumina MiSeq metabarcoding was applied for the target_gene 18S and the target_subfragment, V9 region. URL’s are provided for the protocols followed for nucleic acids extraction and amplification. For a detailed description of the steps taken to process the data, including algorithms used, see the original publication. Adding Operational Taxonomic Unit (OTU) related data are highly recommended and should be as complete as possible, for example: occurrenceID env-broad_scale env_local_scale env_medium 11216c01_12_edna_1_S_occ1 marine biome (ENVO:00000447) coastal water (ENVO:00001250) waterborne particulate matter (ENVO:01000436) 11216c01_12_edna_2_S_occ1 marine biome (ENVO:00000447) coastal water (ENVO:00001250) waterborne particulate matter (ENVO:01000436) 11216c01_12_edna_3_S_occ1 marine biome (ENVO:00000447) coastal water (ENVO:00001250) waterborne particulate matter (ENVO:01000436) samp_vol_we_dna_ext nucl_acid_ext nucl_acid_amp lib_layout target_gene 1000ml dx.doi.org/10.17504/protocols.io.xjufknw dx.doi.org/10.17504/protocols.io.n2vdge6 paired 18S 1000ml dx.doi.org/10.17504/protocols.io.xjufknw dx.doi.org/10.17504/protocols.io.n2vdge6 paired 18S 1000ml dx.doi.org/10.17504/protocols.io.xjufknw dx.doi.org/10.17504/protocols.io.n2vdge6 paired 18S target_subfragment seq_meth otu_class_appr otu_seq_comp_appr V9 Illumina MiSeq 2x250 dada2;1.14.0;ASV blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2% V9 Illumina MiSeq 2x250 dada2;1.14.0;ASV blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2% V9 Illumina MiSeq 2x250 dada2;1.14.0;ASV blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2% otu_db sop DNA_sequence Genbank nr;221 dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository GCTACTACCGATT… Genbank nr;221 dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository GCTACTACCGATT… Genbank nr;221 dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository GCTACTACCGATT… pcr_primer_forward pcr_primer_reverse pcr_primer_name_forward pcr_primer_name_reverse pcr_primer_reference GTACACACCGCCCGTC TGATCCTTCTGCAGGTTCACCTAC 1391f EukBr Amaral-Zettler et al. 2009 GTACACACCGCCCGTC TGATCCTTCTGCAGGTTCACCTAC 1391f EukBr Amaral-Zettler et al. 2009 GTACACACCGCCCGTC TGATCCTTCTGCAGGTTCACCTAC 1391f EukBr Amaral-Zettler et al. 2009 5.6.1.1.2 16S rRNA gene metabarcoding data of Pico to Mesoplankton DNA derived datasets can also include an extendedMeasurementsOrFact (eMoF) extension file, in addition to the Occurrence and DNA derived extensions. In this example, environmental measurements were provided in an eMoF file, in addition to the DNA derived data and occurrence data. Here we show how to incorporate such measurements in the extensions. In the publication “Diversity of Pico- to Mesoplankton along the 2000 km Salinity Gradient of the Baltic Sea”, a dataset with 16S rRNA gene metabarcoding data of surface water microbial communities was created from 21 off-shore stations, following a transect from Kattegat to the Gulf of Bothnia in the Baltic Sea. The full dataset entitled “Diversity of Pico- to Mesoplankton along the 2000 km Salinity Gradient of the Baltic Sea (Hu et al. 2016) is available from GBIF. Occurrence core: The Occurrence core contain information about the organisms in the sample including the taxonomy and quantity of organisms detected, the collection location, references for the identification procedure, and links to the sequences generated. Important note: even though this dataset uses OTU identifiers for taxonomy (therefore not including scientificNameID) OBIS still recommends using scientificNameID. basisOfRecord occurrenceID eventID eventDate MaterialSample SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730 SBDI-ASV-3:16S_1 2013-07-13 07:08:00 MaterialSample SBDI-ASV-3:16S_1:43e088977eba5732bfa45e20b1d8cdd2 SBDI-ASV-3:16S_1 2013-07-13 07:08:00 MaterialSample SBDI-ASV-3:16S_1:887bc7033b46d960e893caceb711700b SBDI-ASV-3:16S_1 2013-07-13 07:08:00 organismQuantity organismQuantityType sampleSizeValue sampleSizeUnit 2235 DNA sequence reads 12393 DNA sequence reads 795 DNA sequence reads 12393 DNA sequence reads 40 DNA sequence reads 12393 DNA sequence reads samplingProtocol associatedSequences identificationReferences identificationRemarks 200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679] [https://www.ebi.ac.uk/ena/browser/view/ERR1202034] [https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation] DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.5 200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679] [https://www.ebi.ac.uk/ena/browser/view/ERR1202034] [https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation] DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.56 200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679] [https://www.ebi.ac.uk/ena/browser/view/ERR1202034] [https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation] DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.99 decimalLatitude decimalLongitude taxonID scientificName 55.185 13.791 ASV:919a2aa9d306e4cf3fa9ca02a2aa5730 UBA6821 55.185 13.791 ASV:43e088977eba5732bfa45e20b1d8cdd2 Chthoniobacterales 55.185 13.791 ASV:887bc7033b46d960e893caceb711700b BACL27 sp014190055 kingdom phylum class order family genus Bacteria Verrucomicrobiota Verrucomicrobiae Chthoniobacterales UBA6821 UBA6821 Bacteria Verrucomicrobiota Verrucomicrobiae Chthoniobacterales NA NA Bacteria Actinobacteriota Acidimicrobiia Acidimicrobiales Ilumatobacteraceae BACL27 DNA Derived Data extension: The DNA Derived Data extension for metabarcoding data contains the DNA sequences and relevant DNA metadata, primers and procedures. This example table contains the highly recommended and recommended fields as populated with the example dataset data. For this dataset, authors additionally provided measurements of of water sample temperature and salinity, which are provided in an extendedMeasurementOrFact extension file: id env_broad_scale env_local_scale env_medium SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730 aquatic biome [ENVO_00002030] marine biome [ENVO_00000447] brackish water [ENVO_00002019] SBDI-ASV-3:16S_1:43e088977eba5732bfa45e20b1d8cdd2 aquatic biome [ENVO_00002030] marine biome [ENVO_00000447] brackish water [ENVO_00002019] SBDI-ASV-3:16S_1:887bc7033b46d960e893caceb711700b aquatic biome [ENVO_00002030] marine biome [ENVO_00000447] brackish water [ENVO_00002019] lib_layout target_gene target_subfragment seq_meth sop paired 16S rRNA V3-V4 Illumina MiSeq https://nf-co.re/ampliseq paired 16S rRNA V3-V4 Illumina MiSeq https://nf-co.re/ampliseq paired 16S rRNA V3-V4 Illumina MiSeq https://nf-co.re/ampliseq pcr_primer_forward pcr_primer_reverse pcr_primer_name_forward pcr_primer_name_reverse DNA_sequence CCTACGGGNGGCWGCAG GACTACHVGGGTATCTAATCC 341 805R TCGAGAATTTTTCACAATG… CCTACGGGNGGCWGCAG GACTACHVGGGTATCTAATCC 341 805R TCGAGAATTTTTCACAATG… CCTACGGGNGGCWGCAG GACTACHVGGGTATCTAATCC 341 805R TGGGGAATCTTGCGCAATG… extendedMeasurementOrFact (eMoF) extension: measurementID occurrenceID measurementType measurementValue measurementUnit SBDI-ASV-3:16S_1:temperature SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730 temperature 16.9 °C SBDI-ASV-3:16S_1:salinity SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730 salinity 7.25 psu SBDI-ASV-3:16S_1:temperature SBDI-ASV-3:16S_1:1ead98754d34073a4606f7ff1e94126e temperature 16.9 °C 5.6.2 Unknown sequences It is important to understand the significance of unknown and uncharacterized sequences in genetic studies. Sequences are given taxonomic names based on comparisons to a reference database. The reference databases contain sequences that have been submitted with a name. Ideally, the reference database is a collection of sequences that are derived from vouchered, morphologically identified specimens. Notably, this is frequently often not the case and sequences can also have erratic annotations. Furthermore, only a small portion of species have sequences in reference databases. Due to this reason, typically many sequences in any given study will remain uncharacterized. This is especially the case for tropical regions with high biodiversity. By also recording all sequences, including uncharacterized sequences, we make sure that the information is not lost, even if the annotation is currently incorrect or missing. These uncharacterized sequences can then still be compared to other studies, and can be given a taxonomic name as more specimens are sequenced and added to the reference databases. For unknown sequences it is required to populate the scientificName field with “Incertae sedis”, or the lowest taxonomic information if available. For example, if it is only known which Class a sequence belongs to, populate scientificName with the associated Class name. Similarly, scientificNameID should be populated with the WoRMS LSID for the name given to scientificName. For records recorded as Incertae sedis, scientificNameID should be populated with urn:lsid:marinespecies.org:taxname:12. We recommend also populating verbatimIdentification with the name that was originally documented (e.g. phototrophic eukaryote). 5.6.3 Compiling qPCR datasets Compiling qPCR data is a little bit different than compiling eDNA or metabarcoding data. One of the main differences is that there are no sequences recorded in the DNA_sequence field of the DNA derived data extension. Instead, occurrences are based on detections made using species-specific primers and either qPCR (Quantitative Polymerase Chain Reaction) or ddPCR (Droplet-Digital Polymerase Chain Reaction), no sequencing is done. It is very important to document the methods used for this type of data because the results can be sensitive to the specificity of the primers/assays used. Therefore documenting as much detail on the methodologies is important to ensure data interpretability. Occurrence core table In addition to the Occurrence core terms, it is strongly recommend to including the following terms for qPCR data: Class Occurrence | DwC:recordedBy Class Occurrence | DwC: organismQuantity Class Occurrence | DwC: organismQuantityType Class Event | DwC: sampleSizeValue Class Event | DwC: sampleSizeUnit Class Event | DwC: samplingProtocol Class Material Sample | DwC:materialSampleID For ddPCR, organismQuantity refers to the number of positive droplets/chambers in the sample, and organismQuantityType is the partition type (e.g., ddPCR droplets, dPCR chambers). sampleSizeValue will be populated with the number of accepted partitions, e.g. meaning accepted droplets in ddPCR or chambers in dPCR. sampleSizeUnit is the partition type, which should be the same as organismQuantityType. All four of these fields are particularly important to include for ddPCR data. For qPCR, these fields can be used for recording e.g. the number of copies that were calculated for the target gene in the sample. In this case organismQuantityType needs to contain the exact type of the measurement reported in the results. The field accepts any string, but the best practice would be to add a URI pointing to a vocabulary, as is done in the extendedMeasurementOrFact extension. The terms sampleSizeValue and sampleSizeUnit would not be used in this case. materialSampleID should contain an identifier for the MaterialSample (i.e. occurrence record), rather than a digital record of the material sample. If an ID was obtained from a nucleotide archive, use the associated biosample ID. Otherwise, construct a persistent unique identifier from a combination of elements in the data that will make the materialSampleID globally unique, similar to eventIDs and occurrenceIDs. recordedBy can be populated with the names of the people, groups, or organizations responsible for recording the original Occurrence. You can use a concatenated list for multiple names, by separating values with a vertical bar (’ | ’). DNA Derived Data extension For qPCR datasets, it is strongly recommended to document as much detail as possible in this extension, particularly details about the PCR primers used and the target gene. We recommend you include the following terms, where relevant. For term definitions see DNA derived data extension. Terms related to the sampling event: DNA Derived | DwC: env_broad_scale DNA Derived | DwC: env_local_scale DNA Derived | DwC: env_medium DNA Derived | DwC: samp_collect_device DNA Derived | DwC: samp_mat_process DNA Derived | DwC: samp_size DNA Derived | DwC: size_frac Terms related to DNA and PCR methods: DNA Derived | DwC: sop DNA Derived | DwC: concentration DNA Derived | DwC: concentrationUnit DNA Derived | DwC: methodDeterminationConcentrationAndRatios DNA Derived | DwC: contaminationAssessment DNA Derived | DwC: target_gene DNA Derived | DwC: target_subfragment DNA Derived | DwC: ampliconSize DNA Derived | DwC: amplificationReactionVolume DNA Derived | DwC: amplificationReactionVolumeUnit DNA Derived | DwC: baselineValue DNA Derived | DwC: automaticBaselineValue DNA Derived | DwC: automaticThresholdQuantificationCycle DNA Derived | DwC: thresholdQuantificationCycle DNA Derived | DwC: pcr_analysis_software DNA Derived | DwC: pcr_primer_forward DNA Derived | DwC: pcr_primer_reverse DNA Derived | DwC: pcr_primer_name_forward DNA Derived | DwC: pcr_primer_name_reverse DNA Derived | DwC: pcr_primer_reference DNA Derived | DwC: pcr_cond DNA Derived | DwC: pcr_primer_lod DNA Derived | DwC: pcr_primer_loq DNA Derived | DwC: annealingTemp DNA Derived | DwC: annealinTempUnit DNA Derived | DwC: probeQuencher DNA Derived | DwC: probeReporter DNA Derived | DwC: quantificationCycle DNA Derived | DwC: ratioOfAbsorbance260_230 DNA Derived | DwC: ratioOfAbsorbance260_280 There are many specialized qPCR terms that are possible to add to the dataset. The terms concentration, concentrationUnit, ratioOfAbsorbance260_230, ratioOfAbsorbance260_280, and methodDeterminationConcentrationAndRatios are related to the original DNA sample before qPCR analysis, and can be useful for evaluating the prevalence of the marker in the sample as well as the purity of the DNA for any indication of PCR inhibition. As with the metabarcoding dataset, the details of the PCR conditions and primers can be recorded in the multiple pcr_ terms as well as target_ terms, and amplificationReactionVolume and amplificationReactionVolumeUnit. The main terms that are important for the quantification information and are different from the metabarcoding dataset are baselineValue, thresholdQuantificationCycle and quantificationCycle. The terms pcr_primer_lod, pcr_primer_loq, probeQuencher, probeReporter are additional terms specific for qPCR assays. The baselineValue indicates the number of cycles below which the signal is considered only background noise. The quantificationCycle is the most important and indicates at which cycle the particular sample crossed the detection threshold, this will be different for each sample. It is recommended to record this information, but not all of this may be easily available. 5.6.4 OBIS Bioinformatics Pipeline OBIS recognizes the vast amount of data generated from marine DNA sampling, especially from eDNA sequencing. Thus we have been developing a bioinformatics pipeline to facilitate publication of this data into OBIS. The pipeline was initially developed for the PacMAN project (Pacific Islands Marine Bioinvasions Alert Network). Broadly speaking, it creates a framework that receives raw sequence data from eDNA samples, cleans, aligns, classifies sequences, and finally outputs a DwC-compatible table. To note, the pipeline automatically searches for aphia-IDs from WoRMS to include in the DWC-compatible tables. In addition the output also contains a phyloseq object, which is compatible with the commonly used phyloseq R package for sequence data analysis. The pipeline is under active development. More details about the PacMAN pipeline can be found on its associated GitHub repository. OBIS is developing guidelines and pipelines to accept other data types, such as: Acoustic Imaging Tracking Habitat "],["vocabulary.html", "6 Standardizing terms & vocabularies 6.1 Map data fields to Darwin Core 6.2 Map eMoF measurement identifiers to preferred vocabulary", " 6 Standardizing terms & vocabularies Content Map data fields to Darwin Core Map eMoF measurement identifiers MeasurementOrFact vocabulary background Guidelines to populate measurementUnitID Guidelines to populate measurementValueID Guidelines to populate measurementTypeID 6.1 Map data fields to Darwin Core There are many possible ways of setting up your datasheets, and if you are new to OBIS you likely did not use controlled Darwin Core (DwC) or BODC vocabulary before samples were collected. In mapping your data fields to DwC we recommend documenting your choices so you have a reference to go back to should the need arise. In such a document you should take notes on the choices you made, as well as any actions you had to take (e.g. separate one column into many, convert dates or coordinates, etc.). For example, a DwC mapping reference table could look like the following: Verbatim field name Mapped DwC term Actions taken Notes date eventDate convert dates to ISO coordinates decimalLongitude, decimalLatitude convert ddmmss to decimal degrees, separated one column into 2 for longitude and latitude put original coordinates into verbatimCoordinates In order to help you map your data to DwC terms, we have provided the table below which outlines some common data fields, their associated Darwin Core vocabulary, and which data table the field is likely to go in: Common Raw Terms DwC Field Data table Date, Time eventDate Event, Occurrence Species, g_s, taxa scientificName Occurrence Any biotic/abiotic measurements* measurementType, measurementValue, measurementUnit* eMoF Depth maximumDepthInMeters or minimumDepthInMeters Event, Occurrence Lat/Latitude, Lon/Long/Longitude, dd decimalLatitude, decimalLongitude Event, Occurrence Sampling method samplingProtocol eMoF Sample size, N, #, No. sampleSizeValue eMoF Location locality Event Presence, absence occurrenceStatus Occurrence Type of record/ specimen basisofRecord Occurrence Person/ people that recorded the original Occurrence recordedBy Occurrence OrcID of person/ people that recorded the original Occurrence recordedByID Occurrence Person/ people that identified the organism identifiedBy Occurrence OrcID of person/ people that identified the organism identifiedByID Occurrence Data collector, data creator recordedBy Event, Occurrence Taxonomist, identifier identifiedBy Occurrence Record number, sample number, observation number occurrenceID (either ID or incorporated into ID) Occurrence Note that mapping abiotic/biotic measurement fields (sex, temperature, abundance, lengths, etc.) will occur within the extendedMeasurementOrFact extension. Here this data will go from being a separate column to being condensed into the measurementType and measurementValue fields. The obistools R package also has the map_fields function that you can use to map your dataset fields to a DwC term. 6.2 Map eMoF measurement identifiers to preferred vocabulary MeasurementOrFact vocabulary background The MeasurementOrFact terms measurementType, measurementValue, and measurementUnit are completely unconstrained and can be populated with free text. While free text offers the advantage of capturing complex and as yet unclassified information, there is inevitable semantic heterogeneity (e.g., of spelling, wording, or language) that becomes a challenge for effective data interoperability and analysis. For example, if you were interested in finding all records related to length measurements, you would have to try to account for all the different ways “length” was recorded by data providers (length, Length, len, fork length, etc.). Caution: You can use the OBIS Measurement Type search tool to see the diversity of measurementTypes that exist across published datasets in OBIS. However note that any measurementTypeIDs listed in this tool are solely for consultation purposes. In some cases codes may have been incorrectly chosen for the associated measurementType. You should always choose measurementTypeIDs based on your own data and the guidelines in this manual. The 3 identifier terms measurementTypeID, measurementValueID and measurementUnitID are used to standardize the measurement types, values and units. These three terms should be populated using controlled vocabularies referenced using Unique Resource Identifiers (URIs). For OBIS, we recommend using the internationally recognized NERC Vocabulary Server, developed by the British Oceanographic Data Centre (BODC). This server can be accessed through: SeaDataNet facet search https://vocab.seadatanet.org/p01-facet-search (recommended for searching the P01 collection, detailed below) NERC Vocabulary Server (NVS) search https://www.bodc.ac.uk/resources/vocabularies/vocabulary_search/ Semantic Model Vocabulary Builder https://www.bodc.ac.uk/resources/vocabularies/vocabulary_builder/ Controlled vocabularies are incredibly important to ensure data are interoperable - readable by both humans and machines and that the information is presented in an unambiguous manner. Vocabulary collections like NERC NVS2 compile vocabularies from different institutions and authorities (e.g., ISO, ICES, EUNIS), allowing you to map your data to them. In this way, you could search for a single measurementTypeID and obtain all related records, regardless of differences in wording or language used in the data. Each vocabulary “term” in NVS is a concept that describes a specific idea or meaning. For consistency, we refer to individual vocabularies in NVS as concepts. Concepts within NVS are organized into collections that group concepts with commonalities (e.g. all concepts pertaining to units). Sometimes collections contain concepts that are deprecated. Terms can be deprecated due to duplication of concepts, or when a term becomes obsolete. You should not use any deprecated concepts for any measurement ID. Deprecated concepts can be identified from lists on NVS because their identifier will have a red warning symbol, and the page for the term itself will indicate the concept is deprecated in red lettering. Unfortunately, there is currently no notification system in place to automatically warn you if a previously used concept has become deprecated. We recommend occasionally confirming that the concepts you or your institution use are still available for use. Guidelines for populating each mesaurement ID are described below. 6.2.1 Populate measurementUnitID The measurementUnitID field is the easiest measurement ID field to populate. It is used to provide a URI for the unit associated with the value provided to measurementValue (e.g. cm, kg, kg/m2). This field should be populated with concepts from the P06 collection, BODC-approved data storage units. Documentation for this collection can be found here. The entire vocabulary list, including deprecated terms can be found at http://vocab.nerc.ac.uk/collection/P06/current. However, we strongly recommend using https://www.bodc.ac.uk/resources/vocabularies/vocabulary_search/P06/ to avoid potentially selecting deprecated terms. Some examples of measurementUnits and the associated measurementUnitID include: Metres: http://vocab.nerc.ac.uk/collection/P06/current/ULAA/ Days: http://vocab.nerc.ac.uk/collection/P06/current/UTAA/ Metres per second: http://vocab.nerc.ac.uk/collection/P06/current/UVAA/ Percent: http://vocab.nerc.ac.uk/collection/P06/current/UPCT/ Milligrams per litre: http://vocab.nerc.ac.uk/collection/P06/current/UMGL/ 6.2.2 Populate measurementValueID The measurementValueID field is used to provide an identifying code for measurementValues that are non-numerical (e.g. sampling related, sex or life stage designation, etc.). Note: it is NOT used for standardizing numeric measurements! Unlike measurementUnitID, there is more than one collection which may be used to search for and use concepts from. The collection is dependent on which type of measurementValue you have. See the table below for some common, non-exhaustive examples. Note that behaviour is an exception in that we recommend to use codes from the ICES vocabulary server, instead of NERC. A video is available on the OBIS YouTube Vocabulary series that demonstrates how to find and select vocabularies for measurementValueID from both NREC and ICES. Type of measurementValue Collection Collection Documentation Complete Vocabulary List Sex (gender) S10 https://github.com/nvs-vocabs/S10 <http://vocab.nerc.ac.uk/collection/S10/current/ Lifestage S11 https://github.com/nvs-vocabs/S11 http://vocab.nerc.ac.uk/collection/S11/current/ Sampling instruments and sensors (SeaVoX Device Catalogue) L22 https://github.com/nvs-vocabs/L22 http://vocab.nerc.ac.uk/collection/L22/current Sampling instrument categories (SeaDataNet device categories) L05 https://github.com/nvs-vocabs/L05 <http://vocab.nerc.ac.uk/collection/L05/current Vessels (ICES Platform Codes) C17 - http://vocab.nerc.ac.uk/collection/C17/current European Nature Information System Level 3 Habitats C35 - https://vocab.nerc.ac.uk/collection/C35/current/ Behaviour ICES Behaviour collection https://vocab.ices.dk/?codetypeguid=d0268a96-afc9-436e-a4db-4f61c2b9f6ba You can also populate measurementValueID with references to papers or manuals that document the sampling protocol used to obtain the measurement. To do this you should use either: The DOI of the paper/manual Handle for publications on IOC’s Ocean Best Practices repository, e.g. http://hdl.handle.net/11329/304 6.2.3 Populate measurementTypeID 6.2.3.1 The P01 Collection One of the more important collections for OBIS is the P01 collection. Important note! P01 codes are required for the measurementTypeID field. The P01 is a large collection with >45,000 concepts. Each concept within this collection is composed of different elements that, together, construct a label you can use for a measurement type. Frequently, concepts from the P01 collection are referred to as a “P01 code”. P01 codes are used to populate the measurementTypeID field. It is important to know that a semantic model, shown below, underlies each P01 code and the elements that compose them. There are 5 potential elements in this semantic model that, together, unambiguously describe a measurement type. See the P01 wheel for example of how these elements relate and combine to make one P01 code. Elements of the semantic model for P01 codes Property/attribute: the measurement or observation of either an object of interest or a matrix, or both Object of interest: a chemical object, a biological object, a physical phenomenon, or a material object In relation to: how the measurement is related to the environment Environmental matrix: what environment the measurement is in (e.g. water body, seabed); needed for most environmental measurements, but may not be necessary for e.g. biological measurements Method: any specific methods used that are important to interpret the measurement Note: Not every element is required, but it is important to think about each piece of the model and how it may or may not apply to your measurement. More details about this are described below in the mesaurementTypeID section. You can use codes from other collections (e.g. P06, QL22) for measurementValueID and measurementUnitID fields, but for measurementTypeID you must always use a code from the P01 collection. 6.2.3.1.1 Selecting P01 codes for measurementTypeID When selection P01 codes, it is important to understand that each element within a P01 code is meant to describe an aspect of the measurement: what is the measurement, what is the object or entity being measured, in what environment was the measurement taken, by what kind of methods, etc.? By taking together all these elements, we are able to have a unique and specific description to differentiate one measurement from another. More documentation about the P01 code and the semantic model it is based on can be found here. The P01 collection is found here and can be searched through the NERC vocabulary server. You may notice when searching for measurement types related to an occurrence that specific taxonomic codes are available to you, e.g., abundance of Notommata. For OBIS, all P01 codes should be generalized - i.e. do not select species-specific codes. Instead only choose codes for “biological entities specified elsewhere” when the measurement is related to an occurrence record. There are several ways of searching for a P01 code, but we highly recommend using the SeaDataNet P01 Facet Search. You may notice when searching for measurement types related to an occurrence that specific taxonomic options are available to you, e.g., abundance of Notommata. For OBIS, all P01 codes should be generalized - i.e. do not select species-specific codes. Instead, only choose codes for “biological entities specified elsewhere”! This is due to the Darwin Core Archive structure - taxonomic identification is already specified in the Occurrence table, but measurements are recorded in the ExtendedMeasurementOrFact table. When you are comfortable and understand P01 codes, you can also use the BODC Vocabulary Builder or simply search for terms directly on the NERC Vocabulary Server. Vocabulary for measurements related to sampling instruments and/or sampling methods can also be found in the P01 collection and are mostly classified as sampling parameters (a search keyword that can be used in the SeaDataNet Facet search). These vocabulary used to fall under the Q01 collection, however this collection has been deprecated and the terms have been remapped to P01 codes (see GitHub issue on this here). See below for examples of sampling-related measurements and their associated P01 code: Description of sub-sampling protocol: https://vocab.nerc.ac.uk/collection/P01/current/DSPSSP01/ Length of sampling track https://vocab.nerc.ac.uk/collection/P01/current/LENTRACK/ Height of sample collector (e.g. a grab) https://vocab.nerc.ac.uk/collection/P01/current/MTHHGHT1/0 Name of sampling instrument https://vocab.nerc.ac.uk/collection/P01/current/NMSPINST/ Sample duration https://vocab.nerc.ac.uk/collection/P01/current/AZDRZZ01/ Use the following decision tree to help you select P01 codes for biological, chemical, physical, and/or sampling measurements. See the OBIS YouTube Vocabulary series for guidance on how to use this tree and examples for different types of measurementTypeIDs. Note that the measurementTypeID:sampling measurements branch is newly developed, and we are happy to receive feedback if the suggestions are not working for you. Decision tree to assist with finding and selecting appropriate controlled vocabulary to be used in the eMoF. For those unfamiliar with the tree see the OBIS YouTube Vocabulary series. 6.2.4 Request new vocabulary terms If you have already tried looking for a P01 code and were unable to identify a suitable code for your measurementType you must then request a code to be created. Before doing so, make sure you have not over filtered the search results. Then, to request a new term, your request must be submitted via: Submit request through the OBIS Vocabulary GitHub repository (https://github.com/nvs-vocabs/OBISVocabs/issues) Requests can also be emailed to vocab.services@bodc.ac.uk if you cannot access GitHub Registration with the BODC Vocabulary Builder Note: these requests should be based on combinations of existing concepts We strongly recommend you use GitHub if you can, as it allows longer-term documentation, and can be relevant for other users who may be interested in the same type of code creation. Finally, if you are unsure about whether a code fits your specific case, please feel free to ask questions to the Vocab channel on the OBIS Slack. 6.2.4.1 How to Submit a GitHub Vocabulary Request Navigate to https://github.com/nvs-vocabs/OBISVocabs/issues and click on the New Issue button. Screenshot of how to request a new vocabulary on Github Click Get started Screenshot of submitting an issue to Github Fill in the title with short details of your request or issue. Then fill in the description. It is recommended to list any existing terms that are similar to your request, or concepts that are sub-components of the request. Screenshot for how to request a new measurementType on Github Example: An issue was created to address difficulties in identifying P01 codes for sex rather than gender. Gender is a concept generally applied to humans, whereas “sex” is more applicable for animals. Thus the request was to either modify the current gender P01 code, or create a P01 code that specifies sex, not gender. At the time the request was issued, when users searched for a P01 term for “sex”, only species-specific terms were available. Example of previously requested new term on Github "],["common_formatissues.html", "7 Other Important Data Formatting Steps 7.1 Temporal: Dates and times 7.2 Historical data 7.3 Spatial 7.4 Missing required fields", " 7 Other Important Data Formatting Steps After creating data tables and mapping terms to DwC, additional data formatting steps may be needed and/or some common issues may arise. On this page, you will find guidelines for specific data formatting cases, including: Temporal issues: dates/times Historical data Spatial issues: coordinate conversion Convert geographical formats Missing required fields 7.1 Temporal: Dates and times The date and time at which an event took place or an occurrence was recorded goes in eventDate. This field uses the ISO 8601 standard. OBIS recommends using the extended ISO 8601 format with hyphens. Note that all dates in OBIS become translated to UTC during the quality control process implemented by OBIS. Formatting your dates correctly ensures there will be no errors during this process. ISO 8601 dates can represent moments in time at different resolutions, as well as time intervals, which use / as a separator. Date and times are separated by T. Timezones can be indicated at the end by using + or - the number of hours offset from UTC. If no timezone is indicated, then the time is assumed to be local time. When a date/time is recorded in UTC, a Z should be added at the end. Times must be written in the 24-hour clock system. If you do not know the time, you do not have to provide it. Please do not indicate unknown times as “00:00” as this indicates midnight. Not every piece of time information is necessary, but a generalization of how to format dates and times looks like: YYYY-MM-DDT[hh]:[mm]:[ss] [+/-XX OR Z] Some specific examples of acceptable ISO 8601 dates are: Dates: 1948-09-13 1993-01/02 1993-01 1993 Dates with Specific Times: 1973-02-28T15:25:00 2008-04-25T09:53 Dates with Time Zones: 2005-08-31T12:11+12 2013-02-16T04:28Z Date and Time Intervals: 1993-01-26T04:39+12/1993-01-26T05:48+12 It is important to note that although ISO 8601 also supports ordinal dates (YYYY-DDD) and week dates (YYYY-Www-D), these formats are not supported by OBIS. Additionally, ISO 8601 guidelines for durations should not be used. Durations for an event (e.g., length of observation) can instead be indicated with the DwC terms startDayOfYear and endDayOfYear. Durations refer to the actual length of time an event (e.g., occurrence) occurred, whereas intervals indicate the time period during which an event was recorded. A note about intervals: Take care when entering date intervals as, for example, entering 1960/1975-08-04 indicates that the event or observation started any time in 1960, and ended any time on 1975-08-04. If you know the exact date and time, you should specify that information. This also helps for continuous samplings and time-series integrated datasets. If you have a mix of dates and times for different aspects of a sampling event, you can embed this information in the Event Core table using hierarchies of date structure. To do this, you can use separate records for events, and specify each event date individually. See example. For uncertainty regarding the date of the event, see guidelines. Tips To ensure your date is formatted correctly, it may be easiest to begin by populating the year, month, and day fields first. If the specific time of sampling is known, populate that into eventTime as well. When you fill these fields, we recommend ensuring the numbers are encoded as Text, not as General or numeric as Excel often tries to interpret what it thinks the content “should” be. Otherwise you may run into problems with Excel auto formatting your numbers in ways you don’t want. You can do this by highlighting the cells of interest, navigating to the Number Format on the Home ribbon and selecting “Text”. Be careful when you do this change of format, as some columns (e.g. time) may become formatted into a decimal or other unexpected format. Screenshot of how to change data type in Excel Then you can use Excel to concatenate each field together, adding the time zone at the end, using the general format: =CONCAT(YEAR, "-", MONTH, "-", DAY, "T", EVENTTIME, TIMEZONE) Example of how to concatenate dates in Excel Note You can also use the Canadensys date parsing tool to help you convert dates or parse them into component parts. A caution about dates and Excel: Excel is unfortunately notorious for causing issues in saving dates. The Data Carpentries have produced this exercise which demonstrates how Excel interprets dates and numbers, sometimes converting numbers into dates and vice versa. This exercise is simply a demonstration of Excel - it does not provide advice on formatting dates for OBIS. Date formats in Excel can be very dependent on your computer system region custom and not all of them have the ISO 8601 format included. Therefore you can type the date in the requested format but it will automatically revert the format according to your Windows system region settings. You can change your system region by: navigating to Control Panel > All Control Panel Items > Region and then select “English (United States)” or “English (United Kingdom)”. The YYYY-MM-DD format will appear among the choices within the Format cells - Date options. If your computer language is not set to English, you may encounter additional issues with Excel. It may change the format of your date even after you save the document. Changing your computer system’s language to English can help, but you may still run into issues. You may also try using other office management softwares, like LibreOffice which in this case is more friendly. In general, we advise you to be very careful when formatting the eventDate field, and to select the “Text” formatting (as above) and to save your file as a .CSV. It is good practice to place the verbatim event date/time description into the verbatimEventDate field. Any modifications you make during data formatting should be recorded in the eventRemarks field, and we recommend taking good notes in a personal reference file. This video provides a demonstration of how you can format dates to ISO 8601, including how to resolve difficulties you might run into (some which are also discussed below). How to handle mixed date information When the sampling date is unclear due to a mix of date types and durations, you can use the hierarchical event structure in the Event core to help format and associate dates (and other information) with the correct sampling event. Often times it is useful to break up each type of event into separate records according to the event hierarchy. Let’s look at a fictional dataset to demonstrate this. In this example, there is a project called Maple. This project has a cruise that takes place in May and June which takes samples at three different sites. The project has data for three years, beginning in 1993. How do we capture all this information in our dataset? And how do we format eventDate to reflect these different times and durations? We can embed this information using a different eventID and parentEventID for each level within the project - the reoccurring cruise, the station sites, and then the samples themselves. We have added a column here for “Event Type” for the sake of example. At this time, there is no Darwin Core event type term, however there is discussion for its creation. Our example dataset would then look like the following: parentEventID eventID Event type eventDate eventRemarks MAPLE_1993_crs cruise This is the first year MAPLE_1994_crs cruise This is the second year MAPLE_1995_crs cruise This is the third year MAPLE_1993_crs MAPLE_1993_crs_st1 station This is the first year, first site MAPLE_1993_crs MAPLE_1993_crs_st2 station This is the first year, second site where samples were taken over two days MAPLE_1993_crs MAPLE_1993_crs_st3 station This is the first year, third site MAPLE_1993_crs_st1 MAPLE_1993_crs_st1_s1 sample 1993-05-05T10:13-04 This is the first year, first site, first sample MAPLE_1993_crs_st1 MAPLE_1993_crs_st1_s2 sample 1993-05-05T10:38-04 This is the first year, first site, second sample MAPLE_1993_crs_st2 MAPLE_1993_crs_st2_s1 sample 1993-05-19T23:40-04 This is the first year, second site, first sample MAPLE_1993_crs_st2 MAPLE_1993_crs_st2_s2 sample 1993-05-20T01:24-04 This is the first year, second site, second sample MAPLE_1993_crs_st3 MAPLE_1993_crs_st3_s1 sample 1993-06-01T09:21-04 This is the first year, third site, first sample MAPLE_1993_crs_st3 MAPLE_1993_crs_st3_s2 sample 1993-06-01T09:57-04 This is the first year, third site, second sample You can see that the eventDate for the parent events does not need to be provided - only the dates for the actual samples are required. 7.2 Historical data OBIS recognizes the difficulties in formatting historical, archaeological and paleontological data. This kind of data is sometimes seen as “specialist” or “niche” when it comes to sharing in globally accepted databases that are accessible and recognised in academic, research and scientific forums. Some of the nuances associated with historical data have to do with the change in calendar systems, from the Julian calendar to the currently used (by most countries) Gregorian calendar metric system. This change was implemented in 1582, so any datasets with data representing periods that predate this year must be checked and converted to the standard Gregorian calendar system. Additionally, there is no year zero, only -1 and 1, where -1 is BCE (Before Common Era) and 1 is CE (Common Era). This can make interpretation of historical dates more challenging as such dates will need to be converted to align with ISO 8601 standards. To accommodate such challenges the OBIS Historical Data Project Team recommends the following: Always populate verbatimEventDate with the originally documented date so that it can be preserved. Place converted dates that align to ISO 8601 in the eventDate field, and document the changes you made to the original in eventRemarks. When the exact date is unknown, provide a date range, e.g. the period 21 November 1521 to 29 August 1612 records as 1521-11-21/1612-08-29. For archaeological data, use terms from the Darwin Core class GeologicalContext and/or the Chronometric Age Extension. GeologicalContext terms can be used to capture information such as periods or ages, however the Chronometric Age extension allows for more thorough descriptions of the time period and can link to the Event core table. For such records, eventDate would be populated with the date of collection. N.B. currently, the Chronometric Age extension will not be aggregated when publishing to OBIS, but the extension will be available when an individual dataset is downloaded. If the historical record contains uncertain or sensitive location information, generalize the location information using polygons or lines as described in this Manual. For historical data originating from old records, such as ship logs or other archival records, we understand there can be a variety of issues in interpreting and formatting data according to DwC standards. If you need further help with historical data formatting, we recommend submitting a Github issue, or contacting the OBIS-OPI node who focuses on Oceans Past historical, archaeological, and paleontological data series. 7.3 Spatial Converting Coordinates All coordinates provided in the decimalLatitude or decimalLongitude fields in OBIS must be in decimal degrees. To convert coordinates from degrees-minutes-seconds into decimal degrees, you can use this Coordinate Conversion tool that OBIS has developed. This tool will convert any coordinate (or list of coordinates on a separate line) in a degrees-minutes-seconds format into decimal degrees, even partial coordinates. To use it, simply copy and paste your coordinates into the box provided and click Convert. For example: Screenshot of how to use the OBIS coordinate converter Watch this video for a demonstration on use of this tool. If your coordinates are in UTMs, then coordinate conversion can be a bit trickier. We suggest using the following conversion tool to convert from UTM to decimal degrees. Note it is very important to ensure you have the correct UTM zone, otherwise the coordinate conversion will be incorrect. You can use this ArcGIS map tool to visually confirm UTM zones. Geographical format conversion In OBIS, the spatial reference system to be documented in geodeticDatum is EPSG:4326 (WGS84). If your spatial data are not already in this format, you may have to convert it. To do this there are a few approaches: QGIS (or ArcGIS), R, or Python. We provide some short guidance for each, however if you are struggling to convert your data to WGS84 please contact helpdesk@obis.org or send a message on the OBIS Slack. QGIS You can load a .csv file containing your coordinates to be reprojected into QGIS. Opening a new project, first set the global projection to WGS84 EPSG:4326. In the bottom right corner, click the Project Properties to change the Project Coordinate Reference System (CRS). A pop up window will allow you to search for and select WGS84 EPSG:4326. Click OK. Screenshot of QGIS interface To load your .csv file containing the longitude and latitude coordinates, go to Layer < Add Layer < Add Delimited Text layer… How to add a .csv with coordinate data in QGIS A popup window will allow you to browse and select your .csv file. Open the Geometry Definition portion of the window and map the field containing longitude values to the X field and latitude to the Y field. Select the CRS that these coordinates were recorded as from the drop down menu. Then click Add and close the window. Screenshot showing how to specify CRS of a .csv file when importing into QGIS Go to Vector < Geometry Tools < Add Geometry Attributes Screenshot showing where to find the Geometry Attributes in QGIS menu Make sure the input layer is your coordinate file. Under the Calculate using, select Project CRS (because we set the Project CRS to the desired projection). Click Run. This will create a new layer with an additional two columns called Xcoord (longitude) and Ycoord (latitude). These fields contain the coordinates in the desired projection (i.e., WGS84). You can view these columns by right clicking and opening the layer’s attribute table. To export the file, right click the layer and click Make Permanent. Then save the .csv. Screenshot showing how to save a temporary layer in QGIS for export For more details see this QGIS guide on reprojection. R To reproject coordinates in R, you can use functions in the sf package. A thorough tutorial using this package can be found here. Python You also have the option to reproject data using the Python library Geopandas. In this package there is a utility called to_crs that will reproject data. A tutorial to do this can be found here. 7.4 Missing required fields If you are a node manager and one of the datasets in your IPT is missing data, you should prepare a brief report to contact the data provider and outline what is missing. Get in contact with original data provider if possible. If it is not possible to contact the original data provider (frequently the case for historical datasets), do your best to follow the guidelines below to fill in data. However, do not guess or make assumptions if you are unsure. For all fields inferred, please record notes in the eventRemarks, occurrenceRemarks, or ’identificationRemarks` field, as applicable. If you are a data provider and notice or been notified that you are missing one (or more) of the eight required terms for OBIS, please proceed accordingly for each term. To resolve missing fields marked as required by OBIS, there are several things you can do, depending on which required field is missing. Follow the guidelines below for each term. occurrenceID or eventID Create a unique occurrenceID for each of your observations. These IDs can be generated by combining dates, location names, and sampling methods. eventDate Ensure your eventDate is specified for each event, formatted according to ISO 8601 standards (e.g., YYYY-MM-DD). We have developed step by step guidelines to help you format contemporary dates and durations into ISO formatting. If your date falls outside the range of acceptable dates - i.e., historical or geological data occurring before 1583 - please follow recommendations for historical data. For any eventDate that is inferred from literature, you should document the original date in the verbatimEventDate field. decimalLongitude and decimalLatitude First, if you have coordinate data, make sure they are converted into decimal degrees. If you do not have specific coordinate data then you must approximate the coordinates based on locality name. You can use the Marine Regions gazetteer to search for your region of interest and obtain midpoint coordinates. Guidelines for using this tool and for dealing with uncertain geolocations can be found here. You will have to make some comments in the georeferenceRemarks field if you are estimating coordinates. scientificName This field should contain only the originally documented scientific name down to the lowest possible taxon rank, even if there are misspellings or if it is a current synonym. Class or even Kingdom levels are accepted if more specific taxonomic levels are unknown. Comments about misspellings, etc. can be documented in the taxonRemarks field. Note that there may be special cases for eDNA and DNA derived data, see specific guidelines for these cases. You may encounter challenges filling this field if the species name is based on description or if its taxonomy was uncertain at the time of sampling. For such uncertain taxonomy situations, see our guidelines here. scientificNameID (strongly recommended) If you cannot obtain the required Life Science Identifier (LSID) from taxon matching with WoRMS then you must contact World Register Marine Species to have an LSID created for your taxon. You will need to confirm that the species is marine. OBIS does not parse LSIDs from other sources (e.g., Integrated Taxonomic Information System, Catalog of Life), but if you want to include other LSIDs alongside the WoRMS LSID, they must be specified in a predictable format. occurrenceStatus Because occurrenceStatus is a binary field, “presence” or “absence”, this field can usually be easily inferred by data. If there are associated measurements or a record of an observation, the taxon in question would be present. If a particular species/taxa is present in one sample, but missing from another, then you could identify that species as absent from the second sample. basisOfRecord basisOfRecord distinguishes what type of record is in your data. For records pertaining to a collected or stored specimen, you must choose one of the following terms: PreservedSpecimen FossilSpecimen LivingSpecimen For records pertaining to an observation in the wild, you should use: HumanObservation (e.g., observation in the wild) MachineObservation (e.g., photograph, acoustic detection, etc.) MaterialSample (e.g. DNA sequences, DNA detection) For records pertaining to literature data, basisOfRecord should always reflect the evidence upon which the Occurrence record was based. For example, a researcher’s record based on photographs should specify MachineObservation, otherwise specifications should be HumanObservation (see relevant GitHub discussion). For specifics on when to use each of these and which other fields should be populated along with them, see the guidelines on record-level terms. "],["examples.html", "8 Dataset Examples: ENV-DATA", " 8 Dataset Examples: ENV-DATA Contents Fish abundance & distribution Hard coral cover & composition Invertebrates abundance & distribution Macroalgae canopy cover & composition Mangroves cover & composition Marine birds abundance & distribution Marine mammals abundance & distribution Marine turtles abundance & distribution Survey & sighting data Microbes biomass & diversity Phytoplankton biomass & diversity Seagrass cover & composition Zooplankton biomass & diversity Special data types: DNA derived data eDNA data from Monterey Bay, California 16S rRNA gene metabarcoding data of Pico- to Mesoplankton Acoustic, imaging, or other multimedia data Tracking data Habitat 8.0.0.1 Fish abundance and distribution (example coming soon)   8.0.0.2 Hard coral cover and composition (example coming soon)   8.0.0.3 Invertebrates abundance and distribution (example coming soon)   8.0.0.4 Macroalgae canopy cover and composition In this section we will encode a fictional macroalgal survey dataset into Darwin Core using the ENV-DATA approach, i.e. using an Event core with an Occurrence extension and an extendedMeasurementOrFact extension. Figure: A fictional macroalgae survey with a single site, multiple zones, quadrats, and different types of transects. Event core: First we can create the Event core table by extracting all events in a broad sense and populating attributes such as time, location, and depth at the appropriate level. The events at the different levels are linked together using eventID and parentEventID. As the survey sites has a fixed location we can populate decimalLongitude and decimalLatitude at the top level event. The zones have different depths, so minimumDepthInMeters and maximumDepthInMeters are populated at the zone level. Finally, as not all sampling was done on the same day, eventDate is populated at the quadrat and transect level. eventID parentEventID eventDate decimalLongitude decimalLatitude minimumDepthInMeters maximumDepthInMeters site_1 54.7943 16.9425 zone_1 site_1 0 0 zone_2 site_1 0 5 zone_3 site_1 5 10 quadrat_1 zone_1 2019-01-02 transect_1 zone_2 2019-01-03 transect_2 zone_3 2019-01-04 Occurrence extension: Next we can construct the Occurrence extension table. This table has the scientific names and links to the World Register of Marine Species in scientificNameID. The first column of the table references the events in the core table (see quadrat_1 for example highlighted in green). id occurrenceID scientificName scientificNameID quadrat_1 occ_1 Ulva rigida urn:lsid:marinespecies.org:taxname:145990 quadrat_1 occ_2 Ulva lactuca urn:lsid:marinespecies.org:taxname:145984 transect_1 occ_3 Plantae urn:lsid:marinespecies.org:taxname:3 transect_1 occ_4 Plantae urn:lsid:marinespecies.org:taxname:3 transect_2 occ_5 Gracilaria urn:lsid:marinespecies.org:taxname:144188 transect_2 occ_6 Laurencia urn:lsid:marinespecies.org:taxname:143914 extendedMeasurementOrFact (eMoF) extension: And finally there is the MeasurementOrFact extension table, which has attributes of the zones (shore height), the quadrats (surface area), the transects (surface area and length), and the occurrences (percentage cover and functional group). Attributes of occurrences point to the Occurrence extension table using the occurrenceID column (see occ_1 and occ_2 highlighted in blue and orange). Note that besides NERC vocabulary terms we are also referencing the CATAMI vocabulary for macroalgal functional groups. id occurrenceID measurementType measurementTypeID measurementValue measurementValueID measurementUnit measurementUnitID zone_1 shore height ? high ? quadrat_1 surface area P01/current/AREABEDS 0.25 m2 P06/current/UMSQ quadrat_1 occ_1 cover P01/current/SDBIOL10 24 percent P06/current/UPCT quadrat_1 occ_2 cover P01/current/SDBIOL10 56 percent P06/current/UPCT transect_1 surface area P01/current/AREABEDS 60 m2 P06/current/UMSQ transect_1 length P01/current/LENTRACK 30 m P06/current/ULAA transect_1 occ_3 functional group ? sheet-like red CATAMI:80300925 transect_1 occ_4 functional group ? filamentous brown CATAMI:80300931 transect_1 occ_3 cover P01/current/SDBIOL10 8 percent P06/current/UPCT transect_1 occ_4 cover P01/current/SDBIOL10 24 percent P06/current/UPCT transect_2 occ_5 cover P01/current/SDBIOL10 4 percent P06/current/UPCT transect_2 occ_6 cover P01/current/SDBIOL10 16 percent P06/current/UPCT 8.0.0.5 Mangroves cover and composition (example coming soon)   8.0.0.6 Marine birds abundance and distribution The example for ENV-DATA collected with marine bird sightings/occurrences is based on the dataset “RV Investigator Voyage IN2017_V02 Seabird Observations, Australia (2017)”. In this dataset, seabird sightings were recorded continuously during daylight hours during a voyage to recover and redeploy moorings at the SOTS site, southwest of Tasmania, Australia, in March 2017. Observations were made from c.30 minutes before sunrise to c.30 minutes after sunset, extending to 300m in the forward quadrant with the best viewing conditions. There were 1200 observations from 38 species of birds along with 3 cetacean species and one seal. This example will focus on the ENV-DATA associated with the bird sightings. The most frequently sighted bird species were Puffinus tenuirostris (Short-tailed Shearwater) and Pachyptila turtur (Fairy Prion). For this dataset, human observation recorded individual bird sightings (thus, each specimen is a single occurrence). The dataset contains abiotic measurements (ENV-DATA) which are related to each individual sighting, instead of an entire sample. Therefore, we can create an Occurrence core with an eMoF extension that contain the abiotic environmental measurements or facts. Occurrence core: The Occurrence core is populated with the occurrence records of seabirds sighted during the RV voyages. Occurrence details and scientific names are provided here. All birds were observed above sea level, all minimumDepthInMeters and maximumDepthInMeters values equal zero. occurrenceID eventDate institutionCode collectionCode basisOfRecord recordedBy organismQuantity organismQuantityType occurrenceStatus decimalLatitude decimalLongitude coordinateUncertaintyInMeters coordinatePrecision footprintWKT in2017_v02_00998 2017-03-17 01:07:00 Australasian Seabird Group, BirdLife Australia in2017_v02_wov HumanObservation EJW+CRC+TAH 2 individuals present -43.40741 147.45576 200 0.0018 POINT (147.45576 -43.40741) in2017_v02_01380 2017-03-19 22:26:00 Australasian Seabird Group, BirdLife Australia in2017_v02_wov HumanObservation EJW+CRC+TAH 1 individuals present -45.98644 142.1445 200 0.0018 POINT (142.14450 -45.98644) in2017_v02_01012 2017-03-17 02:38:00 Australasian Seabird Group, BirdLife Australia in2017_v02_wov HumanObservation EJW+CRC+TAH 1 individuals present -43.40728 147.45549 200 0.0018 POINT (147.45549 -43.40728) extendedMeasurementOrFact (eMoF) extension: As shown in previous examples, the MeasurementOrFact extension table contains abiotic measurements or facts corresponding to an occurrence / sighting. Individual sightings and abiotic measurements are linked with occurrenceID. In the example dataset, the ENV-DATA consist of measurements taken during the moorings deployment at the SOTS site, at the time of the marine bird sightings. In addition to NERC vocabulary terms, authors also referenced the Australian Ocean Data Network (AODN) Discovery Parameter Vocabulary for Sea-floor depth (m) and Sea Surface Temperature as measurementType. NERC equivalents to the AODN terms are added as additional MeasurementOrFact (MoF) records. occurrenceID measurementID measurementType measurementTypeID measurementValue measurementValueID measurementUnit measurementUnitID in2017_v02_00998 in2017_v02_00998-depth Sea-floor depth (m) http://vocab.aodn.org.au/def/discovery_parameter/entity/574 73.0313 NA Metres http://vocab.nerc.ac.uk/collection/P06/current/ULAA in2017_v02_00998 in2017_v02_00998-depth Sea-floor depth http://vocab.nerc.ac.uk/collection/P01/current/MBANZZZZ/ 73.0313 NA Metres http://vocab.nerc.ac.uk/collection/P06/current/ULAA in2017_v02_00998 in2017_v02_00998-air_pressure Air Pressure (hPa) http://vocab.nerc.ac.uk/collection/P01/current/CAPHZZ01 1024.91385 NA hPa http://vocab.nerc.ac.uk/collection/P06/current/HPAX in2017_v02_00998 in2017_v02_00998-air_temp Atmospheric temperature (deg C) http://vocab.nerc.ac.uk/collection/P01/current/CTMPZZ01 15.3 NA degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA in2017_v02_00998 in2017_v02_00998-wov_sea_state Sea state http://vocab.nerc.ac.uk/collection/C39/current/ moderate 1.25 - 2.5 m http://vocab.nerc.ac.uk/collection/C39/current/4/ in2017_v02_00998 in2017_v02_00998-sea_surface_temp Sea surface temperature http://vocab.aodn.org.au/def/discovery_parameter/entity/97 17.32 NA degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA in2017_v02_00998 in2017_v02_00998-sea_surface_temp Sea surface temperature http://vocab.nerc.ac.uk/standard_name/sea_surface_temperature/ 17.32 NA degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA in2017_v02_00998 in2017_v02_00998-wind_direction Wind direction (deg) http://vocab.nerc.ac.uk/collection/P01/current/EWDAZZ01 283 NA degrees http://vocab.nerc.ac.uk/collection/P06/current/UABB in2017_v02_00998 in2017_v02_00998-wind_speed Wind Speed (knt) http://vocab.nerc.ac.uk/collection/P01/current/ESSAZZ01 5.49 NA Knots (nautical miles per hour) http://vocab.nerc.ac.uk/collection/P06/current/UKNT 8.0.0.7 Marine mammals abundance and distribution In this section we will explore how to encode a survey data set into Darwin Core using the ENV-DATA approach. As an example, sections of the actual data set of CETUS: Cetacean monitoring surveys in the Eastern North Atlantic, is used. Figure: A representation of the observation events of CETUS: Cetacean monitoring surveys in the Eastern North Atlantic, presenting the route Madeira as a site with three cruises (zones). Each Cruise is divided into different Transects and each transect contains a number of Positions. Event core: Create the Event core table by extracting all events and populating attributes. As in the previous example, the events at the different levels are linked together using eventID and parentEventID. As the survey observations were made at locations of cetacean sightings instead of fixed locations, we can populate footprintWKT and footprintSRS as location information. Not all sampling was done on the same day, therefore eventDate is populated at the transect level. eventID parentEventID eventDate footprintWKT footprintSRS Madeira 2012-07/2017-09 POLYGON ((-16.74 31.49, -16.74 41.23, -8.70 41.23, -8.70 31.49, -16.74 31.49)) EPSG:4326 Madeira:Cruise-001 Madeira 2012-07 MULTIPOINT ( (-8.7 41.19), (-9.15 38.7)) EPSG:4326 Madeira:Cruise-002 Madeira 2012-07 MULTIPOINT ( (-9.15 38.7), (-16.73 32.74)) EPSG:4326 Madeira:Cruise-003 Madeira 2012-07 MULTIPOINT ( (-16.73 32.74), (-9.15 38.7)) EPSG:4326 Occurrence extension: Construct the Occurrence extension table with the scientific names and links to the World Register of Marine Species in scientificNameID. The first column of the table references the events in the core table (see Madeira:Cruise-001 highlighted in green).The occurrenceID corresponds to the Position of the observation (see Transect-01:Pos-0001 and CIIMAR-CETUS-0001 highlighted in blue, or Transect-01:Pos-0002 and CIIMAR-CETUS-0002 highlighted in orange). id occurrenceID scientificNameID scientificName Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-01:Pos-0002 CIIMAR-CETUS-0002 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-01:Pos-0003 CIIMAR-CETUS-0003 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-02:Pos-0004 CIIMAR-CETUS-0004 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-02:Pos-0005 CIIMAR-CETUS-0005 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-02:Pos-0006 CIIMAR-CETUS-0006 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-02:Pos-0007 CIIMAR-CETUS-0007 urn:lsid:marinespecies.org:taxname:2688 Cetacea extendedMeasurementOrFact (eMoF) extension: And finally, the extendedMeasurementOrFact extension table has attributes of the zones (such as Vessel speed and Vessel Heading), the Transects (such as Wave height and Wind speed), and the Positions (such as Visibility and the Number of smaal/big ships >20m). Attributes of Positions point to the Occurrence extension table using the occurrenceID column (see Transect-01:Pos-0001 and Transect-01:Pos-0002 highlighted in blue and orange, respectively). id occurrenceID measurementType measurementTypeID measurementValue measurementUnit measurementUnitID Madeira:Cruise-001 Vessel name Q01/current/Q0100001 Monte da Guia Madeira:Cruise-001:Transect-01 Length of the track P01/current/DSRNCV01 39.75 km P06/current/ULKM Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Visibility 2000-4000 Meters P06/current/ULAA Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Wind speed P01/current/WMOCWFBF 1 Beaufort scale Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Wave height 2 Douglas scale Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Number of big ships (>20m) 3 Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Vessel heading P01/current/HDNGGP01 206 Degrees P06/current/UAAA Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Number of small ships (<20m) 0 Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Vessel speed P01/current/APSAGP01 16 Knots (nautical miles per hour) P06/current/UKNT 8.0.0.8 Marine turtles abundance and distribution 8.0.0.8.1 Survey and sighting data This section deals with encoding survey and/ or sighting data of sea turtles into Darwin Core using the ENV-DATA approach. Extracts from the actual data set of Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea (Civitavecchia-Barcelona route) between 2013 and 2017, are used as an example. Event core: The Event core is created by extracting all sighting events and populating the attributes at each event. The events at the different levels are linked together using eventID and parentEventID. In the example dataset, turtle sightings have been recorded since 2007, along a ferry route between Italy and Spain, as part of the monitoring project FLT Med Net (Fixed Line Transect Mediterranean monitoring Network). Turtle sighting locations can be given by populating the fields footprintWKT and footprintSRS with location information. Sightings were recorded at different dates, therefore eventDate is populated at the transect level. id modified datasetID datasetName eventID parentEventID eventDate eventRemarks minimumDepthInMeters maximumDepthInMeters decimalLatitude decimalLongitude geodeticDatum coordinateUncertaintyInMeters footprintWKT footprintSRS TURTLE_CBAR_0043 2021-05-05 07:59:08 https://marineinfo.org/id/dataset/6403 Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea TURTLE_CBAR_0043 2013-04-03T05:30:00+02:00/2013-04-03T16:00:00+02:00 transect 0 0 41.26179967 4.933265167 EPSG:4326 222970.2874 LINESTRING (7.602633333333 41.24378333333, 2.263897 41.279816) EPSG:4326 TURTLE_CBAR_0045 2021-05-05 07:59:08 https://marineinfo.org/id/dataset/6403 Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea TURTLE_CBAR_0045 2013-04-18T05:22:00+02:00/2013-04-18T15:53:00+02:00 transect 0 0 41.30371367 4.936571167 EPSG:4326 225420.0359 LINESTRING (7.636983333333 41.32418333333, 2.236159 41.283244) EPSG:4326 TURTLE_CBAR_0045_0001 2021-05-05 07:59:08 https://marineinfo.org/id/dataset/6403 Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea TURTLE_CBAR_0045_0001 TURTLE_CBAR_0045 2013-04-18T05:55:00+02:00 sample 0 0 41.3228 7.4984 EPSG:4326 POINT EPSG:4326 TURTLE_CBAR_0045_0002 2021-05-05 07:59:08 https://marineinfo.org/id/dataset/6403 Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea TURTLE_CBAR_0045_0002 TURTLE_CBAR_0045 2013-04-18T08:35:00+02:00 sample 0 0 41.322845 5.995345 EPSG:4326 POINT EPSG:4326 Occurrence extension: The Occurrence extension contain details regarding the sighted animals and include scientificName and the links to the World Register of Marine Species in scientificNameID. The EventID references the events as in the Event core. This table further provides information on the basisOfRecord and occurrenceStatus. EventID occurrenceID datasetID collectionCode basisOfRecord catalogNumber recordedBy occurrenceStatus scientificNameID scientificName kingdom scientificNameAuthorship TURTLE_CBAR_0043 AdL_TURTLE_CBAR_0001 https://marineinfo.org/id/dataset/6403 TURTLE_CBAR_13-17 HumanObservation AdL_TURTLE_CBAR_0001 Ilaria Campana | Miriam Paraboschi | Erica Ercoli | Erica absent urn:lsid:marinespecies.org:taxname:136999 Cheloniidae Animalia Oppel, 1811 TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 https://marineinfo.org/id/dataset/6403 TURTLE_CBAR_13-17 HumanObservation AdL_TURTLE_CBAR_0004 Antonella Arcangeli | Cristina Berardi | Lucilla Giulietti | Claudia Boccardi absent urn:lsid:marinespecies.org:taxname:136999 Cheloniidae Animalia Oppel, 1811 TURTLE_CBAR_0045_0001 AdL_TURTLE_CBAR_0005 https://marineinfo.org/id/dataset/6403 TURTLE_CBAR_13-17 HumanObservation AdL_TURTLE_CBAR_0005 Antonella Arcangeli | Cristina Berardi | Lucilla Giulietti | Claudia Boccardi present urn:lsid:marinespecies.org:taxname:137205 Caretta caretta Animalia Linnaeus, 1758 TURTLE_CBAR_0045_0002 AdL_TURTLE_CBAR_0006 https://marineinfo.org/id/dataset/6403 TURTLE_CBAR_13-17 HumanObservation AdL_TURTLE_CBAR_0006 Antonella Arcangeli | Cristina Berardi | Lucilla Giulietti | Claudia Boccardi present urn:lsid:marinespecies.org:taxname:137205 Caretta caretta Animalia Linnaeus, 1758 extendedMeasurementOrFact (eMoF) extension: The extendedMeasurementOrFact extension (eMoF) for survey or sighting data contains additional attributes and measurements recorded during the survey, such as those regarding the Research Vessel, environmental conditions, and/ or animal measurements. These attributes are linked to the Occurrence extension using the occurrenceID. The example dataset contain measurements regarding the sampling method; speed and height of the Research Vessel as platform; wind force; sighting distance; as well as the count and developmental stage of the biological entity. id occurrenceID measurementType measurementTypeID measurementValue measurementUnit measurementUnitID TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 BEAUFORT WIND FORCE http://vocab.nerc.ac.uk/collection/P01/current/WMOCWFBF 0 Beaufort scale TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 Platform height http://vocab.nerc.ac.uk/collection/P01/current/AHSLZZ01 29 Metres http://vocab.nerc.ac.uk/collection/P06/current/ULAA/ TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 Sampling method http://vocab.nerc.ac.uk/collection/Q01/current/Q0100003 visual observation from ferries TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 Speed of measurement platform relative to ground surface {speed over ground} by unspecified GPS system http://vocab.nerc.ac.uk/collection/P01/current/APSAGP01 23.291 Knots (nautical miles per hour) http://vocab.nerc.ac.uk/collection/P06/current/UKNT/ TURTLE_CBAR_0045_0001 AdL_TURTLE_CBAR_0005 Development stage of biological entity specified elsewhere http://vocab.nerc.ac.uk/collection/P01/current/LSTAGE01 TURTLE_CBAR_0045_0001 AdL_TURTLE_CBAR_0005 Count (in assayed sample) of biological entity specified elsewhere http://vocab.nerc.ac.uk/collection/P01/current/OCOUNT01 20 Metres http://vocab.nerc.ac.uk/collection/P06/current/ULAA/ TURTLE_CBAR_0045_0001 AdL_TURTLE_CBAR_0005 Sighting distance In addition to the measurements recorded by the example dataset, other measurements are also possible depending on the scope and aims of the survey project. The example dataset Incidental sea snake and turtle bycatch records from the RV Southern Surveyor voyage SS199510, Gulf of Carpentaria, Australia (Nov 1995) for example, contain information regarding the length and weight of the biological entity as follows: extendedMeasurementOrFact (eMoF) extension: id measurementID occurrenceID measurementType measurementTypeID measurementValue measurementUnit measurementUnitID SS199510-001 SS199510-001-length SS199510-001 Length http://vocab.nerc.ac.uk/collection/P01/current/OBSINDLX 1250 Millimetres http://vocab.nerc.ac.uk/collection/P06/current/UXMM SS199510-001 SS199510-001-weight SS199510-001 Weight http://vocab.nerc.ac.uk/collection/P01/current/SPWGXX01 800 Grams http://vocab.nerc.ac.uk/collection/P06/current/UGRM SS199510-002 SS199510-002-length SS199510-002 Length http://vocab.nerc.ac.uk/collection/P01/current/OBSINDLX 1630 Millimetres http://vocab.nerc.ac.uk/collection/P06/current/UXMM SS199510-002 SS199510-002-weight SS199510-002 Weight http://vocab.nerc.ac.uk/collection/P01/current/SPWGXX01 1477.7 Grams http://vocab.nerc.ac.uk/collection/P06/current/UGRM 8.0.0.9 Microbes biomass and diversity (example coming soon)   8.0.0.10 Phytoplankton biomass and diversity This example deals with encoding phytoplankton observation data, including environmental data, into Darwin Core. Extracts from the actual data set LifeWatch observatory data: phytoplankton observations by imaging flow cytometry (FlowCam) in the Belgian Part of the North Sea, are used as an example. Phytoplankton data should adhere to guidelines by Martin-Cabrera et al., 2022: Best practices and recommendations for plankton imaging data management: Ensuring effective data flow towards European data infrastructures. Version 1.. Specific guidelines for the Occurrence and eMoF tables are documented in this publication, and we emphasize important fields in this example. Event core: The Event core contains events at the different levels and are linked together with eventID and parentEventID. In this example,the dataset contains records pointing to the origin, the in-situ sampling position as well as a record referring to the ex-situ collection of living specimens. In this case, the the event type information is provided in type. The recommended practice for providing the countryCode is to use an ISO 3166-1-alpha-2 country code. If additional information regarding licencing is provided, these can be populated under rightsHolder and accessRights. The remaining Event core fields provide location data including datasetID and datasetName, locationID, waterBody, maximumDepthInMeters, minimumDepthInMeters, decimalLongitude, decimalLatitude, coordinateUncertaintyInMeters, geodeticDatum and footprintSRS. eventID parentEventID eventRemarks eventDate modified datasetID datasetName locationID waterBody country countryCode minimumDepthInMeters maximumDepthInMeters decimalLatitude decimalLongitude geodeticDatum coordinateUncertaintyInMeters footprintSRS TripNR3242 cruise 2017-05T13:18:00+00:00/2017-05T22:14:00+00:00 2021-10-21 15:52:00 https://marineinfo.org/id/dataset/4688 LifeWatch observatory data: phytoplankton observations… North Sea Belgium BE EPSG:4326 EPSG:4326 TripNR3242TripStationNR16781 TripNR3242 stationVisit 2017-05-08T20:44:00+00:00/2017-05-08T20:55:00+00:00 2021-10-21 15:52:00 https://marineinfo.org/id/dataset/4688 JN17_5 0 30.22 51.0131 1.90562 EPSG:4326 1.11 EPSG:4326 TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781 sample 2017-05-08T20:50:00+00:00 2021-10-21 15:52:00 https://marineinfo.org/id/dataset/4688 JN17_5 0 1 51.01203 1.90217 EPSG:4326 1.11 EPSG:4326 TripNR3242TripStationNR16781MidasTripActionID105598UW TripNR3242TripStationNR16781 sample 2017-05-08T20:50:00+00:00 2021-10-21 15:52:00 https://marineinfo.org/id/dataset/4688 JN17_5 3 3 51.01203 1.90217 EPSG:4326 1.11 EPSG:4326 Occurrence extension: The Occurrence extension contains data of each occurrence with an occurrenceID and is linked to the Event core with the eventID. The Occurrence extension should provide information on the basisOfRecord and occurrenceStatus. Scientific names and links to the World Register of Marine Species should be provided under scientificName and scientificNameID, respectively. Recommended best practice is to always use the term of MachineObservation for imaging datasets derived from imaging instruments like this example, Additionally, the fields identifiedBy and identificationVerificationStatus are crucial to indicate whether the data has been validated, and by whom. While not shown in this example, identificationReferences and associatedMedia are also recommended to document the citation or algorithm software used in identification, and the persistent URL of annotated images, respectively. We know this dataset was validated because of information provided in the abstract and the eMoF table below, “identified and counted by image analysis and normalised to a unit volume of water body, validated by human”. eventID occurrenceID modified basisOfRecord occurrenceStatus scientificNameID scientificName identifiedBy identificationVerificationStatus TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_(Pseudo-)pediastrum_5 2021-10-21 MachineObservation absent urn:lsid:marinespecies.org:taxname:160560 Hydrodictyaceae Flanders Marine Institute ValidatedByHuman TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus senarius_5 2021-10-21 MachineObservation present urn:lsid:marinespecies.org:taxname:148948 Actinoptychus senarius Flanders Marine Institute ValidatedByHuman TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus splendens_5 2021-10-21 MachineObservation present urn:lsid:marinespecies.org:taxname:148949 Actinoptychus splendens Flanders Marine Institute ValidatedByHuman TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus_5 2021-10-21 MachineObservation present urn:lsid:marinespecies.org:taxname:148947 Actinoptychus Flanders Marine Institute ValidatedByHuman extendedMeasurementOrFact (eMoF) extension: The eMoF extension contains the environmental and measurement information and data of each occurrence. This extension is also linked to the Event core using the eventID, and linked to the Occurrence extension table using the occurrenceID. The various measurements are populated with measurementID, measurementType, measurementTypeID, measurementUnit, measurementUnitID, measurementValue, measurementValueID, measurementAccuracy, measurementMethod, measurementDeterminedBy and measurementDeterminedDate. In the example dataset, the LifeWatch observatory data was compiled using imaging flow cytometry (FlowCam) to observe and identify phytoplankton in the Belgian Part of the North Sea and recorded a number of measurements including abundance, lifestages, sampling device information as well as environmental measurements such as water temperature, salinity and conductivity with accompanying vocabulary. id occurrenceID measurementType measurementTypeID measurementValue measurementValueID measurementUnit measurementUnitID measurementDeterminedBy measurementMethod TripNR3242 Platform Name http://vocab.nerc.ac.uk/collection/Q01/current/Q0100001/ Simon Stevin http://vocab.nerc.ac.uk/collection/C17/current/11SS/ Flanders Marine Institute TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus_5 Abundance of biological entity specified elsewhere per unit volume of the water body http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL01/ 2.24 specimens/L http://vocab.nerc.ac.uk/collection/P06/current/UCPL Flanders Marine Institute identified and counted by image analysis and normalised to a unit volume of water body, validated by human TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus senarius_5 Abundance of biological entity specified elsewhere per unit volume of the water body http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL01/ 1.12 specimens/L http://vocab.nerc.ac.uk/collection/P06/current/UCPL Flanders Marine Institute identified and counted by image analysis and normalised to a unit volume of water body, validated by human TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_(Pseudo-)pediastrum_5 Lifestage http://vocab.nerc.ac.uk/collection/P01/current/LSTAGE01/ adult http://vocab.nerc.ac.uk/collection/S11/current/S1116/ Flanders Marine Institute identified and counted by image analysis and normalised to a unit volume of water body, validated by human TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus senarius_5 Lifestage http://vocab.nerc.ac.uk/collection/P01/current/LSTAGE01/ adult http://vocab.nerc.ac.uk/collection/S11/current/S1116/ TripNR3242TripStationNR16781MidasTripActionID105598 Sampling device aperture diameter http://vocab.nerc.ac.uk/collection/Q01/current/Q0100012/ 0.4 meter http://vocab.nerc.ac.uk/collection/P06/current/ULAA/ Flanders Marine Institute TripNR3242TripStationNR16781MidasTripActionID105598 Sampling instrument name http://vocab.nerc.ac.uk/collection/Q01/current/Q0100002/ Planktonnet Apstein http://vocab.nerc.ac.uk/collection/L22/current/TOOL0978/ Flanders Marine Institute TripNR3242TripStationNR16781MidasTripActionID105598 Sampling net mesh size http://vocab.nerc.ac.uk/collection/Q01/current/Q0100015/ 55 micrometer http://vocab.nerc.ac.uk/collection/P06/current/UMIC/ Flanders Marine Institute TripNR3242TripStationNR16781MidasTripActionID105598UW Conductivity of the water body http://vocab.nerc.ac.uk/collection/P01/current/CNDCZZ01/ 3.916 Siemens per metre http://vocab.nerc.ac.uk/collection/P06/current/UECA Flanders Marine Institute Electrical conductivity of the water body by thermosalinograph, based on the UnderWaySystem of the ship TripNR3242TripStationNR16781MidasTripActionID105598UW Practical salinity of the water body http://vocab.nerc.ac.uk/collection/P01/current/PSALPR01/ 34.295 Grams per kilogram http://vocab.nerc.ac.uk/collection/P06/current/UGKG/ Flanders Marine Institute Practical salinity of the water body based on water from the UnderWaySystem of the ship TripNR3242TripStationNR16781MidasTripActionID105598UW Temperature of the water body http://vocab.nerc.ac.uk/collection/P01/current/TEMPPR01/ 11.881 Degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA/ Flanders Marine Institute Temperature of the water body based on water from the UnderWaySystem of the ship 8.0.0.11 Seagrass cover and composition The structure of the Event, Occurrence and extendedMeasurementOrFact extensions for Seagrass Cover & Composition is based on community feedback organised through the the Scientific Committee on Oceanic Research (SCOR): Coordinated Global Research Assessment of Seagrass System (C-GRASS). We acknowledge the work that the C-grass SCOR work group has done to develop a proposed scheme for completing Seagrass related extension files. Here encode seagrass survey data into Darwin Core according to the ENV-DATA approach and using sections of the actual data set of Seagrass Monitoring at Chengue Bay, Colombia as an example dataset. Event core: The Event core table is created by extracting all events and attributes. All events are linked together using eventID and parentEventID. eventDate is populated at the transect level with the recommended format that conforms to ISO 8601-1:2019. habitat is populated as a category or description of the habitat in which the event occurred. Additional fieldNotes can also be provided if applicable. The recommended best practice for countryCode is to use an ISO 3166-1-alpha-2 country code. The remaining Event core fields comprise of location data including maximumDepthInMeters, minimumDepthInMeters, decimalLongitude, decimalLatitude, coordinateUncertaintyInMeters, footprintWKT and footprintSRS. Addtionally in the Event core, it is recommended to further include information regarding license, rightsHolder, bibliographicCitation, institutionID, datasetID, institutionCode and datasetName. eventID parentEventID eventDate habitat fieldNotes countryCode minimumDepthInMeters maximumDepthInMeters decimalLatitude decimalLongitude coordinateUncertaintyInMeters footprintWKT footprintSRS USBsg-chengue-pastocoral 2019-05-13 seagrass no notes CO 0.8 2 11.32021806 -74.12753684 10 POLYGON ((-74.1273259763024 11.320475512862,-74.1272182449138 11.3200031521579,-74.1277187815195 11.319600402505,-74.1278115962543 11.3205666701908,-74.1274900594943 11.3206860034213,-74.1273259763024 11.320475512862)) EPSG:4326 USBsg-chengue-pastomanglar 2019-05-14 seagrass no notes CO 0.8 0.8 11.31977189 -74.12536879 10 POLYGON ((-74.1273259763024 11.320475512862,-74.1272182449138 11.3200031521579,-74.1277187815195 11.319600402505,-74.1278115962543 11.3205666701908,-74.1274900594943 11.3206860034213,-74.1273259763024 11.320475512862)) EPSG:4326 USBsg-chengue-pastocoral-SquidPopTransect1 USBsg-chengue-pastocoral 2019-05-13 seagrass no notes CO 0.8 2 11.32039927 -74.12737404 50 POINT (-74.1273740410759 11.3203992721869) EPSG:4326 USBsg-chengue-pastocoral-SquidPopTransect2 USBsg-chengue-pastocoral 2019-05-13 seagrass no notes CO 0.8 2 11.32027662 -74.1273989 50 POINT (-74.1273989021655 11.3202766241445) EPSG:4326 Occurrence extension: The Occurrence extension table contain data for each occurrence with an occurrenceID and is linked to the Event core with the eventID. This table should provide information on the basisOfRecord and occurrenceStatus. Scientific names and links to the World Register of Marine Species should be provided under scientificName and scientificNameID, respectively. If a species was identified by an expert, the field identifiedBy can be populated. If the species is well-known by another common name, this name can be provided under vernacularName. eventID occurrenceID basisOfRecord occurrenceStatus scientificNameID scientificName USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt HumanObservation present urn:lsid:marinespecies.org:taxname:374720 Thalassia testudinum USBsg-chengue-pastomanglar USBsg-chengue-manglar-tt HumanObservation present urn:lsid:marinespecies.org:taxname:374720 Thalassia testudinum USBsg-chengue-pastocoral-SquidPopTransect1 USBsg-chengue-pastocoral-fish-001 HumanObservation present urn:lsid:marinespecies.org:taxname:158815 Halichoeres bivittatus USBsg-chengue-pastocoral-SquidPopTransect1 USBsg-chengue-pastocoral-fish-002 HumanObservation present urn:lsid:marinespecies.org:taxname:158932 Lactophrys triqueter extendedMeasurementOrFact (eMoF) extension: The eMoF table contains the measurement information and data of each occurrence. This extension is also linked to the Event core using the eventID, and linked to the Occurrence table using the occurrenceID. The various measurements are populated with measurementType, measurementTypeID, measurementUnit, measurementUnitID, measurementValue, measurementValueID, measurementAccuracy, measurementMethod, measurementDeterminedBy and measurementDeterminedDate. The example dataset of Seagrass Monitoring at Chengue Bay, Colombia recorded a number of measurements and can be used as an example of how to populate the respective fields. Other possible measurementTypes not captured in this example may include percent cover, canopy height, and of course any other biotic/abiotic measurements. eventID occurrenceID measurementID measurementType measurementTypeID measurementValue measurementUnit measurementUnitID USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-PhyQ01 WaterTemp http://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/ 29.23 Degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-PhyQ02 Salinity http://vocab.nerc.ac.uk/collection/P01/current/SSALSL01/ 36 Parts per thousand http://vocab.nerc.ac.uk/collection/P06/current/UPPT/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-PhyQ03 Dissolved oxygen http://vocab.nerc.ac.uk/collection/P01/current/DOXYSE02/ 6.58 Milligrams per litre http://vocab.nerc.ac.uk/collection/P06/current/UMGL/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1C1-shoot-01 Shoot Density http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL02/ 128 Number per square metre http://vocab.nerc.ac.uk/collection/P06/current/UPMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1C1-leafLenght-01 Leaf Length http://vocab.nerc.ac.uk/collection/P01/current/OBSMAXLX/ 18 Centimetres http://vocab.nerc.ac.uk/collection/P06/current/ULCM/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N1-DryBiomass Total Dry Biomass http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.32055 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N1-biomassGL Dry biomass of green leaves http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.05575 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N1-biomassNGL Dry biomass of non green leaves http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.1469 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N1-biomassSH Dry biomass of the shoots http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.07625 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N2-biomassR Dry biomass of the roots http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.0385 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N2-biomassRIZ Dry biomass of the rizome http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.02725 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N2-biomassOTH Dry biomass of other seagrass species http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ 8.0.0.12 Zooplankton biomass and diversity Here we will encode zooplankton observation and environmental data into Darwin Core. Extracts from the actual dataset LifeWatch observatory data: zooplankton observations by imaging (ZooScan) in the Belgian Part of the North Sea, are used as an example. As with the phytoplankton example, zooplankton data should also adhere to guidelines by Martin-Cabrera et al., 2022: Best practices and recommendations for plankton imaging data management: Ensuring effective data flow towards European data infrastructures. Version 1.. These Best Practices indicate that for imaging data like this example, the fields identificationVerificationStatus and identifiedBy are crucial to know whether the data has been validated, and by whom. identificationReferences is used to document the citation or algorithm software used in identification. When possible, associatedMedia should also be populated with the persistent URL of annotated images. Event core: The Event core contains events at the different levels and are linked together with eventID and parentEventID. In this example,the dataset contains records pointing to the origin, the in-situ sampling position as well as a record referring to the ex-situ collection of living specimens. In this case, the the event type information is provided in type. The recommended practice for providing the countryCode is to use an ISO 3166-1-alpha-2 country code. If additional information regarding licencing is provided, these can be populated under rightsHolder and accessRights. The remaining Event core fields provide location data including datasetID and datasetName, locationID, waterBody, maximumDepthInMeters, minimumDepthInMeters, decimalLongitude, decimalLatitude, coordinateUncertaintyInMeters, geodeticDatum and footprintSRS. eventID parentEventID eventRemarks eventDate modified datasetID datasetName locationID waterBody country minimumDepthInMeters maximumDepthInMeters decimalLatitude decimalLongitude geodeticDatum footprintSRS TripNR2547 cruise 2013-07-22T06:58:00+00:00/2013-07-22T16:58:00+00:00 2021-06-23 14:54:00 https://marineinfo.org/id/dataset/4687 LifeWatch observatory data: zooplankton observations… Belgian Part of the North Sea Belgium EPSG:4326 EPSG:4326 TripNR2547TripStationNR9781 TripNR2547 stationVisit 2013-07-22T07:13:00+00:00/2013-07-22T07:26:00+00:00 2021-06-23 14:54:00 https://marineinfo.org/id/dataset/4687 LifeWatch observatory data: zooplankton observations… 130 0 13.4 51.27083333 2.905 EPSG:4326 EPSG:4326 TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781 sample 2013-07-22T07:22:00+00:00 2021-06-23 14:54:00 https://marineinfo.org/id/dataset/4687 LifeWatch observatory data: zooplankton observations… 130 0 0 51.2687318 2.901797 EPSG:4326 EPSG:4326 TripNR2547TripStationNR9781MidasTripActionID23024UW TripNR2547TripStationNR9781 sample 2013-07-22T07:22:00+00:00 2021-06-23 14:54:00 https://marineinfo.org/id/dataset/4687 LifeWatch observatory data: zooplankton observations… 130 3 3 51.2687318 2.901797 EPSG:4326 EPSG:4326 Occurrence extension: The Occurrence extension contains data of each occurrence with an occurrenceID and is linked to the Event core with the eventID. The Occurrence extension should provide information on the basisOfRecord and occurrenceStatus. Scientific names and links to the World Register of Marine Species should be provided under scientificName and scientificNameID, respectively. eventID occurrenceID modified basisOfRecord occurrenceStatus scientificNameID scientificName identifiedBy identificationVerificationStatus identificationReferences TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Amphipoda_sub2_130 2021-06-22 MachineObservation absent urn:lsid:marinespecies.org:taxname:1135 Amphipoda Jonas Mortelmans ValidatedByHuman RandomForest TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Annelida_sub2_130 2021-06-22 MachineObservation present urn:lsid:marinespecies.org:taxname:882 Annelida Jonas Mortelmans ValidatedByHuman RandomForest TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Anomura_sub2_130 2021-06-22 MachineObservation absent urn:lsid:marinespecies.org:taxname:106671 Anomura Jonas Mortelmans ValidatedByHuman RandomForest TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Appendicularia_sub2_130 2021-06-22 MachineObservation absent urn:lsid:marinespecies.org:taxname:146421 Appendicularia Jonas Mortelmans ValidatedByHuman RandomForest extendedMeasurementOrFact (eMoF) extension: The eMoF extension table contains the measurement information and data of each occurrence. This extension is also linked to the Event core using the eventID, and linked to the Occurrence table using the occurrenceID. The various measurements are populated with measurementType, measurementTypeID, measurementUnit, measurementUnitID, measurementValue, measurementValueID, measurementAccuracy, measurementMethod, measurementDeterminedBy and measurementDeterminedDate. The example dataset of LifeWatch observatory data: zooplankton observations by imaging (ZooScan) in the Belgian Part of the North Sea recorded some ENV-DATA and organism measurements the can be used as an example of how to populate the respective fields, including conductivity of the water body; concentration of chlorophyll-a per unit volume of the water body; sampling instrument name; sampling net mesh size; lifestage of the organism observed; and abundance of the organism observed. id occurrenceID measurementType measurementTypeID measurementValue measurementValueID measurementUnit measurementUnitID measurementDeterminedBy measurementMethod TripNR3256TripStationNR17157MidasTripActionID106326 Sampling instrument name http://vocab.nerc.ac.uk/collection/Q01/current/Q0100002/ Planktonnet WP2 <http://vocab.nerc.ac.uk/collection/L22/current/TOOL0979/ TripNR3256TripStationNR17157MidasTripActionID106326 Sampling net mesh size http://vocab.nerc.ac.uk/collection/Q01/current/Q0100015/ 200 micrometer http://vocab.nerc.ac.uk/collection/P06/current/UMIC/ TripNR3529TripStationNR19242MidasTripActionID109631UW Conductivity of the water body http://vocab.nerc.ac.uk/collection/P01/current/CNDCZZ01/ 4.05 Siemens per metre http://vocab.nerc.ac.uk/collection/P06/current/UECA/ Flanders Marine Institute Electrical conductivity of the water body by thermosalinograph, based on the UnderWaySystem of the ship TripNR3529TripStationNR19243MidasTripActionID109634 Concentration of chlorophyll-a per unit volume of the water body http://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/ 1.42 Micrograms per litre http://vocab.nerc.ac.uk/collection/P06/current/UGPL/ Flanders Marine Institute Concentration of chlorophyll-a per unit volume of the water body [particulate >GF/F phase] by filtration, acetone extraction and high performance liquid chromatography (HPLC) TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Annelida_sub2_130 Lifestage http://vocab.nerc.ac.uk/collection/P01/current/LSTAGE01/ unspecified http://vocab.nerc.ac.uk/collection/S11/current/S1152/ Flanders Marine Institute identified and counted by image analysis and normalised to a unit volume of water body, validated by human TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Annelida_sub2_130 Abundance of biological entity specified elsewhere per unit volume of the water body http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL01/ 0.50 specimens/m³ http://vocab.nerc.ac.uk/collection/P06/current/UPMM/ Flanders Marine Insitute identified and counted by image analysis and normalised to a unit volume of water body, validated by human "],["other_data_types.html", "Other data types 8.1 Multimedia data (Acoustic, Imaging) 8.2 Habitat data 8.3 Tracking data", " Other data types Content Multimedia data Habitat data Tracking data 8.1 Multimedia data (Acoustic, Imaging) If you have multimedia data (e.g. images, acoustic, video) that you want to publish alongside your dataset, you can do so by documenting information in the associatedMedia field in your Occurrence table. The usage of this field requires the media in question to be hosted somewhere with a persistent URL of the annotated image(s), e.g., a publication, museum database, etc. Then you simply copy this link to the associatedMedia field for a given occurrence. You may also include a concatenated list if you need to list multiple sources. While there are Core types and extensions (e.g., Audubon Core and Simple Multimedia extension) designed for image, video, and audio files, these data file types are not currently processed by OBIS. Thus for now we recommended to include links in the associatedMedia field. Stay tuned however, as OBIS is looking to incorporate the Simple Multimedia extension. Martin-Cabrera et al., 2022 have produced a best practices for datasets with plankton imaging data that can also apply to acoustic and other imaging data types. Following their guidelines, we strongly recommend including the following terms in your Occurrence table for either of these data types: basisOfRecord - recommended best practice is to always use the term of MachineObservation, especially for imaging datasets derived from imaging instruments identifiedBy - name(s) of persons involved in verifying taxon identification, particularly if automatic identification was made by a software and then validated by a human identificationVerificationStatus - categorical indicator for the extent of taxonomic identification verification. Recommended to use PredictedByMachine or ValidatedByHuman identificationReferences - references used in identification (e.g. citation and version of software or algorithm that identified taxa) The fields identifiedBy and identificationVerificationStatus are crucial to indicate whether an observation has been validated, and by whom. These fields allow users to filter data when basisOfRecord = MachineObservation, so that they can be confident in the taxonomic identification when identificationVerificationStatus = ValidatedByHuman (Martin-Cabrera et al., 2022). The identificationVerificationStatus also has implications for documenting grouped occurrences, particularly for planktonic organisms. For example, if all identifications for a specific taxon in a sample has the same identificationVerificationStatus, you ony need one occurrence record with one associated unique occurrenceID. Then, the summed count or concentration for that taxon can be reported in the eMoF as, e.g. “Abundance of biological entity specified elsewhere per unit volume of the water body”. However, if individuals of a taxon have more than one identificationVerificationStatus (e.g. ValidatedByHuman and PredictedByMachine), you will need two occurrence records with associated unique occurrenceIDs. The two records will document the same taxon with different identificationVericationStatus, and with different summed concentrations of abundance reported in the eMoF. Example Resources: Martin-Cabrera et al. (2022) have created a best practices document for plankton imaging data that you can reference. To see an example imaging dataset implementing these best practices, see the supplementary material of Establishing Plankton Imagery Dataflows Towards International Biodiversity Data Aggregators. Data originating from ROV (Remote Operating Vehicle) observations may require additional processing. Ocean Networks Canada (ONC) is developing a pipeline for publishing ROV data to OBIS. ROV datasets should have: An Event core that documents the hierarchical nature of ROV dives (e.g., ROV dives nested within a cruise) Occurrence and eMoF extensions to record taxonomic and other measurement data e.g., from sensors. ONC’s pipeline outlines the importance of including identifiedBy in order to vet taxon identifications by experts. 8.2 Habitat data Event Core is perfect for enriching OBIS with interpreted information such as biological community, biotope or habitat type (collectively referred to as ‘habitats’). However, the unconstrained nature of the terms measurementTypeID, measurementValueID, and measurementUnitID leads to a risk that habitats measurements are structured inconsistently within the Darwin Core Archive standard and as a result, are not easily discoverable, understood or usable. As a result, members of the European Marine Observation and Data Network (EMODnet) Seabed Habitats and Biology thematic groups have produced a technical report Duncan et al. (2021) that provides guidance on using the Darwin Core eMoF extension to submit habitat data to OBIS, following the ENV-DATA approach and using Seabed Habitats as a use case. Note that the guidelines and structuring approach outlined in this document has not yet been approved or accepted at the global level and is only a recommended approach as agreed upon by EMODnet Seabed Habitats, EMODnet Biology, and OBIS. The implementation at the EurOBIS level may be considered a pilot. The overarching principles are summarised here. Note that because of the numerous classification systems and priority habitat lists in existence, it is not possible to point to a single vocabulary for populating each of measurementTypeID, measurementValueID and measurementUnitID, as for other measurement types, so below are the types of information to include, with an example, as recommended by Duncan et al. (2021): measurementTypeID: A machine-readable URI or DOI reference describing the (version of the) classification system itself. For example: https://dd.eionet.europa.eu/vocabulary/biodiversity/eunishabitats/ measurementValueID: If available, a machine-readable URI describing the habitat class in “measurementValue”. For example: https://dd.eionet.europa.eu/vocabulary/biodiversity/eunishabitats/A5.36 measurementUnitID: null because habitat types are unitless. Please consult the Duncan et al. (2021) technical report (title: A standard approach to structuring classified habitat data using the Darwin Core Extended Measurement or Fact Extension; note you must refine search to Technical Reports from 2021 to identify this report) for more details, including: how to handle a single event with multiple habitat measurements recommended vocabularies and terms for common habitat classification systems example eMoF table For filling measurementType with habitat-related data and/or the dwc:habitat column, you should reference the NERC vocabulary search. While the Coastal and Marine Ecological Classification Standard (CMECS) and the Environment Ontology (ENVO) also contain habitat vocabularies, OBIS recommends the use of NERC vocabulary. If other vocabularies are used, please provide the NERC vocabulary equivalent as additional records in the eMoF table. 8.3 Tracking data Encoding Tracking data into Darwin Core follows the same standards as that of survey/sighting data. Tracking data should additionally indicate the accuracy in latitudinal and longitudinal measurements received from the positioning system, grouped by location accuracy classes, recorded in the coordinateUncertaintyInMeters field. The Ocean Tracking Network (OTN) has developed some guidelines for formatting this type of data in Darwin Core. We summarize the main points below. Using Event core for tracking data is recommended as there can be multiple events involved in tracking an organism. There are capture/tag and release events, receiver deployment events, and detection occurrences. Note that the capture and release of an organism are not considered to be distinct Occurrence records because they are not natural occurrences. Thus, in the Event core table you may record unique events for: The capture of an animal The release of an animal The deployment of a listening (or receiver) station Information pertaining to a specific individual is linked by a unique organismID. You can use eventIDs associated with a receiver to record detection occurrences in the Occurrence table. One organism may then have multiple occurrences (and thus multiple occurrenceIDs), but the same organismID. Any measurements for an organism taken during capture can be recorded in the extendedMeasurementsOrFact extension, linked to the core by the capture event’s eventID as well as the unique organismID. For more details, see the DwC guidelines for biologging. Extracts from the extendedMeasurementOrFact Extension (eMoF) of the actual dataset Ningaloo Outlook turtle tracking of Green turtles (Chelonia mydas), Western Australia (2018-present), are shown as an example tracking dataset, following ARGOS Location class codes. extendedMeasurementOrFact (eMoF) extension: id measurementID occurrenceID measurementType measurementValue measurementValueID 2347540 2347540-argosclass 2347540 ARGOS Location Class A http://vocab.nerc.ac.uk/collection/R05/current/A 2347541 2347541-argosclass 2347541 ARGOS Location Class B http://vocab.nerc.ac.uk/collection/R05/current/B 2347542 2347542-argosclass 2347542 ARGOS Location Class 2 http://vocab.nerc.ac.uk/collection/R05/current/2 2347543 2347543-argosclass 2347543 ARGOS Location Class 3 http://vocab.nerc.ac.uk/collection/R05/current/3 "],["data_qc.html", "9 Data quality control 9.1 Why are records dropped? 9.2 How to conduct Quality Control", " 9 Data quality control OBIS ignores records that do not meet a number of standards. For example, all species names need to be matched against an authoritative taxonomic register, such as the World Register of Marine Species. In addition, quality is checked against the OBIS required fields as well as against any impossible values. OBIS checks, rejects and reports the data quality back to the OBIS nodes, but never change records. The OBIS tier 2 nodes are responsible for the data quality and communicate errors back to the data providers. A number of QC tools are developed to help data providers and OBIS nodes: QC tool for species names QC tool for geography and data format For specific concerns regarding quality control checks or issues, please submit a GitHub ticket to the OBIS QC repository. 9.1 Why are records dropped? Records can be dropped and therefore not published with your dataset for a number of reasons, including: The species is not marine The ‘scientificName’ or scientificNameID did not match with WoRMS Issues with coordinates: No coordinates given decimalLatitude or decimalLongitude out of range The coordinate is zero For each dataset published, a quality report is generated where the number of dropped records and other quality issues will be flagged. Such reports can also be found when searching for data in OBIS. For example, if we searched for ‘Crustacea’ records, the following data quality report is given: Number of Crustacean records dropped We can see that >110,222 Crustacean records have been dropped, mostly due to records missing coordinates or species being flagged as non-marine. Because species are determined as being marine by WoRMS, we acknowledge that sometimes species are marked as not_marine erroneously.For specific advice on this topic, see the common QC issues page. To minimize the number of records dropped, be careful when formatting your data so that you are meeting the requirements. 9.2 How to conduct Quality Control Once you have formatted your data for OBIS, or have received a formatted dataset, it is important to run quality control checks before publishing the dataset on the IPT. The following is a list of various tools you can use to help you perform quality checks on your data: R package obistools EMODnet Biocheck Web UI built on obistools. This tool requires your dataset to be published on an IPT (e.g., a test IPT such as https://ipt.gbif.org/ where your dataset will not be harvested by GBIF or OBIS). Note you are required to have a login to access an IPT R package Lifewatch data services The US Integrated Ocean Observing System Standardizing Marine Bio Data Guide WoRMS taxon match tool Other WoRMS web services, incl. taxon match Excel Conditional Formatting tool Excel > Home > Conditional Formating > Highlight cells Rules > Duplicate values… GBIF data validator Python library for OBIS QC developed by Canadian Integrated Ocean Observing System R package and function Hmisc:: describe Can give important summary statistics and identify numbers that don’t match Conducting QC with obistools Installing obistools requires the devtools package. Use the following code to install both packages: install.packages("devtools") devtools::install_github("iobis/obistools") If you have difficulty installing obistools, please try updating your R packages, in particular the vctrs package. This can be done in RStudio in the Packages tab (“update” button) or by using the update.packages() command (you can choose which packages to update). If you cannot install obistools please reach out to helpdesk@obis.org and we will help you. To use obistools to conduct quality control, you can follow the general order below. Please see the obistools GitHub for examples of how to use the functions. Check that the taxa match with WoRMS obistools::match_taxa Check that all required fields are present in the occurrence table obistools::check_fields Check coordinates Plot them on a map to identify any points that appear outside the scope of the dataset obistools::plot_map. Using obistools:plot_map_leaflet() will additionally allow you to identify the row number for a particular data point. Check that points are not on land obistools::check_onland Ensure depth ranges are valid obistools::check_depth Check that the identifiers are present, unique, and appropriately correspond with each other obistools::check_eventids. You should also check the uniqueness of the occurrenceID field (e.g. using Hmisc::describe or simple code like length(occur$occurrenceID) == length(unique(occur$occurrenceID))) Ensure all eventIDs in extensions have matching eventIDs in the core table obistools::check_extension_eventids Check that eventDate is formatted properly obistools::check_eventdate Check for statistical outliers or other anomalies with Hmisc (below) QC with R package Hmisc The R package Hmisc has the function describe which can help you identify any discrepancies or outliers in your dataset. It will summarize each of your variables for a given data field. This can help you quickly identify any missing data and ensure the number of unique IDs is correct. For example, in an Occurrence table with 1000 records, there should be 1000 unique occurrenceIDs. library(Hmisc) library(Hmisc) data<-read.csv("example_data_occur.csv") describe(data) 12 Variables 407 Observations ------------------------------------------------------------------------------------------------------------------ CollectionCode n missing distinct value 407 0 1 BIOFUN1 Value BIOFUN1 Frequency 407 Proportion 1 ------------------------------------------------------------------------------------------------------------------ eventID n missing distinct 407 0 27 lowest : BIOFUN1_BF1A01 BIOFUN1_BF1A02 BIOFUN1_BF1A03 BIOFUN1_BF1A04 BIOFUN1_BF1A05 highest: BIOFUN1_BF1M3 BIOFUN1_BF1M4 BIOFUN1_BF1M6 BIOFUN1_BF1M8 BIOFUN1_BF1M9 ------------------------------------------------------------------------------------------------------------------ occurrenceID n missing distinct 407 0 407 lowest : CSIC_BIOFUN1_1 CSIC_BIOFUN1_10 CSIC_BIOFUN1_100 CSIC_BIOFUN1_101 CSIC_BIOFUN1_102 highest: CSIC_BIOFUN1_95 CSIC_BIOFUN1_96 CSIC_BIOFUN1_97 CSIC_BIOFUN1_98 CSIC_BIOFUN1_99 This video shows how to use both obistools and Hmisc to conduct QC checks in R. "],["dataquality.html", "9.3 Data quality flags", " 9.3 Data quality flags As you are following the guidelines in this manual to format data, it is important to consider the potential quality flags that could be produced when your dataset is published to OBIS. OBIS performs a number of automatic quality checks on the data it receives. This informs data users of any potential issues with a dataset they may be interested in. A complete list of flags can be found here but broadly speaking potential flags relate to issues with: Location - coordinates Event time - start, end dates Depth values out of range Taxonomic WoRMS name matching Non-marine or terrestrial When you are filling these fields it is important to double check that correct coordinates are entered, eventDates are accurate and in the correct format, and depth values are in meters. Records may be rejected and not published with your dataset if the quality does not meet certain expectations. In other cases quality flags are attached to individual occurrence records. We acknowledge that sometimes you may encounter a QC flag for data that is accurate. For example, you may document a depth value that gets flagged as DEPTH_OUT_OF_RANGE. Sometimes this occurs because your measured depth value is more accurate than the GEBCO bathymetry data which OBIS bases its depth data on. In these cases, you can ignore the flag, but we recommend adding a note in eventRemarks, measurementRemarks,or occurrenceRemarks. The checks we perform as well as the associated flags are documented here. QC Flags in downloaded data There are several ways to inspect the quality flags associated with a specific dataset or any other subset of data. Data downloaded through the mapper and the R package will include a column named flags which contains a comma separated list of flags for each record. In addition, the data quality panel on the dataset and node pages has a flag icon which can be clicked to get an overview of all flags and the number of records affected. This table includes quality flags, but also annotations from the WoRMS annotated names list. When OBIS receives a scientific name which cannot be matched with WoRMS automatically, it is sent to the WoRMS team. The WoRMS team will then annotate the name to indicate if and how the name can be fixed. Documentations about these annotations will be added here soon. Clicking any of these flags will take you to a table showing the affected records. For example, this is a list of records from a single dataset which have the no_match flag, indicating that no LSID or an invalid LSID was provided, and the name could not be matched with WoRMS. The column originalScientificName contains the problematic names, as scientificName is used for the matched name. At the top of the page there’s a button to open the occurrence records in the mapper where they can be downloaded as CSV. The occurrence table also has the flags column, so when inspecting non matching names for example it’s easy to check if the names at hand have any WoRMS annotations: Inspecting QC flags with R Inspecting flags using R is also very easy. The example below fetches the data from a single dataset, and lists the flags and the number of records affected. Notice that the occurrence() call has dropped = TRUE to make sure that any dropped records are included in the results: library(robis) library(tidyr) library(dplyr) # fetch all records for a dataset df <- occurrence(datasetid = "f3d7798e-7bf2-4b85-8ed4-18f2c1849d7d", dropped = TRUE) # unnest flags df_long <- df %>% mutate(flags = strsplit(flags, ",")) %>% unnest(flags) # get frequency per flag data.frame(table(df_long$flags)) Var1 Freq 1 depth_exceeds_bath 78 2 no_accepted_name 17 3 no_depth 5 4 no_match 138 5 not_marine 2 6 on_land 1 7 worms_annotation_await_editor 5 8 worms_annotation_reject_ambiguous 2 9 worms_annotation_reject_habitat 2 10 worms_annotation_todo 9 11 worms_annotation_unresolvable 7 This second example creates a list of annotated names for a dataset: library(robis) library(dplyr) library(stringr) # fetch all records for a dataset df <- occurrence(datasetid = "f3d7798e-7bf2-4b85-8ed4-18f2c1849d7d", dropped = TRUE) # only keep WoRMS annotations and summarize df %>% select(originalScientificName, flags) %>% mutate(flags = strsplit(flags, ",")) %>% unnest(flags) %>% filter(str_detect(flags, "worms")) %>% group_by(originalScientificName, flags) %>% summarize(records = n()) originalScientificName flags records <chr> <chr> <int> 1 Alcyonidium fruticosa worms_annotation_reject_habitat 1 2 Apicularia (Thapsiella) rudis sp. worms_annotation_unresolvable 1 3 Arcoscalpellum vegae worms_annotation_unresolvable 1 4 Balanus evermanni worms_annotation_await_editor 1 5 Chloramidae worms_annotation_reject_ambiguous 2 6 Cleippides quadridentatus worms_annotation_todo 1 7 Enhydrosoma hoplacantha worms_annotation_reject_habitat 1 8 Hippomedon setosa worms_annotation_unresolvable 1 9 Leionucula tenuis worms_annotation_await_editor 1 10 Ophiocten borealis worms_annotation_todo 1 11 Ophiopholis gracilis worms_annotation_todo 1 12 Priapulus australis worms_annotation_await_editor 1 13 Primnoella residaeformis worms_annotation_unresolvable 1 14 Robulus orbigny worms_annotation_unresolvable 1 15 Tetraxonia worms_annotation_unresolvable 2 16 Tmetonyx barentsi worms_annotation_await_editor 2 17 Triaxonida worms_annotation_todo 6 "],["mofreports.html", "9.4 How To Use MoF Report and Tool", " 9.4 How To Use MoF Report and Tool A MEASUREMENT TYPES dataset report has been added regarding currently used measurementType and associated measurementTypeID(s), located near the bottom of the individual dataset pages (if measurementType in use for the dataset). This new dataset report was derived from this MoF statistics report https://r.obis.org/mof/ and this active filtering MoF tool https://mof.obis.org/. Important note: the mof filtering tool should NOT be used as a resource for mapping vocabularies to measurements and parameters! It is a tool designed to assist node managers in finding any incorrect mappings within their datasets. For assistance with mapping vocabularies, see the vocabulary section of this Manual. To more easily locate the datasets within your node that may have possible measurementTypeID issues, use the MoF Statistics page: https://r.obis.org/mof/. This contains the list of Nodes currently using measurementType/measurementValue/measurementUnit with counts and percentage missing for the associated ID(s). If there is a node in that list that you are interested in locating, searching for, and possibly fixing MoF issues, select the Node from the list, then select a dataset (displaying a high percentage of missing ID(s)), and scroll down to the MEASUREMENT TYPE report Example, selected OBIS USA, then selected Florida Keys Reef Visual Census 1994, and scrolled down to MEASUREMENT TYPES section: To locate other datasets using these MEASUREMENT TYPES, use this active filtering MoF tool https://mof.obis.org, sort by measurementType (click column header) and scroll to measurementType(s) of interest For MEASUREMENT TYPE “Number of species observed during time period” has only one entry, which is missing associated ID. To see which datasets are using the listed measurementType, measurementTypeID combination, click on the number of records which is the last column. All are from OBIS USA. For MEASUREMENT TYPE “fish length” … To see which datasets are using this also listed measurementType, measurementTypeID combination, click on the number of records which is the last column. There are two records for fish length, one missing an ID and the other using S06, which may not be the preferred ID for this measurementType: Also, while scrolling through this report, you may notice something you would like to further research, click the record count value to see a list of datasets and associated node(s) using this noted type/ID. NOTE: Current USE does not indicate CORRECT use: To see BODC label for the provided ID, click the Find button, second last column: This is showing a different label from the (variety of) measurementType provided. To see which datasets are using a specific measurementType / ID combination, click the records count, last column: Things you are looking to clean up: If measurementTypeID is empty this should be updated. If the same measurementType (with same meaning/purpose) is using multiple measurementTypeIDs, these should be fixed to a single, preferred BODC vocab value. "],["lifewatch_qc.html", "9.5 Geographic and data format quality control", " 9.5 Geographic and data format quality control These Data validation and QC services are available on the LifeWatch portal at http://www.lifewatch.be/data-services. 9.5.0.1 Geographical service This service allows to upload a file and to plot the listed coordinates on a map. Using this web service does not require knowledge of GIS. This service allows a visual check of the available locations and makes it possible to easily identify points on land or outside the scope or study area. Geographic data are essential for OBIS and the experience is that a lot of these data is incomplete or contains errors. A visual check of the position of the sampling locations is thus a simple way of filtering out obvious errors and improving the data quality. Latitude and longitude need to be in WGS84, decimal degrees. This format is also necessary for the OBIS Schema and for uploading the dataset to IPT (Darwin Core). 9.5.0.2 OBIS data format validation This is the most extensive check currently available and is available for data that are structured according to the OBIS Schema. This validation service checks the following items: Are all mandatory fields completed, what are the missing fields? Are the coordinates in the correct format (decimal degrees, taking into account the minimum and maximum possible values)? Are the sampling points on land or in water? Is the information in the date-fields valid (e.g. month between 1-12)? Can the taxon name be matched with WoRMS? This tool undertakes several actions simultaneously. In a first step, this data service allows you to map your own column headers to the field names used in the OBIS Schema. When you then run the format validation service, the following actions are performed: A check of the mandatory fields of the OBIS Scheme. If mandatory fields would be missing, these will be listed separately, so you can complete them. Without these fields, the dataset cannot be accepted by the OBIS node. A listing of all the optional fields of the OBIS Scheme that are available in your file. Validation of the content of a number of fields: Latitude & longitude: Are the values inside the world limit? (yes/no); Are the values different from zero? (yes/no); Are the values situated in the marine environment (sea/ocean) (=prerequisite of a marine dataset)? (yes/no) Date-related fields: Do the year-month-day fields form a valid date? (yes/no) Do the start- and end-date fields form a valid date? (yes/no) Scientific name: Is the scientific name available in WoRMS? (yes/no) When yes: Indication whether taxon is marine or not Indication whether taxon name is valid or not Indication of the taxonomic rank After matching with WoRMS, the report gives a brief overview containing: the number of exact matches the number of fuzzy (=non-exact) matches the number of non-matches the number of errors that might have occurred during matching For each of the above steps, the result report lists the number of records that passes the check. The tool also makes a ‘grand total’ of these results, indicating if the quality of record is sufficient to be imported into OBIS, taking into account the results of the above mentioned checks. If the file contains fields that do not match the OBIS schema, these are also listed. Fields that cannot be mapped to the OBIS schema will not be uploaded in OBIS. After this data format check, a number of columns are added to the originally uploaded file, where the results of each step are listed. Each check is basically a yes/no question, which is translated to a 1 (yes) or 0 (no) value in the results file and is thus easy to interpret. "],["common_qc.html", "9.6 Common Quality Control issues", " 9.6 Common Quality Control issues Content: Uncertain temporal range Uncertain geolocation How to use OBIS Maptool How to use Gazetteers to obtain geolocation information Low confidence taxonomic information Uncertain measurements 9.6.1 Uncertain temporal range When the eventDate or temporal scope of your dataset is in question or provided in an invalid format (e.g., textual description), there are a number of options to ensure the most accurate date is provided. You may provide a range of dates in the ISO 8601 format if the range of dates is certain. Do not include a date range if you are making assumptions. Notes about any assumptions or interpretations on date ranges can be documented in the eventRemarks field. Be careful when entering date ranges. For example, entering 1870/1875-08-04 is equivalent to any date between 1870 and 1875-08-04. Date ranges can be used in this way to capture some level of uncertainty in when an event occurred. If only parts of the date are known (e.g., year but not month and day), you may provide the date in ISO 8601 format while excluding the unknown elements. Do not use zero to populate incomplete dates, simply end the date with the known information (e.g., 2011-03 instead of 2011-03-00). Additionally, if the year is unknown, you should only populate the month and day fields because eventDate cannot be formatted to exclude year. In these cases, eventDate is not necessary to fill. If date was provided as a textual description that is accurately interpretable, include the text description in the verbatimEventDate field. Then provide the interpreted date in ISO 8601 format in the eventDate field. Be sure to document any other important information in ‘eventRemarks’. For historical dates that do not conform to the ISO 8601 format, guidelines have been developed by the OBIS Historical Data Project team. The dwc:class:GeologicalContext can be used to capture some information for records pertaining to fossilized specimens, and the Chronometric Age Extension can also be used. Note that currently when publishing, the Chronometric Age extension will not be aggregated with OBIS data, but this extension will be available when an individual dataset is downloaded. This applies to any other extensions not currently implemented. 9.6.2 Uncertain geolocation Sometimes locality information can be difficult to interpret, especially if records originate from historical data with vague descriptions, or descriptions/names of areas that no longer exist. If your dataset is missing decimalLongitude and decimalLatitude, but the locality name is given, there are a number of approaches you can take. You can: Use the OBIS Map Tool to obtain a WKT string for point, line, or polygon features to put in the footprintWKT field. The corresponding projection should be placed in the footprintSRS field. Note the accepted spatial reference system for OBIS is EPSG:4326 (WGS84). The Marine Regions Gazetteer is available for use within the Map tool to help find locations. Search for locations with the Marine Regions Gazetteer to obtain coordinates and a locationID. For information on how to use this tool, see below. You can also use the Getty Thesaurus of Geographic Names or Google Maps. See below Note: Always be sure to fill in the georeferenceSources field to indicate the sources you used to obtain locality information when appropriate If you have a set of points, a line, or a polygon (perhaps from the Map tool), you can find the centroid of the features using either obistools::calculate_centroid or PostGIS, and then enter this coordinate into the decimalLatitude and decimalLongitude fields. This PostGIS guideline will help you select a centroid that is guaranteed to fall within your designated area. Estimate coordinateUncertaintyinMeters that is wide enough to cover the area If the location is provided as an array or WKT format, you can use R package obistools::calculate_centroid to obtain coordinate uncertainty. Use the OBIS maptool to obtain from the “radius” column. This is only applicable for lines or polygons, not point features. For data that only has textual descriptions: Try GBIF’s GEOLocate Web Application. You can use this tool for one location at a time with the Standard Client, or upload a CSV file for batch processing. This tool lets you enter text descriptions in the “Locality String” field, and other relevant locality information (e.g. country, state, county) to obtain geographic coordinates. Use this Biodiversity Enhanced Location Services tool developed by VertNet. It can translate textual descriptions and provide decimalLatitude, decimalLongitude, geodeticDatum, and coordinateUncertaintyInMeters as a csv sent to an email address. For more information on this service, see the associated GitHub. GBIF also provides some guidelines for difficult localities as well as other georeferencing tips for different geographic features, such as when only a distance or heading is provided (e.g., 10 km off Sao Paulo’s coast, north of Fiji). Important note: If you are making any inferences and/or decisions about locality coordinates, please record this in the georeferenceRemarks field. Additional information about the locality can also be stored in DwC terms such as waterBody, islandGroup, island and country. locationAccordingTo should provide the name of the gazetteer that was used to obtain the coordinates for the locality. 9.6.3 How to use OBIS Map Tool A video tutorial on how to use our Map tool is available below. This video covers the following topics: Estimating coordinates Using the line and polygon tool Obtaining and exporting WKT strings Well-Known Text (WKT) strings are representations of the shape of the location and can be provided in the footprintWKT field. This is particularly useful for tracks, transects, tows, trawls, habitat extent, or when an exact location is not known. WKT strings can be created using the Map tool’s WKT function. The Map tool also calculates a midpoint and a radius for line or polygon features, which can then be added to decimalLongitude, decimalLatitude, and coordinateUncertaintyInMeters, respectively. As mentioned above, the obistools::calculate_centroid function can be used to calculate the centroid and radius for WKT polygons. This wktmap tool can also be used to visualize and share WKT strings. 9.6.4 Using Getty Thesaurus & Google Maps to obtain locality coordinates For both the Getty thesaurus and Google Maps you can simply search the name of a locality, for example the Cook Strait in New Zealand. The search result on the Getty thesaurus will bring you to a page where you can obtain decimalLatitude and decimalLongitdue. Screenshot of Cook Strait page on the Getty Thersaurus For Google Maps, the coordinates can be found in the url after searching. Screenshot of Google Maps showing where coordinates can be found in the URL 9.6.5 How to use Marine Regions Gazetteer tool Marine Regions offers a marine gazetteer search engine to obtain geographic information and unique identifiers for marine regions. Once you have navigated to the gazetteer search engine, you have two options to search by: enter the name of the desired locality, or enter an MRGID code. Most likely you will have a locality name but not an MRGID. You may also select a placetype to search instead for types of regions that may be physical (e.g., seamount, bay, fjord, etc.) or administrative (e.g., exclusive economic zones, countries, etc). You can specify specific sources if known (e.g., published paper, organization, etc.). Finally, you can give a latitude/longitude coordinate with a radius around it to obtain a list of regions near that point. For this example we will search by geographic name for the Bay of Fundy. Our search returned 5 results from different sources (indicated in brackets). So how do we select the correct one? We can notice right away that the second result, from SeaVox SeaArea, has a preferred alternative, which when you click on the link brings you to the IHO Sea Area description for Bay of Fundy. So already we can likely drop SeaVox as a potential candidate. A good next step may be to compare the geographical extent for each to ensure it covers the desired area. If you are uncertain about exactly where your locality is, it may be better to be safe and choose a wider geographic region. Let’s compare the maps for all 5 results: Notice that no region has the exact same geographic extent. Let’s select the IHO Bay of Fundy locality (the first search result) to ensure we are covering the entire area of the Bay of Fundy, but not the Gulf. Inspecting the rest of the page, there is a lot of other useful information we can use. We can populate the following OBIS fields for our dataset, copying the information outlined in the red boxes: locationID from MRGID: http://marineregions.org/mrgid/4289 decimalLatitude and decimalLongitude latitude and longitude coordinates of the location’s midpoint in decimal degrees: 44.97985204, -65.80601556 coordinateUncertaintyInMeters precision: 196726 meters Screenshot of Marine Region placedetails, highlighting important information for OBIS Since we are obtaining all this locality data from Marine Regions, we must also populate the locationAccordingTo field. Here, we will provide the name of the gazetteer we used to obtain the coordinates for the locality - in this case you would write “Marine Regions”. In georeferenceRemarks we must document that the coordinates are the region’s midpoint, that locality information was inferred by geographic name, and, where applicable, place the original locality name in the field verbatimLocality. Finally, the location portion of our dataset would look something like this: locality locationID decimalLatitude decimalLongitude coordinateUncertaintyInMeters locationAccordingTo georeferenceRemarks Bay of Fundy http://marineregions.org/mrgid/4289 44.97985204 -65.80601556 196726 Marine Regions Coordinates are a midpoint inferred from location name The OBIS Mapper has built-in access to the Marine Regions Gazetteer. The video below demonstrates how to use this built-in tool, as well as how to navigate the Marine Regions Gazetter to obtain important georeferencing information to include in your data. 9.6.5.1 DwC Terms obtained from Maptool and Gazetteers Below is a table summarizing the different DwC terms you can obtain from the OBIS Maptool or from the Gazeteers discussed above. DarwinCore Term Maptool Term Marine Regions Term Notes decimalLatitude Latitude Latitude decimalLongitude Longitude Longitude locationID MRGID coordinateUncertaintyInMeters radius precision (not always available) footprintWKT WKT 9.6.6 Low confidence taxonomic identification In case of low confidence taxonomic identifications, and/or the scientific name contains qualifiers such as cf., ?, or aff., then you should: Put the name of the lowest possible taxon rank that can be determined with high-confidence in scientificName (e.g. the genus) Put any text regarding identification with low confidence and/or qualifiers in identificationQualifier (e.g., cf., aff.) Put the species name in specificEpithet Place the rank of the taxon documented in scientificName (e.g., genus) in taxonRank Document any relevant comments in taxonRemarks or identificationRemarks (e.g. reasoning for identification) Take an example specimen named Pterois cf. volitans. The associated occurrence record would have the following taxonomic information: scientificName = Pterois identificationQualifier = cf. volitans specificEpithet = leave blank scientificNameID = the one for Pterois taxonRank = genus If the provided name is unaccepted in WoRMS, it is okay to use the unaccepted name in this field. scientificNameID should contain the WoRMS LSID for the genus. There is a new Darwin Core term verbatimIdentification meant for containing the originally documented name, however this term is not yet implemented in OBIS so if you populate this field it will not be indexed alongside your data. However you can use identificationRemarks to add extra information. The use and definitions for additional Open Nomenclature (ON) signs (identificationQualifier) can be found in Open Nomenclature in the biodiversity era, which provides examples for using the main Open Nomenclature qualifiers associated with physical specimens (Figure 1). Whereas the publication Recommendations for the Standardisation of Open Taxonomic Nomenclature for Image-Based Identifications provides examples and definitions for identificationQualifiers for image-based non-physical specimens (Figure 2). Figure 1. Flow diagram with the main Open Nomenclature qualifiers associated with physical specimens. The degree of confidence in the correct identifier increases from the top down. More info and figure copied from Open Nomenclature in the biodiversity era. Figure 2: Flow diagram with the main Open Nomenclature qualifiers for the identification of specimens from images (non-physical, image-based) . More information and figure copied from Recommendations for the Standardisation of Open Taxonomic Nomenclature for Image-Based Identifications If the occurrence is instead unknown or new to science, it should be documented according to recommendations by Horton et al. 2021. Populate the scientificName field with the genus, and in identificationQualifer provide the ON sign ‘sp.’. Be sure to also indicate the reason why species-level identification is unavailable by supplementing ‘sp.’ with either stet. (stetit) or indet. (indeterminabilis). If neither of these are applicable, (e.g. for undescribed new species), add a unique taxon identifier code after ‘sp.’ to identificationQualifer. For example Eurythenes sp. DISCOLL.PAP.JC165.674. When adding a taxon identifier code, please avoid simple alphanumeric codes (i.e. Eurythenes sp. 1, Eurythenes sp. A). Like creating eventIDs or occurrenceIDs, try to provide more complex and globally unique identifiers. Identifiers could be constructed by combining higher taxonomic information with information related to a collection, institution, museum or collection code, sample number or museum accession number, expedition, dive number, or timestamp. This ensures namestrings will remain unique within OBIS. We also recommend including these temporary names on specimen labels for physical specimens. 9.6.6.1 Changes in taxonomic classification Taxonomic classification can change over time - so what does that mean for your datasets when records change classification? Because OBIS relies on WoRMS as the taxonomic backbone, changes in taxonomic classification will be updated between a day to a few weeks from the date of change, unless triggered manually. This means that the WoRMS LSID associated with a species in question will be used to automatically populate the taxonomic classification with the updated information. However we recognize that there may be issues when larger changes occur (e.g. Family level splits). Then, records that are only identified to the Family level may not get updated properly. For example, there was a shift in coral taxonomy where the family Nephtheidae was split into Capnellidae and Alyconiidae for species occurring in the Northern Atlantic. While species that were identified as belonging to Nephtheidae have now been updated to belong to one of those two families, records that were only identified down to family (i.e., Nephtheidae) are still documented as Nephtheidae. Unfortunately there is currently no solution for dealing with splits like this, aside from contacting data providers and asking them to change the taxonomy. A future solution may be an annotation registry that includes statements like “identifications of taxon x in geographic area y are misidentified and should be linked to taxon z”. 9.6.7 Uncertain measurements 9.6.7.1 individualCount In some cases individualCount may be uncertain due to only fragments of organisms being found, or only a range of individuals is known. The occurrenceRemarks can be used to document information about decisions made during data formatting. In cases where only certain body parts (e.g., head, tail, arm) are recorded, you can incorporate this information in measurementTypeID by finding a P01 code that contains elements from the S12 collection, which documents sub-components of biological entities. If a P01 code for your sub-component does not exist, you can request the creation of a P01 code. If you are uncertain whether fragments are from the same individual, a range can be provided and the uncertainty can be recorded in occurrenceRemarks. When providing a range to estimate the count, there are two suggested approaches: Use MOF/eMoF: place the data in the (extended)MeasurementOrFact extension. This requires an additional two rows per Occurrence. One with the equivalent of a minimumIndividualCount and one with the equivalent of maximumIndividualCount. Place data into dynamicProperties: Include the information in the Occurrence record itself, with no extension, and instead document it in dynamicProperties, with a value such as {“minimumIndividualCount”:0, “maximumIndividualCount”:5} We note that documenting the data in ‘dynamicProperties’ means the information will not be machine readable and may be more difficult for users to extract. Abundance vs Count data: A brief clarification on abundance and count data: abundance is the number of individuals within an area or volume. This type of data is recorded in organismQuantity, where organismQuantityType should be used to specify the type of quantity (e.g., individuals, percent biomass, Braun Blanquet Scale, etc.). However, if the value is just the number of individuals without reference to a space, this information is recorded in individualCount. 9.6.8 Non-marine species If you are given an error that your taxon is not marine, please confirm first whether the species is actually freshwater by cross referencing with WoRMS or IRMNG. If the species is not marine (i.e. belongs to a non-marine genus), check with the data provider as necessary for possible misidentification. Finally you can contact WoRMS at info@marinespecies.org to discuss adding the taxon to the WoRMS register. You will be required to provide documentation in this case to confirm marine status of the taxon. Otherwise, records marked as non-marine will be dropped from the published dataset, and this will be flagged in the data quality associated with your dataset. Let’s consider an example within this dataset on benthic macroalgae. Inspecting the data quality report we can see there are three dropped records due to species not being marine. Dropped records from a benthic macroalgae dataset Clicking on the dropped records we can see which three species were dropped. By scrolling to the right of the table, we can see these records have two quality flags: NO_DEPTH and NOT_MARINE. Flags specifying why certain records were dropped Let’s take a look at the first species, Pseudochantransia venezuelensis. When we search for this species on WoRMS we can see that the species is marked as freshwater. Cross-referencing with IRMNG, if we search for the genus-species, the species is not even found, an indication that it is not in the database (and also why it can be good to check multiple sources). Searching for just the genus, we can see that marine and brackish are stricken out, indicating the species is not marine. If you have species that are marked as non-marine in these registers but are either supposed to be marine, or were found in a marine environment, then you should contact WoRMS to discuss adding it to the register. For additions and/or edits to environmental or distribution records of a species, contact the WoRMS Data Management Team at info@marinespecies.org with your request along with your record or publication substantiating the addition/change. "],["data_publication.html", "10 Data publication and sharing 10.1 Licenses", " 10 Data publication and sharing Once you have finished formatting your data and have conducted some basic quality control procedures on it, you are ready to publish your dataset to OBIS. Note that OBIS nodes can accept any data files from its data sources or data providers, and they publish these data on their respective OBIS node Integrated Publishing Toolkit (IPT). Data from node IPTs are then harvested by central OBIS to become accessible via the global database. The Integrated Publishing Toolkit (IPT) is developed and maintained by the Global Biodiversity Information Facility (GBIF). While GBIF maintains an IPT manual, we outline specific OBIS instructions here. Some nodes require the manager themself to upload data. This means after you have formatted your data for OBIS and have done some basic quality control steps, you simply pass this file on to the appropriate node manager. They will communicate with you any issues during the publication process. Sometimes a node manager will request the data provider to upload their own dataset to the IPT. If you the data provider are required to upload your data then there are a number of steps you will take to upload your data. A reminder that all data formatting and quality control steps should be completed before uploading your dataset. Once uploaded, the node manager will check your upload before publishing the dataset. There are a few steps involved to publish a dataset, whether you are the provider or the node manager. You must: Identify the correct IPT for your OBIS node (you may have to contact your node manager to confirm IPT) Login to the IPT, or have the node manager create an account for you if you do not have one, so you can upload your dataset(s) Map each of your fields to Darwin Core terms. This should be relatively straight forward if you have done this already during data formatting Fill all relevant metadata to help users understand and cite your dataset Publish and make your data public Details for each of these steps are outlined on the subsequent pages. We will start with an overview of which Creative Commons license should be used, and then move on to using the IPT to publish data. 10.1 Licenses OBIS nodes must make the necessary agreements with the original data providers so that data can be made available to OBIS under one of the following Creative Commons licenses (in order of preference): CC-0 - data may be used without restrictions CC-BY - data are available for any use if proper attribution and credit is given CC-BY-NC - data may be used for any non-commercial use as long as proper attribution/credit is given You may need to consult with your organization if there are any copyright concerns. For more information on the different Creative commons license types see About the licenses. "],["ipt.html", "10.2 IPT: Integrated Publishing Toolkit", " 10.2 IPT: Integrated Publishing Toolkit Contents: Introduction How to access the IPT Who populates IPTs? Upload data Map to Darwin Core Add metadata Publish on the IPT Publish your data as a dataset paper Download data from the IPT Introduction to the IPT Before we get into the details for accessing and using the IPT, let’s understand what it is. Biodiversity datasets and their metadata are published in OBIS using the Integrated Publishing Toolkit (IPT), developed by GBIF. The IPT is an open source web application that can be customized by the OBIS node manager (see IPT admin page for details). An IPT-instance is used to publish and register all datasets. To be able to create and manage your own dataset (called a “resource” by GBIF), you will need a user account. In general, the IPT software assists users in mapping data to valid Darwin Core terms, as well as archiving and compressing the Darwin Core content with: A descriptor file: meta.xml that maps the core and extensions files to Darwin Core terms, and describes how the core and extensions files are linked The eml.xml file, which contains the dataset metadata in Ecological Metadata Language (EML) format. For instructions on how to enter the metadata go to EML. All these components (i.e., core file, extension files, descriptor file, and metadata file) become compressed together (as a .zip file) and comprise the Darwin Core Archive. Example showing how Occurrence core, EML, and meta.xml files make up a Darwin Core-Archive file How to access the IPT Once you have determined which OBIS node IPT is suited for your dataset, you can contact your node manager to create an associated account for you. There will be a link on the sign in page that will direct you to the IPT’s administrator to contact them. If your node’s IPT is not listed here, you will have to contact the node manager to get the link to their IPT. Screenshot of IPT login page, highlighting link to IPT admin and the login button If you are an IPT admin and want to know how to set up an IPT yourself, see the IPT admin page. 10.2.0.1 Who populates the IPT with datasets? With regard to populating the IPT with marine data for OBIS, there are two possible approaches: Manager driven: You as node manager take the responsibility of describing, checking and uploading the data and metadata to the IPT. The data provider can send you the data ‘as such’ or you can make agreements with your providers on the accepted OBIS data format and standards. This approach will give you a very good knowledge of what data is available. It can be time-consuming, as (extended) communication with the data provider will be necessary to document the metadata and to re-format the data to the OBIS standards. User driven: You as node manager can guide (some of your) data providers to publish the data and metadata to the IPT themselves. Your main task will be to make sure that all relevant information and data for OBIS is available and that you perform the necessary quality checks before the data are released to OBIS. Once the Darwin Core Archive is created, the data provider should inform the node manager of this action, so he or she can do the necessary quality control checks. In order for the node manager to be able to look at the dataset, the data provider should add him or her as a “resource manager” to this specific dataset. In most cases, there will be a combination of these two approaches. The chosen approach will largely depend on the capacity, availability and willingness of your data provider to invest extra time in formatting and thoroughly describing their data. If you – as node manager – would prefer a partly user driven approach, the following steps to publishing marine data to OBIS briefly explains how you or a data provider can upload, standardize and publish a dataset on the OBIS node IPT, without the hassle of installing and maintaining an IPT instance. The data are published in your organization’s name. This guide is based on the Canadensys 7-step guide to publishing marine data: Desmet, P. & C. Sinou. 2012. 7-step guide to data publication. Canadensys. http://community.canadensys.net/publication/data-publication-guide. Caution: Make sure you have obtained the rights from the data owners to publish their data! Create your resource on the IPT Once you have your account, login at the top of the IPT page. Click on the tab Manage resources: it will display all the datasets you are managing and will be empty at first. You can create a new resource at the bottom of the page. Follow the GBIF IPT manual for more detailed instructions. Screenshot of the manage resources page on the IPT The first thing that needs to be completed is the shortname of your resource. This shortname uniquely identifies your resource (i.e. dataset) and will eventually show up in the URL of this resource on IPT. These shortname identifiers are also used to create folders on the IPT and they cannot be changed. We therefore advise that the shortname: is unique, descriptive and short (max. 100 characters) does not contain a space, comma, accents or special characters Shortname good examples: VLIZ_benthos_NorthSea_2000 UBC_algae_specimens … When you would delete a resource, please inform your node manager of this action! If you create a test-file, please include _test at the end of your shortname. Then select the type of data you are uploading: Occurrence, Checklist, Sampling-Event (i.e. Event Core), Metadata only, or Other. Note that Checklist datasets are accepted by GBIF, but not currently implemented in OBIS. However, you can still have checklist data hosted on OBIS IPTs. You can also create an entirely new resource by uploading an existing archived resource. See the IPT manual section Upload a Darwin Core-Archive for instructions. Please note the IPT has a 100MB file upload limit, however, there is no limit to the size of a Darwin Core Archive that the IPT can export/publish. Refer to this note in the IPT manual to find out how to work around the file upload limit. Once you have created your resource, you will see an empty resource overview page. 10.2.0.2 Upload data Uploading your source file to the IPT is easy: go to > your resource overview page > Source Data and click on Choose File. This is where you will select and add the files containing your Core table and (if applicable) extensions. You might want to compress/zip your source file first to improve the upload speed of large files. The IPT will unzip them automatically once received. Follow the IPT manual for more detailed instructions (including the option to use multiple source files or to upload via a direct database connection). Accepted formats are delimited text files (csv, tab and files using any other delimiter), either directly or compressed as zip or gzip. Screenshot of where on the IPT page you can add source data Let’s follow an example Event core dataset. The first file we will upload will be a .csv file containing the Event core table. After it’s been selected, we click “Add”. A source file detail page will then be shown, displaying how the IPT has interpreted your file (number of columns, rows, header rows, character encoding, delimiters, etc.). Click the preview button to verify everything is correct, click anywhere on the screen to exit the preview, then click save. Screenshot of page shown after you upload a source file for the first time You can also provide information about how the data table is encoded, how many header rows exist, the type of delimiters, and what type of character encoding you used. You may need to double check the character encoding for your file, especially if you used any special characters (e.g., in species or place names). UTF-8 is one of the most common encoding standards, and you can select this encoding when saving files, depending on the software used: Windows: MS Excel: select Save as. From the drop down select the “CSV UTF-8 (Comma delimited)” option Windows: Notepad: Click on File, then Save as In the drop down menu “Save as type” drop-down, select All Files In the “Encoding” drop-down on the bottom right, select UTF-8. Be sure to name your file using the .csv extension (e.g., data.csv). Mac: Numbers: Select File then Export to… -> CSV. Click Advanced options Click the drop-down menu next to Text Encoding and select Unicode (UTF-8) Click Next, then finally name your file, select a location, and click Save Once you have specified all the above information, click Save, This will redirect you back to the Overview page for your dataset. You can add multiple files for each of your tables by clicking the three vertical dots to the right and “+ Add”. We will add a .csv file for an Occurrence extension, as well as one for the eMoF extension. Example screenshot of the different files, core and extensions, you can upload to an IPT Map your data to Darwin Core Because biodiversity data are published in the Darwin Core standard, the next step is to map your data fields to Darwin Core. As we have mentioned earlier in this manual, the DwC standard includes a list of defined terms and allows your data to be understood and used by others. It also allows an aggregator like OBIS or GBIF to integrate your data with other datasets. Darwin Core mapping is the process of linking the fields in your resource file with the appropriate Darwin Core terms. It is the most challenging step in publishing your data for two reasons: The list of Darwin Core terms can be overwhelming, so it might be difficult to select the ones that are appropriate for your dataset The IPT currently only allows one-to-one mapping of fields, so the ease of mapping will depend on your database structure and on the feasibility of exporting as close to Darwin Core as possible. You can contact your node manager or the OBIS secretariat at info@iobis.org to help guide you through the steps, review your mapping, suggest terms etc. You also welcome to post questions in the OBIS Slack. You can find more information regarding Darwin Core mapping in the IPT manual (including core types, extensions, auto-mapping, default values, value translation, etc.). To add the DwC mappings, click the three vertical dots to the right of this section and select “+ Add” Screenshot of the IPT demonstrating where to add new DwC mappings A popup window will allow you to select your Core type to facilitate mapping - Occurrence or Event core. In this example, we will select Darwin Core Event for the Event core table. Screenshot showing the two Core types you can map a datafile to Next we will confirm which file we are mapping to: the .csv file containing our event data. Then click Save. Because we had already named several of our fields with Darwin Core terms, they have been auto-mapped for us. It is important to review each Darwin Core class (Record, Event, Location, etc.) and ensure any unmapped fields are mapped to the correct DwC term. When you select a term, a drop down menu will appear where you can select the appropriate field from your dataset. It is good practice to double check that the auto-mapped fields are mapped correctly. Once you are done mapping, any unmapped fields will appear at the bottom of the page for you to check. If there is no DwC term to map these terms to, that is okay, but the data will not be published alongside the rest of your dataset. Consider moving these unmapped fields to either dynamicProperties or to one of the extensions (e.g., eMoF), whichever is most applicable. Finally, click Save. You may return to the Overview page by clicking Back. To add DwC mappings for the other files (Occurrence and eMoF), click the same “+ Add” button and go through the same process for each extension table you have. The IPT may identify Redundant terms if certain terms appear in the e.g., Event core and Occurrence extension. If your Occurrence extension (or core) contains information about individualCount and organismQuantity, you can map such fields in both the Occurrence and the eMoF as a measurementType. Screenshot showing other DwC extensions you can map to Watch the video below for an overview of all of the above procedures, from uploading data to mapping terms to DwC. The next step is to fill in or upload metadata. Add metadata Metadata enables users to discover, assess, understand and attribute your dataset for their particular needs, so it pays off to invest some time providing them. Go to your resource overview page > Metadata and click Edit to open the metadata editor. Any information you provide here will be visible on the resource homepage and bundled together with your data when you publish. Screenshot showing where to add or upload metadata Follow the guidelines on the OBIS metadata standards and best practices page, or check the IPT manual for detailed instructions about the metadata editor. You can also upload a file with metadata information. The video below also demonstrates how to fill metadata on the IPT. Publish on the IPT With your dataset uploaded, properly mapped to DwC, and all the metadata filled, you can publish your dataset. On your resource overview page, go to the Publication section, click the vertical dots and select Publish. Screenshot showing where to manage the publishing of your dataset The IPT will now generate your data as Darwin Core, and combine the data with the metadata to package it as a standardized zip-file called a “Darwin Core Archive”. See the IPT manual for more details. Note: Hitting the “publish” button does not mean that your dataset is available to everyone, it is still private, with access limited to the resource managers. It will only be publicly available when you have changed Visibility to Public. You can choose to do this immediately or at a set date. Screenshot showing how to change the visibility of your dataset Your dataset will only be harvested by GBIF when you change Registration to Registered. This step is not needed for OBIS to harvest your datasets. Please do not register your dataset with GBIF if your dataset is already published in GBIF by another publisher. Note that the IPT itself must be registered with GBIF in order to publish to GBIF. The node manager can do this. Screenshot showing where to register your dataset with GBIF. This is only available if the IPT itself is registered with GBIF as well. Back on the resource overview page > Published Release, you can see the details of your first published dataset, including the publication date and the version number. Since your dataset is published privately, the only thing left to do is to click Visibility to Public (see the IPT manual) to make it available to everyone. Warning: please do not do this with your test dataset. It is now listed on the IPT homepage and you can share and link to it, e.g.: http://ipt.vliz.be/resource.do?r=kielbay70. This would be a good time to notify any regional or thematic network you are involved in, which can also have an interest in your dataset. Your published dataset is a static snapshot of your data and will not change until you upload an updated source file and click publish again or publish a new version (do not create a new resource). This procedure has the advantage that your dataset is always available, does not require a live internet connection to your database and can be easily shared. It also allows you to control the publication process more precisely: version 1, version 2, etc. and users are informed of how recent the data are (via the last publication date). To view an older version of the metadata about the resource, just add the trailing parameter &v=n to the URL where v stands for “version”, and n gets replaced by the version number, e.g., http://ipt.vliz.be/ilvo/resource.do?r=zoopl_bpns&v=1. In this way, specific versions of a resource’s EML, RTF, and DwC-A files can be retrieved. Please note, the IPT’s Archival Mode must be turned on in order for old versions of DwC-A to be stored (see Configure IPT settings section of the IPT manual). The first minute of the video below provides an overview of how to publish on the IPT. 10.2.0.3 Publish your metadata as a data paper The Metadata expressed in the EML Profile standard can also be downloaded as a Rich Text Format (RTF) file. The latter can serve as a draft manuscript for a data paper (First database-derived ‘data paper’ published in journal), which can be submitted for peer-review to e.g. a Pensoft journal. Downloading datasets from an IPT To download a dataset from an IPT, simply navigate to the IPT home page (not the Manage Resources tab) and search for the dataset in question. You can search for keywords in the Filter box on the right side of the page. Generally you do not need to log in to an IPT to download a dataset. Overview of home page of an IPT Once you navigate to the page of a dataset, at the top of the page you will have options to download the whole Darwin Core Archive file, or just the metadata as an EML or RTF file. Be sure to note the license associated with the dataset. Overview of a dataset page on an IPT, emphasizing where to download the resource "],["ipt_admin.html", "10.3 IPT Admin Responsibilities", " 10.3 IPT Admin Responsibilities If you are an IPT administrator, you are responsible for: Creating the IPT Registering IPT with OBIS and GBIF Keeping the IPT up to date Describing, checking and uploading the data and metadata to the IPT Guide data providers to publish the data and metadata to the IPT themselves Make sure all relevant information and data for OBIS is available Perform necessary quality checks before the data are released to OBIS Creating and Installing IPTs OBIS nodes can decide to install and manage their IPT on their own institutional servers or use (at no charge!) the OBIS servers in Oostende, Belgium, provided as in-kind by the Flanders Marine Institute (VLIZ), which also runs the European OBIS node (EurOBIS). VLIZ also ensures the IPT instances run on the latest version (important for security updates). Here is an overview of the IPT instances hosted in Oostende: http://ipt.iobis.org/. Please contact the secretariat at info@iobis.org if you would like OBIS to host your IPT. To install your own IPT, please follow the instructions in the GBIF IPT manual. Registration When you have installed your IPT, please provide the IPT instance URL to the OBIS secretariat (helpdesk@obis.org), so your IPT is included in the data harvesting process. OBIS recommends to share the data as widely as possible including with other networks such as GBIF. On 13 October 2014, a cooperation agreement was signed between the secretariats of IOC-UNESCO/OBIS and GBIF in which the two parties recognized the two initiatives (OBIS and GBIF) as complementary with common goals (and in particular OBIS’s role in Marine Biodiversity Data). Together they agreed to work towards maximizing the quantity, quality, completeness and fitness for use of marine biodiversity data, accessible through OBIS and GBIF and in particular in the development of data standards (DwC), technology (IPT), maximizing fitness for use, development of biodiversity indicators for assessments, enhance capacity through training and coordinate approaches to the global science/policy interface. At the 4th session of the OBIS Steering Group (SG-OBIS-IV, Feb 2015), it was recommended that GBIF should harvest OBIS tier 2 nodes if OBIS tier 2 nodes could also harvest marine datasets from their GBIF nodes. In this way OBIS could work directly with the entire marine community and promote its standards and best practices. It was not recommended that iOBIS set up a separate IPT for GBIF to harvest, since this would mean a duplication of effort. In order to publish data with GBIF, the OBIS node also needs to become a data publisher in GBIF, and link the IPT installation with this publishing organization. OBIS nodes are encouraged to use the OBIS node name as the publishers’s name, unless the host institution requires its institutional name to be used. In the latter case, reference to the OBIS node can be added in the description, as well as between brackets in the title. The name of the IPT instance can also refer to the OBIS node. OBIS nodes are also encouraged to select OBIS as the endorsing organization. In this way, the OBIS node is also listed on the OBIS page at GBIF. The video tutorial below will help guide you on registering your OBIS node IPT with GBIF. Maintaining the IPT As the IPT administrator, you must make sure the IPT is kept up to date, datasets (resources) are published in a timely manner, add, edit, or delete IPT users as required, register with GBIF as applicable, and configure the types of cores and extensions accepted by the IPT. To add or update extensions, navigate to the Darwin Core Types and Extensions page from the Administration menu. To install an extension (e.g., DNA Derived Data), simply scroll down the page and click the Install button to the right of the desired extension. Screenshot of IPT Admin page For extensions already installed, you may notice yellow flags indicating a core or extension is out of date. You can update these easily by clicking the Update button. Screenshot demonstrating when core or extensions need to be updated For a detailed breakdown of administrator options, see the IPT guide. "],["data_sharing.html", "10.4 Maintaining and sharing published data 10.5 Update your data in OBIS 10.6 Publish OBIS data to GBIF", " 10.4 Maintaining and sharing published data Content Add a DOI User tracking Update your own data Publish to OBIS and GBIF 10.4.1 Adding a DOI to datasets DOIs are important for tracking your dataset. Fortunately you can easily reserve a DOI for your dataset if the IPT administrator has configured the IPT accordingly. As the IPT administrator, you must enable the capacity for users to reserve DOIs. To do this you first need a DataCite account associated with an Organization. Only one DataCite account can be used to register DOIs in this manner (i.e. IPT users do not need an account). The IPT’s archival mode, configurable on the IPT settings page, must also be turned on (note that enabling this mode will use more disk space) to enable this feature. For more information see the IPT administration manual. Once this has been configured, a data provider or admin can easily reserve a DOI for a dataset. First log in to the IPT, navigate to the Manage Resources tab, then select the dataset for which you wish to reserve a DOI. On the overview page for the dataset, scroll to the Publication section, click the three vertical dots and select “Reserve DOI”. Screenshot indicating how to reserve a DOI for your dataset 10.4.2 User tracking OBIS tracks the number of times your dataset is downloaded. This information is available on your dataset’s page under the Statistics box. Example screenshot of how dataset downloads can be tracked 10.5 Update your data in OBIS To update your own data in OBIS, the process is largely the same as publishing your first version. Follow the steps below: Log in to the IPT where your data are hosted Under the Manage Resources tab, locate your dataset Upload new files, complete the DwC mapping, and/or update any metadata that may have changed In the Publication section, click Publish just as you did before The new version will be automatically updated. A new DOI will be generated only if you generate it yourself on the new version. Deciding when to generate a new DOI is up to you, but generally you should generate a new DOI when there have been major changes to your dataset, such as significant changes in your metadata or a move to a 2.0 resource version within the IPT. Example of IPT version control 10.6 Publish OBIS data to GBIF There are a few differences between OBIS and GBIF. Perhaps the most obvious difference is that OBIS focuses on marine data, whereas GBIF includes broader biodiversity data. However, OBIS implements more strict quality control requirements for published datasets. A complete list of GBIF’s quality control checks can be found here, and a guide on GBIF’s publishing process is here. OBIS currently accepts two core data table types: Occurrence core and Event core. GBIF includes both Occurrence and Event core, with one additional core type, Checklists. GBIF is also developing a new data model to expand data publishing capabilities. Some of the other main differences in how OBIS and GBIF structure and publish datasets that you should be aware of include: OBIS uses WoRMS as the exclusive taxonomic backbone (and WoRMS identifiers to populate scientificNameID), whereas GBIF uses Catalog of Life and does not currently require the use of taxonomic identifiers The OBIS-ENV-DATA structure, the eMoF extension, and the DNA Derived data extensions are not included in GBIF downloads (e.g., this dataset description). This data can still be published alongside your dataset, and is available when it is downloaded from the Source archive, but it will not be included in a GBIF Annotated Archive download. OBIS conducts some QC procedures that GBIF does not, including: Checking validity of depth measurements Checking validity of WoRMS LSID Identifying if taxa are exclusively freshwater or terrestrial GBIF includes most of the same data standards as OBIS (Darwin Core, EML), however GBIF also follows the Biological Collection Access Service (BioCASE/ABCD) Watch the video below for details on how to publish OBIS datasets to GBIF (starting at 1:07), or how to publish GBIF datasets to OBIS (starting at 6:42). See the tables below for a quick comparative reference on which terms are required or recommended in OBIS and GBIF for Occurrence and Event tables. Event Table: Term Status in OBIS Status in GBIF eventID required required eventDate required required decimalLatitude & decimalLongitude required strongly recommended samplingProtocol strongly recommended required samplingSizeValue & samplingSizeUnit strongly recommended required countryCode strongly recommended strongly recommended parentEventID strongly recommended strongly recommended samplingEffort strongly recommended strongly recommended locationID strongly recommended strongly recommended coordinateUncertaintyInMeters strongly recommended strongly recommended geodeticDatum recommended strongly recommended footprintWKT recommended strongly recommended occurrenceStatus required in occurrence extension strongly recommended Occurrence Table: Term Status in OBIS Status in GBIF occurrenceID required required eventDate required required scientificName required required basisOfRecord required required kingdom recommended required decimalLatitude & decimalLongitude required strongly recommended scientificNameID required not required, accepted occurrenceStatus required not required, accepted taxonRank strongly recommended strongly recommended coordinateUncertaintyInMeters strongly recommended strongly recommended individualCount, organismQuantity & organismQuantityType strongly recommended strongly recommended geodeticDatum recommended strongly recommended eventTime recommended not required, accepted countryCode not required, accepted strongly recommended informationWithheld not required, accepted not required, accepted dataGeneralizations not required, accepted not required, accepted country not required, accepted not required, accepted "],["access.html", "11 Data access 11.1 OBIS Homepage and dataset pages 11.2 Mapper 11.3 R package 11.4 API 11.5 Full exports 11.6 Finding your own data in OBIS 11.7 How to contact data provider 11.8 Interpreting downloaded files from OBIS", " 11 Data access OBIS has over 100 million records of marine data accessible for downloading. To download data from OBIS, there are several options: OBIS homepage or advanced dataset search OBIS Mapper Accessible through the R package robis OBIS API Full data exports IPT NOTE When you download data from the Mapper or full export, the data you will receive is flattened into one table with occurrence plus event data. eMoF data tables are separate upon request. However when you download a dataset from the OBIS homepage or dataset page, all tables (Event, Occurrence, eMoF) are separate files. 11.1 OBIS Homepage and dataset pages From the OBIS homepage, you can search for data in the search bar in the middle of the page. You can search by particular taxonomic groups, common names, dataset names, OBIS nodes, institute name, areas (e.g., Exclusive Economic Zone (EEZ)), or by the data provider’s country. When you search by dataset you will notice an additional option appears for advanced search options. This will allow you to identify specific datasets, and apply filters for OBIS nodes and whether datasets include extensions. OBIS homepage search, showing where to find the advanced search link Regardless if you found a dataset through the homepage or the advanced Dataset search, you will be able to navigate to individual dataset pages. For individual dataset pages (instead of aggregate pages for e.g., a Family) there are three buttons available: Report issue - allows you to report any issues with the dataset in question Source DwC-A - download the dataset as a Darwin Core-Archive file. This will provide all data tables as separate files within a zipped folder To mapper - this will open another browser with the data shown in the Mapper Dataset download If you searched for aggregate datasets (e.g., all Crustacea records, all records from OBIS-Canada, etc.), the source DwC-A button will not be available to you. To download these data subsets, you must click to mapper and then download the data from the Mapper as a CSV. 11.2 Mapper https://mapper.obis.org Watch this video demonstration of how to use the Mapper as well as the OBIS homepage search. The mapper allows users to visualize and inspect subsets of OBIS data. A variety of filters are available (taxonomic, geographic, time, data quality) and multiple layers can be combined in a single view. Layers can be downloaded as CSV files. Screenshot demonstrating where how to download a particular layer When you download data from the mapper, you will be given the option to include eMoF and/or DNA Derived Data extensions alongside the Event and Occurrence data. You must check the boxes of extensions you want to include in your download. Screenshot showing the popup confirmation for which extensions you want to include in your download from the OBIS Mapper After downloading, you will notice that the Event and Occurrence data is flattened into one table, called “Occurrence.csv”. Upon inspecting this file in your viewer of choice, you will see it contains all 225 possible DwC fields, although not every field will contain data for each observation. Any extensions you checked will be downloaded as separate tables. 11.3 R package https://github.com/iobis/robis The robis R package has been developed to facilitate connecting to the OBIS API from R. The package can be installed from CRAN or from GitHub (latest development version). The package documentation includes a function reference as well as a getting started vignette. As a quick example of what the package can do, you can obtain raw occurrence data by feeding a taxon name or AphiaID to the occurrence function. If you’d like to then download this data, you can simply export R objects with the write.csv function. For example, if we wanted to obtain Mollusc data from OBIS: library(robis) moll<-occurrence(“Mollusca”) write.csv(moll, “mollusca-obis.csv”) This file will be saved to your working directory (if you are not familiar with working directories, read here). After opening the file, you will notice that the fields in the download do not include every possible field, but instead only those where information has been recorded by data providers, plus the fields added by OBIS’s quality control pipeline. To use robis for visualizing and mapping occurrences, see the Visualization section of the manual. Watch the video below for a walkthrough of how to use the robis package to obtain OBIS data. 11.4 API https://api.obis.org/ Both the mapper and the R package are based on the OBIS API, which can also be used to find and download data. When using the API directly, you can filter by the following options: Occurrence Taxon Checklist Node Dataset Institute Area Country Facet Statistics When you have entered all the information you are interested in filtering by, scroll down and click the “Execute” button. This will produce a response detailing how many records match your criteria, as well as information for some of the headers from the data (e.g., basisOfRecord, Order, genus, etc.). A download button will be available for you to download the data as well. When searching with the API, you may need to know certain identifiers, including: AphiaID - obtainable from the WoRMS page of a taxa of interest (e.g. the AphiaID for Mollusca would be 51) Dataset UUID - can be obtained from the URL on individual dataset pages E.g., this dataset’s UUID would be 5061d21c-6161-4ea2-a8d4-38f8285dfc47 Area ID Institute ID - this should be the Ocean Expert ID (e.g., the ID for NOAA Fisheries Service, Southeast Regional Office St. Petersburg is 7532) OBIS node UUID A short video demonstrating use of the API is shown below. 11.5 Full exports https://obis.org/data/access/ To obtain a full export of OBIS data, navigate to the OBIS homepage, click on Data from the top navigation bar, then select Data Access from the dropdown menu. OBIS homepage showing where to navigate to access full database exports Here you will be able to download all occurrence records as a CSV or Parquet file. Note the disclaimer that such exports will not include measurement data, dropped records, or absence records. As with downloads from the Mapper, the exported file is a single Occurrence table. This table includes all provided Event and Occurrence data, as well as 68 fields added by the OBIS Quality Control Pipeline, including taxonomic information obtained from WoRMS. OBIS Data Access page 11.6 Finding your own data in OBIS To find your own dataset in OBIS, you can use the same tools as finding any dataset in OBIS. You have the following options: From the OBIS homepage or the Mapper, you can search by dataset name, species of interest, the OBIS node that you uploaded to, or by institute Note: When using the Mapper you can combine multiple search criteria to help narrow down your search E.g., if we wanted to find this dataset in the Mapper, we could search for OBIS USA under Nodes, National Oceanic and Atmospheric Administration, Washington under Institutes, and/or Radiozoa under Scientific Name. Then when we view the data and scroll down to datasets, the only one listed is the one we were interested in If you have used the (extended)measurementOrFact extension and have measurementType data, you can search by the name of your measurementType, and click on the hyperlink for records. This will populate a list of datasets that you can scroll through which have used a particular measurementType. Note this tool is only for consultatation purposes and should never be used to help you select vocabulary for your data 11.7 How to contact data provider To contact the data provider, navigate to the page for the individual dataset in question (e.g., https://obis.org/dataset/80479e14-2730-436d-acaa-b63bdc7dd06f). Under the “Contacts” section, there will be a list of individuals you can contact. Clicking any name will direct you to your system’s default email program. For example: Example of contact section on a dataset homepage access via the OBIS search If you are the node manager and need to contact the data provider about a particular dataset, contact information should be provided in the metadata and you can contact them from information provided. 11.8 Interpreting downloaded files from OBIS In general, the field names you will see when you download data from OBIS are the same as those seen during the data formatting and publishing process. When you download data from the Mapper you will see all 225 possible Darwin Core fields. Downloading data from an IPT or full export will include only the fields provided by the data provider, formatted as one Occurrence file (or separate files for individual datasets). Some fields are added through the OBIS quality control pipeline, including taxonomic information from WoRMS and the fields flags, bathymetry, and dropped. As mentioned in the Quality Control section, the fields flags and dropped will list quality control issues or if the record was dropped, respectively. Details and definitions for all fields added by the OBIS QC pipeline can be found here. For a full list of the other Darwin Core terms and their definitions included in downloads, please reference the Darwin Core reference guide. "],["citing.html", "11.9 Citing Data from OBIS", " 11.9 Citing Data from OBIS Depending on how you access data from OBIS, there are different ways you should cite downloaded datasets. General OBIS citation: OBIS (YEAR) Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. For individual datasets retrieved from OBIS (dataset citations are available in the zip downloads as html file): [Dataset citation available from metadata] [Data provider details] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. Accessed: YYYY-MM-DD). For example: Sousa Pinto, I., Viera, R. (Year: if not provided use year from dataset publication date) Monitoring of the intertidal biodiversity of rocky beaches with schools in Portugal 2005-2010. CIIMAR - Interdisciplinary Centre of Marine and Environmental Research, Porto. (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. Accessed: 2024-01-01) When data represents a subset of many datasets taken from the integrated OBIS database (e.g. downloaded from the Mapper), you can, in addition to citing the individual datasets (and taking into account the restrictions set at each dataset level), also cite the OBIS database as follows: OBIS (YEAR) [Data e.g. Distribution records of Eledone cirrhosa (Lamarck, 1798)] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. Accessed: YYYY-MM-DD) The derived information products from OBIS are published under the CC0 license and can be cited as follows: OBIS (YEAR) [Information product e.g. Global map showing the Hulbert index in a gridded view of hexagonal cells] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. Accessed: YYYY-MM-DD) "],["dataviz.html", "12 Data Visualization 12.1 Example notebooks using data from OBIS 12.2 obisindicators: calculating & visualizing spatial biodiversity using data from OBIS", " 12 Data Visualization 12.1 Example notebooks using data from OBIS Here are a few R notebooks showcasing the robis package: Data exploration of wind farm monitoring datasets in OBIS Diversity of fish and vulnerable species in Marine World Heritage Sites based on OBIS data Data exploration - Stratified random surveys (StRS) of reef fish in the U.S. Pacific Islands DNADerivedData extension data access Canary Current LME Here are others that may be of interest: Diversity indicators using OBIS data OBIS species richness for OSPAR Quality control of ISA data Accessing gridded data 12.2 obisindicators: calculating & visualizing spatial biodiversity using data from OBIS obisindicators is an R library developed during the 2022 IOOS Code Sprint. The purpose was to create an ES50 diversity index within hexagonal grids following the diversity indicators notebook by Pieter Provoost linked above. The package includes several examples, limited to 1M occurrences, that demonstrate uses of the package. Screenshot of diversity map derived from OBIS data "],["other_resources.html", "13 Other Resources 13.1 MBON Pole to Pole Tutorial 13.2 IOOS Darwin Core Guide 13.3 EMODnet Biology 13.4 Template Generators 13.5 Bionomia", " 13 Other Resources In this section we highlight useful resources created by collaborators and other community members. 13.1 MBON Pole to Pole Tutorial https://www.youtube.com/watch?v=teJhfsSWonE This tutorial was created by the MBON Pole to Pole project to help guide people through the process of transforming datasets to Darwin Core using tools MBON Pole to Pole has developed. 13.2 IOOS Darwin Core Guide https://ioos.github.io/bio_data_guide/ This book contains a collection of examples and resources related to mobilizing marine biological data to the Darwin Core standard for sharing though OBIS. This book has been developed by the Standardizing Marine Biological Data Working Group (SMBD). The working group is an open community of practitioners, experts, and scientists looking to learn and educate the community on standardizing and sharing marine biological data. 13.3 EMODnet Biology https://classroom.oceanteacher.org/course/view.php?id=958 Contributing Datasets to EMODnet Biology is a course hosted on Ocean Teacher Global Academy (OTGA), developed by members of the European Marine Observation and Data Network. The course prepares users to format, publish, and perform quality control checks on datasets according to Darwin Core standards. While targeted at EMODnet Biology users, this course has significant overlap in how to prepare datasets for OBIS and is useful for those unfamiliar with OBIS standards. Note, an account with OTGA is required to access the course. 13.4 Template Generators There is an Excel template generator developed by Luke Marsden & Olaf Schneider as part of the Nansen Legacy project. It allows the creation of Event or Occurrence core templates, with an optional eMoF extension. Note this template generator is aimed at GBIF users, so make sure to account for and include required OBIS terms. There is also an Excel to Darwin Core macro tool developed by GBIF Norway that you can download for use in Microsoft Excel. This macro can help you set up Event, Occurrence, and eMoF tables by selecting all relevant DwC fields from a list, or by importing data from another spreadsheet. It allows for auto-generation of identifiers (e.g. eventID, occurrenceID) if macros are enabled, and can also auto-populate the eMoF when measurement fields in the Occurrence table are populated. 13.5 Bionomia https://bionomia.net/ Bionomia is a platform that links individuals to the biological specimens they collect and identify, recognizing their contributions to biodiversity research. To ensure your data is integrated, publish your dataset compliant with Darwin Core standards and include relevant information in fields such as recordedBy, recordedByID, identifiedBy, identifiedByID. Bionomia automatically harvests specimen data when published in this format (learn more at Bionomia). Please note, Bionomia respects privacy; attributions are not openly shared if the individual has not made their profile publicly accessible (see Bionomia’s privacy policy). Users can also utilize Bionomia’s integration with Zenodo by enabling settings to push specimen data into Zenodo (see Bionomia’s integration with Zenodo). "],["404.html", "Page not found", " Page not found The page you requested cannot be found (perhaps it was moved or renamed). You may want to try searching to find the page's new location, or use the table of contents to find the page you are looking for. "]] +[["index.html", "The OBIS manual 1 Introduction 1.1 Guidelines on the sharing and use of data in OBIS 1.2 Acknowledgements", " The OBIS manual 11 September, 2024 1 Introduction This manual provides a comprehensive walk-through on how to contribute data to OBIS and how to access data from OBIS. It is divided into topical sections that provide detailed information on: Frequently Asked Questions How to contribute data to OBIS, including information on data standards and terms of reference for OBIS groups Formatting datasets Running quality control checks Publishing data Accessing data from OBIS Data visualization tips Links to additional resources The manual is designed to provide guidelines for OBIS nodes and data providers, ensuring that data published via OBIS are of high quality and adhere to internationally recognized standards. It also serves as a resource for data users on how to access, process, and visualize data from OBIS. See the quick links page for a quick reference to various guides, tools, and resources. See the OBIS YouTube playlist for many helpful How-to videos! The OBIS manual is a dynamic document and is revised on a regular basis. We welcome suggestions for additions and changes, which can be sent to helpdesk@obis.org, the OBIS Capacity Development Task Team at training@obis.org, and/or submitted as issues at https://github.com/iobis/manual/issues. 1.1 Guidelines on the sharing and use of data in OBIS It is important that our data providers, as well as all data users, are aware and agree on the OBIS guidelines on the sharing and use of data in OBIS, which was adoped at the 12th OBIS Steering Group. NB: Data users should be aware of the licenses associated with all datasets downloaded from OBIS (e.g. CC0, CC-BY, CC-BY-NC). 1.2 Acknowledgements This manual received contributions from (in alphabetical order): Ward Appeltans, Abby Benson, Samuel Bosch,Yi-Ming Gan, Philip Goldstein,Mary Kennedy, Elizabeth Lawrence, Carolina Peralta Brichtova, Pieter Provoost, Saara Suominen, Serita van der Wal, and Leen Vandepitte. "],["policy.html", "1.3 Data Policy", " 1.3 Data Policy Guidelines on the sharing and use of data in OBIS The IOC Assembly adopted a new IOC Data Policy and Terms of Use (IOC Decision A-32/4.4, 2023) and requested that IOC programmes (like OBIS), projects, as well as other communities of practice should develop and/or apply, where applicable, detailed metadata, data and products sharing guidelines that are consistent with this new policy. The OBIS guidelines on the sharing and use of data in OBIS was agreed at SG-OBIS-IV (Feb 2015) and adopted at IODE-XXIII (March 2015) and was based on the principles of the previous IOC data exchange policy. The SG-OBIS-11 (May 2023) set up an IWG Data Policy to fulfil this task and then the SG-OBIS-12 agreed upon an updated policy. The OBIS data policy is outlined below. Text in italics represents text from the IOC Data Policy and Terms of Use. 1.3.0.1 Section 1. Preamble The OBIS data policy follows the guidelines from the Intergovernmental Oceanographic Commission (IOC) data policy. In this regard, we recognize that: the timely, open and unrestricted international sharing, in both real-time and delayed mode of ocean metadata, data and products is essential for a wide variety of purposes and benefits including scientific research, innovation and decision making, the prediction of weather and climate, the operational forecasting of the marine environment, the preservation of life, economic welfare, safety and security of society, the mitigation of human-induced changes in the marine and coastal environment, as well as for the advancement of scientific understanding that makes this possible. Innovation of specialised products can be stimulated and encouraged by timely, open and unrestricted access to metadata and data. Metadata, data, and products should be accessible, interoperable and openly shared with minimum delay and minimum restrictions. This document describes how the IOC data policy is implemented by OBIS. Although this document broadly covers the requirements for metadata, data, and data products, more detailed guidance for the OBIS community is also provided in the OBIS manual (https://manual.obis.org). 1.3.0.2 Section 2. Purpose The purpose of this data policy is to outline the requirements with respect to sharing, access, preservation, and attribution to facilitate the broad use and reuse of metadata, data and data products. 1.3.0.3 Section 3. FAIR & CARE principles To support knowledge discovery and innovation both by humans and machines and to acknowledge indigenous data governance, data should meet the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) [1] and In the case of indigenous data and information, data should meet the CARE principles (Collective Benefit, Authority to Control, Responsibility, Ethics) [2] to the greatest extent practicable. The FAIR Principles related to Findability state that data and metadata are assigned globally unique and persistent identifiers. For OBIS this means that all event and occurrence records published to OBIS should have a globally unique identifier in the appropriate field. Care must be taken to preserve these identifiers across dataset updates. Datasets should be assigned a DOI (Digital Object Identifier) upon publishing. Findability also means that datasets are described using rich metadata. In practice, this means that OBIS dataset metadata records must contain at least the following: a title, a detailed description including information on the methodology used to collect the data, contact information, a license, a citation, geographic and taxonomic scope, and may contain other relevant metadata. Regarding the Accessibility aspect of the FAIR data principles, OBIS datasets’ metadata should always be accessible via the data provider IPT instance even when the data is no longer available. In addition, OBIS datasets should be retrievable by using a DOI that leads to the data. Interoperability principles state that knowledge must be described using a shared language which has been formally specified, so that the knowledge can be interpreted not just by humans but also by machines. OBIS data are described using the Darwin Core standard, which includes a set of terms with identifiers and definitions, as well as a number of vocabularies which should be used wherever possible. In order to optimize interoperability of measurements and sampling facts data, it is recommended that OBIS datasets use terms and identifiers from the BODC NERC vocabulary server, or other vocabulary collections with machine interpretable identifiers. OBIS data must also be Reusable, that is, enough information is available so that others can use that data in different settings. In this regard, all data and metadata associated with records needs to be complete and accurate, containing at least the minimum information described in the OBIS manual. Ideally, providers should go well beyond the minimum and provide as much information as possible. Both the data and metadata should comply with the standards adopted by OBIS, for example Ecological Metadata Language (EML) for metadata and Darwin Core for biodiversity data. It is also necessary that all data provided is clearly linked to a usage license, respecting the licenses currently accepted by OBIS (i.e. CC0, CC BY, CC BY-NC), ensuring that users are able to fully understand how they can reuse the data and also to give the correct acknowledgement to data providers where required. Beyond complying with FAIR principles, OBIS data must also be in accordance with the CARE principles. That is, indigenous data and information should always be handled with sensitivity and respect. Authority of indigenous data holders must be respected, and indigenous peoples’ rights and wellbeing should be considered at all stages of the data life cycle. 1.3.0.4 Section 4. Conditions of use Data should be licensed (respecting Section 8) under a minimally restrictive and voluntary common-use licence[3] that grants permission, ensures proper attribution (for example, citable using a persistent identifier) and allows others to copy, distribute and make use of the data. Section 5. Data Repositories and the IOC ocean data and information system (ODIS) Data should be quality controlled (using community adopted and documented best practices or standards), accompanied by complete metadata and stored in an openly discoverable and accessible long-term data repository and made available through standards-based data services. Member States shall encourage convergence and interoperability and, where possible, use IODE data centres (National Oceanographic Data Centres or Associate Data Units) or other IOC programme related data centres that share metadata and data using the IOC Ocean Data and Information System (ODIS). ODIS is an interoperability layer and supporting technology, to allow existing and emerging ocean data and information systems to interoperate with one another. Data provided to OBIS must be licensed with one of the accepted licenses described in the OBIS manual. Data users should be encouraged to appropriately attribute data publishers, for example by providing a license, clear citation instructions, and a DOI. OBIS, through the work of its nodes, is also concerned in providing high-quality marine biodiversity data for users. Thus, all data submitted to OBIS needs to undergo rigorous quality assurance and validation processes before submission. Minimally, data providers must check data for locality issues, taxonomic mismatches, and proper formatting. They must also ensure that all necessary information fields are available. Guidance on how to perform data quality control is provided by the OBIS community, through its OBIS manual. An inseparable part of high quality data is that it must have complete and comprehensive metadata, as described in the Section 3. All data and products published through OBIS are freely accessible to any user, following the principles of equitable access and benefit sharing. In this regard, when licenses allow, users can build products based on OBIS data without any cost. Whenever possible, users should consider sponsoring or partnering with OBIS and its OBIS nodes in grant proposal writing. Creating a global database like OBIS cannot happen without the, often voluntary, contribution of many scientists and data managers all over the world. Several activities, such as coordination, data integration, quality control, database and website maintenance require resources including manpower at national and international level. Any use of OBIS data and/or derived products, including (but not limited to) software applications, workflows and papers, should be properly cited. The following formats are recommended: General OBIS citation: OBIS (YEAR) Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. obis.org. Use the following format to cite data retrieved from OBIS (dataset citations are available in the zip downloads as html file): [Dataset citation available from metadata] [Data provider details] [Dataset] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. obis.org. Accessed: YYYY-MM-DD. For example: Sousa Pinto, I., Viera, R. (Year: if not provided use year from dataset publication date) Monitoring of the intertidal biodiversity of rocky beaches with schools in Portugal 2005-2010. CIIMAR - Interdisciplinary Centre of Marine and Environmental Research, Porto. [Dataset] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. obis.org. Accessed: 2015-01-01) When data represents a subset of many datasets taken from the integrated OBIS database, you can, in addition to cite the individual datasets (and taking into account the restrictions set at each dataset level), also cite the OBIS database as follows: OBIS (YEAR) [Data e.g. Distribution records of Eledone cirrhosa (Lamarck, 1798)] [Dataset] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. Accessed: YYYY-MM-DD) The derived information products from OBIS are published under the CC-0 license and can be cited as follows: OBIS (YEAR) [Information product e.g. Global map showing the Hulbert index in a gridded view of hexagonal cells] [Map] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. Accessed: YYYY-MM-DD) While those citation formats cover a broad range of applications, users should always consult the OBIS manual for more detailed guidelines on how to properly attribute the data and information products provided by OBIS and its broader community. 1.3.0.4.1 Section 5. Data Repositories and the IOC ocean data and information system (ODIS) Data should be quality controlled (using community adopted and documented best practices or standards), accompanied by complete metadata and stored in an openly discoverable and accessible long-term data repository and made available through standards-based data services. Member States shall encourage convergence and interoperability and, where possible, use IODE data centres (National Oceanographic Data Centres or Associate Data Units) or other IOC programme related data centres that share metadata and data using the IOC Ocean Data and Information System (ODIS). ODIS is an interoperability layer and supporting technology, to allow existing and emerging ocean data and information systems to interoperate with one another. As explained in section 3, data providers are responsible for performing quality control on all data prior to its submission to OBIS. Details on quality control procedures are provided in the OBIS manual, which is regularly updated by the OBIS secretariat and OBIS nodes. OBIS is connected to ODIS and is an integral part of the IODE data structure. 1.3.0.5 Section 6: Secure long-term data archives To support long-term and secure archival, data and associated metadata should be submitted, to the best practicable degree, to IODE’s World Ocean Database (WOD), the Ocean Biodiversity Information System (OBIS), Global Sea Level Observing System (GLOSS), other IOC related global data archives, and data centres linked to the World Data System (WDS), their successors or other global data archives. OBIS and its nodes should ensure long-term data archival and adhere to best practices for data preservation. OBIS secretariat will archive the curated database, but nodes are responsible for archiving individual datasets. Data and data products provided by OBIS and its nodes must also be available through standards-based data services that allow users to access, query, and retrieve data in various formats. OBIS currently provides several ways through which users can access its data and follows the best practices for data sharing, and in this sense, any newly developed product must ensure maximum accessibility and interoperability. 1.3.0.6 Section 7. Access restrictions Data and associated metadata should be made available with minimal restrictions on use unless there are valid reasons to restrict access. Legitimate reasons to restrict access to, and reuse of, data include, inter alia, privacy and confidentiality, protection of species, populations, or habitats of concern, and national security. OBIS datasets should be published using any of the three accepted CC licenses, with a preference for the most permissive CC0. In case access to information must be restricted for any of the above reasons, data providers should censor or generalize the data before publishing to OBIS, and ensure that these changes are reflected in the dataset metadata. When data is made available to OBIS, OBIS is granted permission to: Distribute the data via its data and information portal Build an integrated database, use the data for data quality control purposes, complement the data with other data such as climate variables and build value-added information products and services for science and decision-making Serve the data to other similar open-access networks in compliance with the terms and conditions for use set by the data providers. In pursuance of copyright compliance, OBIS endeavors to secure permission from rights holders to ingest their datasets. In the event that the inclusion of a dataset in OBIS is challenged on the basis of copyright infringement, OBIS will follow a take-down policy until there is resolution. 1.3.0.7 Section 8. Data sharing policies of Member States This Policy acknowledges the right of Member States and data owners to determine the terms of metadata, data and products sharing in a manner consistent with national jurisdictions, international conventions, and treaties, where applicable. Whenever possible, data should be made available in its fullness. However, in any case access to data must be restricted to respect Member States jurisdictions, conventions or treaties, it is the responsibility of data providers to censor or generalize the data before publishing to OBIS, as explained in Section 7. 1.3.0.8 Section 9. Data and metadata sharing guidelines IOC programmes, projects as well as other communities of practice should develop and/or apply, where applicable, detailed metadata, data and products sharing guidelines that are consistent with this IOC Data Policy and Terms of Use. OBIS and its nodes provide detailed guidelines on how to publish and use OBIS data. This information is made available in the OBIS manual (https://manual.obis.org/) and is regularly revised and improved to adhere to best practices. 1.3.0.9 Section 10. Definitions ‘Data’ is a set of values, symbols or signs (recorded on any type of medium) that represent one or more properties of an entity[4]. ‘Metadata’ is ‘data about data’ describing the content, quality, condition, and other characteristics of data that allows their inventory, discovery, evaluation or use. ‘Timely’ in this context means the distribution of data and/or products, sufficiently rapidly to be of value for a given application. ‘Openly’ means data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and share alike. ‘Product’ means a value-added enhancement of data applied to a particular use. OBIS uses the same definitions as IOC. 1.3.0.10 Disclaimer Appropriate caution is necessary in the interpretation of results derived from OBIS. Users must recognize that the analysis and interpretation of data require background knowledge and expertise about marine biodiversity (including ecosystems and taxonomy). Users should be aware of possible errors, including in the use of species names, geo-referencing, data handling, and mapping. They should cross check their results for possible errors and qualify their interpretation of any results accordingly. Unless data are collected through activities funded by IOC/IODE, neither UNESCO, IOC, IODE, the OBIS Secretariat, nor its employees or contractors, own the data in OBIS and they take no responsibility for the quality of data or products based on OBIS, or its use or misuse. "],["gethelp.html", "1.4 Getting Help in OBIS", " 1.4 Getting Help in OBIS If you require additional assistance with OBIS we recommend you first get in touch with the most relevant OBIS node. We also have a support channel on Slack where you can communicate with the OBIS community for help. Please feel comfortable posting to this channel before reaching out to the OBIS Secretariat (helpdesk@obis.org). The OBIS community is quite active on Slack and GithHub (see below) so you are more likely to receive a quick answer to your question by posting in either place, as the Secretariat receives many requests. You can submit issues and questions on relevant Github repositories: OBIS Manual OBIS issues GitHub repo OBIS quality control issues All other OBIS repositories We strongly recommend creating a GitHub account to engage with the OBIS community, document issues, ask questions, find datasets that need endorsing, etc. GitHub gives threads a more permanent home and allows for open communication and transparency. If you are unfamiliar with GitHub, the Carpentries have these training resources which you can reference. "],["FAQ.html", "1.5 Frequently Asked Questions", " 1.5 Frequently Asked Questions Content General Darwin Core Formatting data Vocabulary Tools Quality Control Publishing Data Access 1.5.0.1 General I have data and want to publish to OBIS - what do I do? Why is it important to share and format data? How do I handle sensitive data? Where can I make suggestions for improvements on this Manual? Where can I find OBIS related training videos? What are the responsibilities of OBIS node managers? Where can I find marine datasets linked to the OBIS network by the GBIF registry, that now require endorising? I am an OBIS node and need to update the contact information listed for my node on the OBIS website, how do I do that? To update OBIS Node contact information listed at https://obis.org/contact/, you can edit the information here https://metadata.obis.org/ by signing in with your OceanExpert credentials. 1.5.0.2 Darwin Core Where can I learn about “Darwin Core”? I am having trouble understanding how Core and Extension tables relate to each other How does the OBIS format avoid redundancy in data How are extension tables (e.g. eMOF, occurrence) linked with the core table? What is the difference between Occurence Core and Event Core? What is the difference between eventID, occurrenceID, organismID, taxonID, scientificNameID, recordNumber, materialEntityID, materialSampleID, and catalogNumber? What is the difference between individualCount, and organismQuantity, and which one should I use? The DwC term individualCount is used simply for documenting the number of individuals present at the time the occurrence happened. Conversely, organismQuantity is used to record any type of quantity related to an organism or occurrence. organismQuantityType may include the number of individuals, the % cover, biomass, the cover-abundance on the Braun-Blanquet Scale, etc. While both DwC terms can be placed in the Occurrence table, OBIS recommends also populating them in the eMoF table because of the standardization of terms and because you can link to sampling events and factual sampling information. 1.5.0.3 Formatting Data Is there a checklist of all required Darwin Core fields for OBIS? How does data flow in OBIS? Can I add polygon data to OBIS? Yes, polygons, lines, or combinations of polygon, line, and/or point data can be added to OBIS by using the footprintWKT field. This is can be used to record tracks, transects, tows, trawls, habitat extent, or when an exact location is not known. Midpoints of polygons can be added to the required fields decimalLongitude and decimalLatitude. WKT strings can be generated using the OBIS Map Tool. The tool can also calculate a midpoint and a radius, which is added to decimalLongitude, decimalLatitude, and coordinateUncertaintyInMeters respectively. There is also an obistools R function to calculate the centroid and radius for WKT polygons. What should I do if I do not have the data for required fields by OBIS? How do I construct an eventID? How do I construct occurrenceID? What data goes into Occurrence core (or extension) and how do I set up this file? How do I set up an Event core table? How should I populate parent and child event information? Do I have to populate information for child events if the parent event already contains that information? Do I have to provide decimalLatitude and decimalLongitude for the Event and Occurrence tables? The answer may depend on your dataset structure, but generally, no. If you have Event core, then you do not need to repeat location information in the Occurrence table (but you can if you’d like). If you are using Occurrence core, then location information must be provided in the Occurrence table. What data goes into extendedMeasurementOrFact (eMoF) and how do I set it up? Can I have multiple eMoF tables, one for Event and one for Occurrence measurements? Yes, you can create multiple eMoF tables if it is simpler for you to separate Event and Occurrence measurement data. However, you must ensure all tables include the correct identifiers, i.e. eventID for measurements related to events, and occurrenceID and eventID for measurements associated with occurrences. When you add multiple tables of the same type in the IPT, they will be treated as if they were a single table, so there is no issue if you would like to do this. How do I format dates? How do I handle historical data? How do I convert coordinates to decimal degrees? How do I convert different geographical formats to WGS84? How do I compile acoustic, imaging, or other multimedia data for OBIS? How do I compile habitat data for OBIS? How do I compile tracking data for OBIS? How do I compile DNA and genetic data for OBIS? What does sampleSize and organismQuantity refer to for genetic data? For genetic data, sampleSizeValue and organismQuantity do not refer to the amount sampled nor the number of organisms. Instead these fields are related to either 1) the number of sequence reads for eDNA data or 2) the number of droplets/partitions for qPCR data. See DNA data guidelines for more details. 1.5.0.4 Vocabulary How do I map Measurement or Fact terms in OBIS with preferred BODC vocabulary? I can’t find a suitable vocabulary, what do I do? How do I request a new vocabulary term? Should I use taxon-specific P01 codes to populate for measurementTypeID? e.g. http://vocab.nerc.ac.uk/collection/P01/current/A15985A1 No. You should never use taxon-specific P01 codes. This is because the taxa are already identified in the Occurrence table, in the fields scientificName and scientificNameID. How should I match raw data fields with Darwin Core terminology? Taxonomy How do I document occurrences from unknown species, those new to science, or those with temporary names? e.g. Eurythenes sp. DISCOLL.PAP.JC165.674 Occurrences unknown or new to science should be documented according to recommendations by Horton et al. 2021. You should populate the scientificName field with the genus, and in identificationQualifer provide the ON sign ‘sp.’. However you must also indicate the reason why species-level identification is unavailable. To do this, supplement ‘sp.’ with either stet. (stetit) or indet. (indeterminabilis). If neither of these are applicable, (e.g. for undescribed new species), add a unique taxon identifier code after ‘sp.’ to identificationQualifer. For example Eurythenes sp. DISCOLL.PAP.JC165.674. Please avoid simple alphanumeric codes (i.e. Eurythenes sp. 1, Eurythenes sp. A). Similar to creating eventIDs or occurrenceIDs, you should strive to provide more complex and globally unique identifier. Identifiers could be constructed by combining higher taxonomic information with information related to a collection, institution, museum or collection code, sample number or museum accession number, expedition, dive number, or timestamp. This ensures namestrings will remain unique within a larger repositories like OBIS. It is also recommended to include these temporary names on specimen labels for physical specimens. Can scientificNameID be populated with an identifier (e.g. WoRMS LSID) representing an unaccepted taxon name? Yes. The identifier in scientificNameID should always correspond with the name that is in the scientificName field, even if the name is an unaccepted name in WoRMS. For example, the species name “Holothuria mammiculata” was provided, but this name is unaccepted in WoRMS. The accepted name is “Holothuria (Stauropora) pervicax Selenka, 1867”. In this case scientificNameID should correspond to the original name with LSID urn:lsid:marinespecies.org:taxname:529968 because the ID must correlate with the name as recorded in scientificName. What happens when a WoRMS taxonomy is changed? e.g. a species is reclassified When species are reclassied in WoRMS, the original scientificName and scientificNameID provided in a dataset remains unchanged. However WoRMS will list the old ID as “Unaccepted”, and link to the accepted taxon entry, and this will be reflected in the taxonomic information attached to a dataset download. For example, if we search for Manta birostris in OBIS (https://obis.org/taxon/105857), we see that the taxon’s status in WoRMS is unaccepted. At the bottom of the page it links to the currently accepted name: https://obis.org/taxon/1026118. We can find an occurrence which shows the source scientificName as “Manta” while the interpreted scientificName is “Mobula”: https://obis.org/occurrence/0020c873-02f1-4bd7-b396-ad36600bc8b2. We can also see that originalScientificName is populated with the source name in the intepreted output. As a user, you don’t have to trace species names. However if the datasets’s DwC-A is downloaded from the dataset page instead of obtained through R or the Mapper, all fields will contain the original value. It remains good practice to also check identifiers against WoRMS to see if any have been updated when you download data. 1.5.0.5 Tools How do I use the WoRMS taxon match tool? Can I fetch a full classification for a list of species from WoRMS? What do I do if my scientificName does not return a match from WoRMS? Where can I find DNA sequences published in OBIS? Is there a template generator I can use to help create my Event, Occurrence, and eMoF tables? Yes. There is an Excel template generator developed by Luke Marsden & Olaf Schneider as part of the Nansen Legacy project. Note this template generator is aimed at GBIF users, so make sure to account for and include required OBIS terms. There is also this Excel to Darwin Core macro tool developed by GBIF Norway you can use to help generate templates. How do I georeference locations, including text-based descriptions? 1.5.0.6 Quality Control How do I do data quality control? What are the OBIS quality control flags? Why are certain records dropped in OBIS? What do I do when I am uncertain about the: Temporal range of a dataset OR eventDate Geospatial location Taxonomic identification Individual count What do I do with freshwater species that are part of my marine dataset? 1.5.0.7 Publishing How do I add my data to the OBIS database? What metadata do I have to provide? Where? How? How do you know which license to choose? How do I access the IPT? How do I use the IPT? Are there instructions for IPT administrators? How do I add DOI to my dataset? How do I publish to both GBIF and OBIS? How do I update my already published dataset? 1.5.0.8 Accessing data in OBIS Can I get (or publish) other types of information from OBIS, besides occurences (e.g. bio-eco variables, DNA data, abiotic measurements, etc.)? Yes! OBIS allows access to not only taxa occurrences, but also all kinds of measurement data including DNA data (see Contribute for a list of data accepted by OBIS). This data is recorded in the (Extended) Measurement Or Fact and DNA-Derived Data extension tables, respectively. To access this data when downloading, be sure to select MoF or DNA from the dropdown Dataset Type menu on the advanced dataset serach page. If you are obtaining data from the OBIS Mapper, make sure to check the box for MeasurementOrFact and/or DNADerivedData from the Extensions toggle section. When using the R package robis, use the mof=TRUE and/or dna=TRUE arguments. More data filter options will be added in the future. How do I download data from OBIS? How do I load the full (.csv) export of OBIS data? Loading the entire OBIS dataset uses a lot of memory and is probably not feasible on most desktop computers. You have a few potential options depending on the use case: i) process the data in smaller batches, or ii) load the dataset into a local database such as SQLite and use SQL queries to analyze the data Otherwise, we recommend you use the parquet download which is available here, instead of the CSV. Then in R, you can use the arrow package to work with parquet files. We also have a short tutorial on working with parquet files in R here, with an example application of this approach here (see first code block). How can I use R to access OBIS data? How do I use the OBIS API to fetch and filter data? How do I contact the data provider? How can I cite OBIS datasets and downloads? What are the definitions of the field names in the downloads generated by OBIS? How do I obtain a taxon checklist for an area? There are a few possible ways to obtain a taxon checklist for a given area. We will obtain a checklist of species in the Albain EEZ as an example. To do this we will create a bounding box around our area of interest, and then apply filters to simplify the geometry. library(mregions) library(dplyr) library(robis) library(sf) #obtain Albanian EEZ as sf geom <- mr_shp(key = "MarineRegions:eez", filter = "Albanian Exclusive Economic Zone", maxFeatures = NULL) #get WKT for the bounding box wkt <- st_as_text(st_as_sfc(st_bbox(geom)), digits = 6) #fetch occurrences for bounding box occ <- occurrence(geometry = wkt) %>% st_as_sf(coords = c("decimalLongitude", "decimalLatitude"), crs = 4326) #filter using geometry occ_filtered <- occ %>% filter(st_intersects(geometry, geom, sparse = FALSE)) %>% as_tibble() %>% select(-geometry) #get taxa alb_taxa <- occ_filtered %>% group_by(phylum, class, order, family, genus, species, scientificName) %>% summarize(records = n()) The dates look unusual in the download file. What are these, how do I convert them, and/or how do I obtain separate elements from them (e.g. month)? The values in date_start, date_mid, and date_end are unix timestamps which have been calculated from the ISO date in the eventDate column. We can convert these numerical values to dates using the formula below. =(E2/86400000)+DATE(1970,1,1) If, when you apply this formula, you still see numbers, you will need to set the cell formatting to Date. Once you have dates, you can obtain, e.g. months for seasonal analyses using: =MONTH(H2) You can also use this tool to convert timestamps. How do I filter by or obtain trait information for OBIS data (e.g. all benthic organisms)? Currently, it is not possible to filter OBIS data by trait. To do this, we recommend using the traits database of the World Register of Marine Species. For example, searching by “functional group”, you can specify benthos, plankton, nekton, etc. How do I get data from multiple regions from OBIS? If the areas OBIS currently uses does not work for your use case, then it is best to first define all the boundaries for the desired regions. OBIS can be queried using WKT polygons by providing a WKT string to the geometry parameter in the robis::occurrence function. HOWEVER there are some limitations with respect to polygon complexity, and if it is too complex you will likely receive the error “The OBIS API was not able to process your request”. For more complex spatial queries we recommend indexing OBIS and GBIF data against polygons and using (finely) gridded versions of these datasets to make the process faster. We note we have not yet properly documented this process, but see the example script produced by Pieter Provoost below. The script first indexes a polygon to the H3 spatial index, then queries a gridded version of OBIS+GBIF data on AWS to get the species list, and finally fetches taxonomy from WoRMS for every species, which may take some time. library(readr) library(h3jsr) library(sf) library(duckdb) library(DBI) library(dplyr) sf_use_s2(FALSE) # Read WKT from text file, convert to sf, and index to H3 resolution 7 # https://wktmap.com/?e6b28728 wkt <- read_file("wkt_21773.txt") geom <- st_as_sfc(wkt, crs = 4326) cells <- data.frame(cell = polygon_to_cells(geom, 7)[[1]]) # Set up duckdb connection and register cells table con <- dbConnect(duckdb()) dbSendQuery(con, "install httpfs; load httpfs;") duckdb_register(con, "cells", cells) # Join cells list and gridded species dataset species <- dbGetQuery(con, " select species, AphiaID from cells inner join read_parquet('s3://obis-products/speciesgrids/h3_7/*') h3 on cells.cell = h3.h3_07 group by species, AphiaID ") # Add WoRMS taxonomy id_batches <- split(species$AphiaID, ceiling(seq_along(species$AphiaID) / 50)) taxa_batches <- purrr::map(id_batches, worrms::wm_record) taxa <- bind_rows(taxa_batches) %>% select(AphiaID, scientificname, phylum, class, order, family, genus, scientificName = scientificname) # Get Mollusca species mollusca <- taxa %>% filter(phylum == "Mollusca") "],["linklist.html", "1.6 Quick links to resources", " 1.6 Quick links to resources Below you will find a convenient list of important or relevant websites and resources that are mentioned in this Manual. Links are organized by category. 1.6.0.1 General links and guides OBIS home page Darwin Core term OBIS Checklist OBIS FAQ OBIS Slack OBIS GitHub helpdesk List of OBIS nodes Darwin Core term Quick Reference Guide Darwin Core text guide Publishing DNA-derived data guide OBIS Data formatting tutorial YouTube series: this series covers topics including creating identifiers (eventID, occurrenceID), creating data tables (event, Occurrence, eMoF), accessing OBIS data, taxon matching, running QC checks, etc. De Pooter et al. 2017. Toward a new data standard for combined marine biological and environmental datasets - expanding OBIS beyond species occurrences. Biodiversity Data Journal 5: e10989. hdl.handle.net/10.3897/BDJ.5.e10989 Duncan et al. (2021). A standard approach to structuring classified habitat data using the Darwin Core Extended Measurement or Fact Extension. EMODnet report. https://www.emodnet-seabedhabitats.eu/resources/documents-and-outreach/#h3298bcd0a15741a8a0ac1c8b4576f7c5 (note you must refine search to Technical Reports from 2021 to identify this report as it does not have an individual link) 1.6.0.2 Data Access & Mapping/Georeferencing Tools OBIS Mapper: map-based tool for obtaining OBIS data, allowing various filters to be applied R package robis OBIS MapTool: used for generating WKT strings, georeferencing, etc. Wellknown Text (WKT) visualization: tool to visualize WKT strings Full OBIS Data export: also includes a list of which fields are appended/modified when data is published to OBIS OBIS API: API interface, note that the result previews from executing functions are frequently incomplete and only provide the first ~10 results Marine Regions Gazetteer: a standard list of geographic names with information and maps; can be used to assist georeferencing, particularly for marine locations Coordinate Conversion Tool: used to convert coordinates from degrees minutes seconds to decimal degrees Getty Thesaurus of Geographic Names: search interface for place names 1.6.0.3 Controlled vocabulary OBIS YouTube Vocabulary series: this series demonstrates how to use the vocabulary decision tree in conjunction with the SeaDataNet P01 facet search in order to find/select vocabularies for eMoF measurements SeaDataNet P01 facet search: recommended interface to search for P01 codes used to populate measurementTypeID field. Note that the free text search is best used by supplying one search term at a time (it does not search the same way we use Google) OBIS GitHub repository for vocabulary: use this repository to request new terms for measurements related to OBIS data Darwin Core Basis of Record Vocabulary: definitions for terms used to populate basisOfRecord OBIS MoF viewer: this tool allows you to search for all measurements or facts published in OBIS, but it should NOT be used as reference to select vocabularies. See the vocabulary section of the Manual for additional help MeasurementOrFact statistics summary by Node: this page provides a summary for each dataset by OBIS node, summarizing the % of missing measurementTypeID, measurementValueID, and measurementUnitIDs NERC Vocabulary Server: interface to search through the entire NERC Vocabulary Server to find vocabulary terms. As with the SeaDataNet facet search, the search does not function the same way Google does and you may have to adjust how you search for terms. 1.6.0.4 Tools for Taxonomy & Dates WoRMS Taxon match tool: used to match a list of species names to taxonomic authority and obtain LSIDs for species GBIF Name parser: tool to divide scientific names into their components and to check them against the taxonomic backbone used by GBIF OBIS Guidelines for Historical Data Chronometric Age Extension Canadensys Date parser: tool to parse dates into component parts 1.6.0.5 Data QC Tools & Links List of Quality Checks conducted by OBIS on published data List of QC Flags implemented by OBIS obistools R package for conducting QC checks on OBIS data LifeWatch & EMODnet Biology QC tool Hmisc R package for summarizing data, useful for checking data quality and outliers 1.6.0.6 Template Tools GBIF Excel templates: Event table and Occurrence table Excel to Darwin Core Standard (DwC) Tool: a macro Excel spreadsheet for setup, data entry, data validation and file export to DwC (Darwin Core Standard) files. Creates templates for Event, Occurrence, MeasurementsOrFacts, Extended MeasurementsOrFacts (EMoF), and Simple Multimedia tables. Darwin Core Template Generator for Event and Occurrence tables: allows selection of DwC terms to generate template for Event and Occurrence tables, developed by Luke Marsden, & Olaf Schneider 1.6.0.7 Publishing Tools & Links Integrated Publishing Toolkit (IPT) User Manual: manual with more details on how to use the IPT for publishing datasets Ecological Metadata Language (EML) guide EML R package: assists with creating EML files in R, assumes some existing familiarity with EML ezEML: form-like interface for generating EML files. Offers dataset attachment but this is not required to generate the EML. Note that this is also an repository which offers data Jinja EML template generator: can be used to create tailored EML templates that you can reuse for your datasets. A template may be a good option for you if there are several EML elements which will not need to be changed each time. Code used is similar to Python Become a GBIF publisher: crucial step for publishing your dataset to GBIF as well as OBIS "],["contribute.html", "2 What can you contribute and how? 2.1 Why publish data to OBIS 2.2 How to handle sensitive data", " 2 What can you contribute and how? Since 2000, OBIS has accepted, curated and published marine biodiversity data obtained by varied sources and methods. There is a common misconception that OBIS only accepts species occurrence data - however this is not true! OBIS can accept many types of marine data including: Presence/Absence Abundance, individual count Biomass Abiotic measurements Biotic measurements Sampling methods Sample processing methods Genetic data including DNA sequences Data originating from historical records Tracking data Habitat data Acoustic data Imaging data Metadata describing the dataset and any project or programme related metadata So if you have any of these types of marine data linked to your occurrence data and also want to contribute to OBIS - great! OBIS accepts data from any organization, consortium, project or individual who wants to contribute data. OBIS Data Sources are the authors, editors, and/or organisations that have published one or more datasets through OBIS. They remain the owners or custodians of the data, not OBIS! OBIS harvests and publishes data from recognized IPTs from OBIS nodes or GBIF publishers. If you own data or have the right to publish data in OBIS, you can contact the OBIS secretariat or one of the OBIS nodes, or additionally a GBIF publisher. Your organization or programme can also become an OBIS node. An OBIS node usually publishes data from multiple data holders, effectively being a node in a network of data providers. So you may have to first find a relevant node before you get your data ready to publish. To publish a dataset to OBIS, there are five main steps you must go through. First, you must identify which OBIS node is best suited to host your published data. If you would like to publish to GBIF at the same time, that is also possible. If your organization is already affiliated with a GBIF node with which you must publish from, OBIS can also harvest from GBIF nodes. Second, you must determine the structure of your data and which format will best suit your dataset. OBIS follows Darwin Core Archive (DwC-A) standards for datasets, and currently follows a star schema format. This format is based on relational databases. If you are unfamiliar with such database structures, or would like to refamiliarize yourself with them, please read here Then, you need to actually format your data according to OBIS and DwC-A standards and guidelines Once formatted, you should run a series of quality control measures to ensure you are not missing any required information and that all standards are being met. This helps ensure all data published in OBIS is formatted in a standardized way. When published in OBIS, OBIS provides a quality report to inform data owners and users of any quality control issues. By completing quality control before you publish your dataset you ensure there are fewer errors to fix later. Now that your dataset is ready for publishing, the relevant metadata must be filled in, and then published on the previously identified IPT. Each of these steps are covered in detail in the relevant sections of the manual. For an overview of this process see data management flow in OBIS. 2.1 Why publish data to OBIS It is important to publish and ensure your dataset follows a universal standard for several reasons. The FAIR guiding principles for scientific data management and stewardship provide a good framework to understand the reasoning behind publishing data. FAIR stands for Findable, Accessible, Interoperable, and Reusable. Let’s understand each aspect within the FAIR framework and how it is linked to publishing data in OBIS. F - Findable Even if you publish your dataset on its own, publishing your data with OBIS will make your data more Findable (and Accessible) to a wider audience you might not have otherwise reached. By publishing your dataset to OBIS you are adding to a global database where your data can be found and analyzed alongside thousands of other datasets. For example, a dataset on marine invasive species in Venezuela was published July 20, 2022 and as of October 5, 2022 records of this dataset were included in 1,873 data download requests. This can save you time rather than handling individual data requests. A - Accessible Similar to being Findable, OBIS makes your datasets more Accessible. Each dataset is given an identifier when you upload it on an IPT. Thus when users obtain data from OBIS, the original dataset can easily be identified and accessed. Data from OBIS is accessible in numerous ways, giving data users multiple avenues to potentially access your data. I - Interoperable Using a standardized data format with controlled vocabularies will ensure your data are more Interoperable - more easily interpreted and processed by computers and humans alike. Increasingly, scientists use computer programs to conduct e-Science and collect data with algorithms. Formatting your data for OBIS will ensure it can be read and accessed by such programs as well as understood by users. R - Reusable Publishing your data allows it to be Reused according to your chosen data usage license. Very likely you expended resources to collect your data and it would be a waste of those resources to leave your unique data unpublished and inaccessible for current and future generations. Likewise, it is better to preserve any data processing done to ensure your dataset is reproducible and/or verifiable. Finally, data in OBIS is often used in several assessment processes and used as information to support policy makers around the globe making informed decisions. There are many other benefits of publishing in OBIS, even if you haven’t published any work on it yet. This includes: Your dataset can be associated with a DOI, allowing for your dataset to be more easily cited. By ensuring your dataset citation is complete you will ensure you are being cited properly. Publishing your dataset with OBIS makes it easier to set it up as a Data paper, which generates value for you and other researchers. There are social benefits to data publishing as your work becomes integrated into a wider dataset. It gives both you and your data more visibility. This can lead to more opportunities for collaboration and further career development as a researcher or professional. Your data can be incorporated into larger analyses to better understand global ocean biodiversity, helping to shape regional and international policies. 2.2 How to handle sensitive data We recognize that sometimes your dataset may contain sensitive information (e.g., location data on endangered or poached species), or perhaps your organization does not want certain details publicly accessible. Types of sensitive data include: Location data on endangered or protected species Information regarding a commonly poached species Species or locations that have an economic impact (positive or negative) To accommodate sensitivity but still be able to contribute to OBIS, we suggest: Generalizing location information by: Obtaining regional coordinates using MarineRegions, Getty Thesaurus of Geographic Names, or Google Maps Using the OBIS Map tool to generate a polygon area with a Well-Known Text (WKT) representation of the geometry to paste into the footprintWKT field. Delay timing of publication (e.g., to accommodate mobile species) Submit your dataset, but mark it as private in the IPT so it is not published right away (i.e., until you set it as public). Alternatively, you can set a password on your dataset in order to share with specific individuals. Note that setting passwords will require some coordination with the IPT manager. By submitting your data to an IPT but not immediately publishing it, you can ensure that the dataset will be in a place to be incorporated at a later date when it is ready to be made public. This not only saves time and helps retain details while relatively fresh in your mind, but also ensures the dataset is still ready to be mobilized in case jobs are changed at a later date. GBIF has created the following Best Practices for Generalizing Sensitive data which can provide you with additional guidance. Chapman AD (2020) Current Best Practices for Generalizing Sensitive Species Occurrence Data. Copenhagen: GBIF Secretariat. https://doi.org/10.15468/doc-5jp4-5g10. "],["data_standards.html", "2.3 OBIS Data Life Cycle 2.4 Biodiversity data standards", " 2.3 OBIS Data Life Cycle The basic data life cycle for contributions to OBIS can be broken down into six step-by-step phases: Data structure Data formatting Quality control Publishing Data access (downloading) Data visualization Each of these phases are outlined in this manual and are composed of a number of steps which are covered in the relevant sections. After you have decided on your data structure and have moved to the Data Formatting stage, you must first match the taxa in your dataset to a registered list. In formatting your dataset you will ensure the required OBIS terms and identifiers are mapped correctly to your data fields and records. Depending on your data structure, you will then format data into a DwC-A format with the appropriate Core table (Event or Occurrence) with any applicable extension tables. Any biotic or abiotic measurements will be moved into the extendedMeasurementOrFact table. Before proceeding to the publishing stage, there are a number of quality control steps to complete. Once your data has been published, you and others can access datasets through various avenues and it becomes part of OBIS’ global database! This may seem like a daunting process at first glance, but this manual will walk you through each step, and the OBIS community is full of helpful resources. Throughout the manual you will find tutorials and tools to guide you from start to finish through the OBIS data life cycle. 2.3.0.1 Who is responsible for each phase? Phases 1 through 3 are the responsibilities of the data provider, while Phases 3 and 4 are shared between the data provider and the node manager. Data users are involved in Phases 5 and 6. The OBIS Secretariat is responsible for data processing and harvesting published resources. 2.4 Biodiversity data standards From the very beginning, OBIS has championed the use of international standards for biogeographic data. Without agreement on the application of standards and protocols, OBIS would not have been able to build a large central database. OBIS uses the following standards: Darwin Core Ecological Metadata Language Darwin Core Archive and dataset structure The following pages of this manual review each of these in turn. We show you how to apply these standards to format your data in the Data Formatting section. We also provide some dataset examples for your reference. "],["darwin_core.html", "", " 2.4.1 Darwin Core Terms & Guidelines Contents Introduction to Darwin Core Darwin Core terms Darwin Core guidelines Taxonomy and identification Occurrence Record level terms Location Event Time Sampling 2.4.1.1 Introduction to Darwin Core Darwin Core is a body of standards (i.e., identifiers, labels, definitions) that facilitate sharing biodiversity informatics. It provides stable terms and vocabularies related to biological objects/data and their collection. Darwin Core is maintained by TDWG (Biodiversity Information Standards, formerly The International Working Group on Taxonomic Databases). Stable terms and vocabularies are important for ensuring the datasets in OBIS have consistently interpretable fields. By following Darwin Core standards, both data providers and users can be certain of the definition and quality of data. 2.4.1.1.1 History of Darwin Core and OBIS The old OBIS schema was an OBIS extension to Darwin Core 1.2., which was based on Simple Darwin Core, a subset of Darwin Core which does not allow any structure beyond rows and columns. This old schema added some terms which were important for OBIS, but were not supported by Darwin Core at the time (e.g., start and end date and start and end latitude and longitude, depth range, lifestage, and terms for abundance, biomass and sample size). In 2009, the Executive Committee of TDWG announced their ratification of an updated version of Darwin Core as a TDWG Standard. Ratified Darwin Core unifies specializations and innovations emerging from diverse communities, and provides guidelines for ongoing enhancement. The Darwin Core Quick Reference Guide links to TDWG’s term definitions and related practices for Ratified Darwin Core. We will discuss the relevance of terms in this guide further below. In December 2013, the 3rd session of the IODE Steering Group for OBIS agreed to transition OBIS globally to the TDWG-Ratified version of Darwin Core, and the mapping of the (old) OBIS specific terms to Darwin Core can be found here. 2.4.1.2 Darwin Core (DwC) terms DwC terms correspond to the column names of your dataset and can be grouped according to class type for convenience, e.g., Taxa, Occurrence, Record, Location, etc. It is important to use DwC field names because only columns using Darwin Core terms as headers will be recognized. A list with definitions of all possible Darwin Core terms can be found on TDWG. However, OBIS does not parse all terms (note this doesn’t mean you cannot include them, they just will not be parsed when you publish to OBIS). Below is an overview of the most relevant Darwin Core terms to consider when contributing to OBIS, with guidelines regarding their use. We have also compiled a convenient checklist of OBIS-accepted terms, their DwC class type, and which OBIS file (Event Core, Occurrence, eMoF, etc.) it is likely to be found in. OBIS currently has seven required and one strongly recommended DwC term: occurrenceID, eventDate, decimalLongitude, decimalLatitude, scientificName, occurrenceStatus, basisOfRecord, scientificNameID (strongly recommended). The following DwC terms are related to the Class Taxon: scientificName scientificNameID scientificNameAuthorship kingdom taxonRank taxonRemarks The following DwC terms are related to the Class Identification: identifiedBy dateIdentified identificationReferences identificationRemarks identificationQualifier typeStatus The following DwC terms are related to the Class Occurrence: occurrenceID occurrenceStatus recordedBy individualCount (OBIS recommends to add measurements to eMoF) organismQuantity (OBIS recommends to add measurements to eMoF) organismQuantityType (OBIS recommends to add measurements to eMoF) sex (OBIS recommends to add measurements to eMoF) lifeStage (OBIS recommends to add measurements to eMoF) behavior associatedTaxa occurrenceRemarks associatedMedia associatedReferences associatedSequences catalogNumber preparations The following DwC terms are related to the Class Record level: basisOfRecord institutionCode collectionCode collectionID bibliographicCitation modified dataGeneralizations type The following DwC terms are related to the Class Location: decimalLatitude decimalLongitude coordinateUncertaintyInMeters geodeticDatum footprintWKT minimumDepthInMeters maximumDepthInMeters minimumDistanceAboveSurfaceInMeters maximumDistanceAboveSurfaceInMeters locality waterBody islandGroup island country locationAccordingTo locationRemarks locationID The following DwC terms are related to the Class Event: parentEventID eventID eventDate eventType eventRemarks habitat samplingProtocol (OBIS recommends to add sampling facts to eMoF) sampleSizeValue (OBIS recommends to add sampling facts to eMoF) SampleSizeUnit (OBIS recommends to add sampling facts to eMoF) samplingEffort (OBIS recommends to add sampling facts to eMoF) The following DwC terms are related to the Class MaterialSample: materialSampleID 2.4.1.3 Darwin Core guidelines 2.4.1.3.1 Taxonomy and identification scientificName (required term) should always contain the originally recorded full scientific name, even if the name is currently a synonym. This is necessary to be able to track back records to the original dataset. The name should include authorship and date information if known and should be the lowest possible taxonomic rank that can be determined, preferably at species level or lower, but higher ranks, such as genus, family, order, class etc. are also acceptable. You may still populate scientificNameAuthorship for authorship, but it is preferred to include authorship in scientificName as well. Additionally, scientificName should only contain the name and not identification qualifications (such as ?, confer, or affinity), which should instead be supplied in the IdentificationQualifier term, see examples below. taxonRemarks can capture comments or notes about the taxon or name. verbatimIdentification should be used to record the taxon identification as it appeared in the original record, unaltered, with hybrid formulas, informal descriptions, misspellings, etc. A WoRMS LSID should be added in scientificNameID (strongly recommended term). The LSID used for scientificNameID should correspond to the name provided to the scientificName field, even if it is not the currently accepted scientific name. OBIS will use this identifier to pull the taxonomic information from the World Register of Marine Species (WoRMS) into OBIS and attach it to your dataset. This information includes: Taxonomic classification (kingdom through species) The accepted name in case of invalid names or synonyms AphiaID IUCN red list category LSIDs are persistent, location-independent, resource identifiers for uniquely naming biologically significant resources. More information on LSIDs can be found at www.lsid.info. For example, the WoRMS LSID for Solea solea is: urn:lsid:marinespecies.org:taxname:127160, and can be found at the bottom of each WoRMS taxon page, e.g. Solea solea. kingdom and taxonRank can help us in identifying the provided scientificName in case the name is not available in WoRMS. kingdom in particular can help us find alternative genus-species combinations and avoids linking the name to homonyms. Please contact the WoRMS data management team (info@marinespecies.org) in case the scientificName is missing in WoRMS. kingdom and taxonRank are not necessary when a correct scientificNameID is provided. OBIS recommends providing information about how an identification was made, for example by which ID key, species guide or expert; and by which method (e.g morphology vs. genomics), etc. The person’s name who made the taxonomic identification can go in identifiedBy and when in dateIdentified. Use the ISO 8601:2004(E) standard for date and time, for instructions see Time. A list of references, such as field guides used for the identification, can be listed in identificationReferences. Any other information, such as identification methods, can be added to identificationRemarks. Examples: scientificNameID scientificName kingdom phylum class order family genus specificEpithet scientificNameAuthorship urn:lsid:marinespecies.org:taxname:142004 Yoldiella nana Animalia Mollusca Bivalvia Nuculanoida Yoldiidae Yoldiella nana (Sars M., 1865) urn:lsid:marinespecies.org:taxname:140584 Ennucula tenuis Animalia Mollusca Bivalvia Nuculoida Nuculidae Ennucula tenuis (Montagu, 1808) urn:lsid:marinespecies.org:taxname:131573 Terebellides stroemii Animalia Annelida Polychaeta Terebellida Trichobranchidae Terebellides stroemii Sars, 1835 Data from Benthic fauna around Franz Josef Land. If the record represents a nomenclatural type specimen, the term typeStatus can be used, e.g. for holotype, syntype, etc. In case of low confidence identifications, and the scientific name contains qualifiers such as cf., ? or aff., then this name should go in identificationQualifier, and scientificName should contain the name of the lowest possible taxon rank that refers to the most accurate identification. E.g. if the specimen was accurately identified down to genus level, but not species level, then the scientificName should contain the name of the genus, the scientificNameID should contain the LSID the genus and the identificationQualifier should contain the low confidence species name combined with ? or other qualifiers. The table below shows a few examples: The use and definitions for additional ON signs (identificationQualifier) can be found in Open Nomenclature in the biodiversity era, which provides examples for using the main Open Nomenclature qualifiers associated with physical specimens. The publication Recommendations for the Standardisation of Open Taxonomic Nomenclature for Image-Based Identifications provides examples and definitions for identificationQualifiers for non-physical specimens (image-based). Examples: scientificName scientificNameAuthorship scientificNameID taxonRank identificationQualifier verbatimIdentification Pelagia Péron & Lesueur, 1810 urn:lsid:marinespecies.org:taxname:135262 genus gen. nov. Pelagia gen. nov. Pelagia benovici Piraino, Aglieri, Scorrano & Boero, 2014 urn:lsid:marinespecies.org:taxname:851656 species sp. nov Pelagia benovici sp. nov Gadus Linnaeus, 1758 urn:lsid:marinespecies.org:taxname:125732 genus cf. morhua Gadus cf. morhua Polycera Cuvier, 1816 urn:lsid:marinespecies.org:taxname:138369 genus cf. hedgpethi Polycera cf. hedgpethi Tubifex Lamarck, 1816 urn:lsid:marinespecies.org:taxname:137392 genus ? Tubifex tubifex(Müller, 1774)? Tubifex Lamarck, 1816 urn:lsid:marinespecies.org:taxname:137392 genus sp. inc. Tubifex tubifex(Müller, 1774)sp. inc. Brisinga Asbjørnsen, 1856 urn:lsid:marinespecies.org:taxname:123210 genus gen. inc. Brisinga gen. inc. Uroptychus compressus Baba & Wicksten, 2019 urn:lsid:marinespecies.org:taxname:1332465 genus sp. inc. Uroptychus compressus sp. inc. Eurythenes S. I. Smith in Scudder, 1882 urn:lsid:marinespecies.org:taxname:101607 genus sp. DISCOLL.PAP.JC165.674 Eurythenes sp.DISCOLL.PAP.JC165.674 Paroriza Hérouard, 1902 urn:lsid:marinespecies.org:taxname:123467 genus sp.[unique123]aff.pallens Paroriza sp.[unique123]aff. pallens Aristeidae Wood-Mason in Wood-Mason & Alcock, 1891 urn:lsid:marinespecies.org:taxname:106725 family stet. Aristeidae stet. Nematocarcinus Milne-Edwards, 1881 urn:lsid:marinespecies.org:taxname:107015 genus sp.indet. Nematocarcinus sp.indet. Brisinga Asbjørnsen, 1856 urn:lsid:marinespecies.org:taxname:123210 genus gen.inc. Brisinga gen.inc. Brisinga costata Verrill, 1884 urn:lsid:marinespecies.org:taxname:17825 species sp.inc. Brisinga costata sp.inc. 2.4.1.3.2 Occurrence occurrenceID (required term) is an identifier for the occurrence record and should be persistent and globally unique. If the dataset does not yet contain (globally unique) occurrenceIDs, then they should be created. Guideline for ID creation can be found here occurrenceStatus (required term) is a statement about the presence or absence of a taxon at a location. It is an important term, because it allows us to distinguish between presence and absence records. It is a required term and should be filled in with either present or absent. A few terms related to quantity: organismQuantity and organismQuantityType, have been added to the TDWG ratified Darwin Core. This is a lot more versatile than the older individualCount field. However, OBIS recommends to use the Extended MeasurementorFact extension for quantitative measurements because of the standardization of terms and the fact that you can link these measurements to sampling events and factual sampling information. Please take note that OBIS recommends all quantitative measurements and sampling facts to be placed in the ExtendedMeasurementOrFact extension and not in the Darwin Core files. In the case specimens were collected and stored (e.g. museum collections), the catalogNumber and preparations terms can be used to provide the identifier for the record in the collection and to document the preparation and preservation methods. The term typeStatus see above (under identification) can be used in this context too. Both associatedMedia, associatedReferences and associatedSequences are global unique identifiers, URLs, or URIs pointing to respectively associated media (e.g. online image or video), associated literature (e.g. DOIs) or genetic sequence information (e.g. GenBANK ID). It is recommended to ensure URLs include the domain name (e.g. NCBI) when possible. associatedTaxa include a list (concatenated and separated) of identifiers or names of taxa and their associations with the Occurrence, e.g. the species occurrence was associated to the presence of kelp such as Laminaria digitata. Data columns recording an organism’s sex, life stage, and/or behaviour should be populated with controlled vocabulary. It is recommended to include the columns in preferably both the Occurrence table and the extendedMeasurementOrFact table. The recommended vocabulary for sex can be found in BODC vocabulary collection S10, for lifeStage in BODC vocabulary collection S11, and for behavior in ICES Behaviour collection. occurrenceRemarks can hold any comments or notes about the Occurrence. recordedBy can hold a list (concatenated and separated) of names of people, groups, or organizations responsible for recording the original Occurrence. The primary collector or observer, especially one who applies a personal identifier (recordNumber), should be listed first. Example: collectionCode occurrenceID catalogNumber occurrenceStatus SluiceDock_benthic_1976/1981 SluiceDock_benthic_1976_1 SluiceDock_benthic_1976_1 present SluiceDock_benthic_1976/1981 SluiceDock_benthic_1976_2 SluiceDock_benthic_1976_2 present SluiceDock_benthic_1976/1981 SluiceDock_benthic_1979-07/1980-06_1 SluiceDock_benthic_1979-07/1980-06_1 present Data from A summary of benthic studies in the sluice dock of Ostend during 1976-1981. 2.4.1.3.3 Record level terms basisOfRecord (required term) specifies the nature of the record, i.e. whether the occurrence record is based on a stored specimen or an observation. A full list of vocabularies with definitions for this term can be found here. In case the specimen is collected and stored in a collection (e.g. at a museum, university, research institute), the options are: PreservedSpecimen e.g. preserved in ethanol, tissue etc. FossilSpecimen a fossil, which allows OBIS to make the distinction between the date of collection and the time period the specimen was assumed alive LivingSpecimen an intentionally kept/cultivated living specimen e.g. in an aquarium or culture collection. In case no specimen is deposited, the basis of record is either HumanObservation (e.g bird sighting, benthic sample but specimens were discarded after counting), MachineObservation (e.g. for occurrences based on automated sensors such as image recognition, etc), or MaterialSample (e.g. physical sample was taken, and may have been preserved or destroyed). For records pertaining to genetic samples, basisOfRecord can be MaterialSample (e.g. in the DNA-derived data extension). When the basisOfRecord is either a preservedSpecimen, LivingSpecimen or FossilSpecimen please also add the institutionCode, collectionCode and catalogNumber, which will enable people to visit the collection and re-examine the material. Sometimes, for example in case of living specimens, a dataset can contain records pointing to the origin, the in-situ sampling position as well as a record referring to the ex-situ collection. In this case please add the event type information in eventType (see OBIS manual: event). institutionCode identifies the custodian institute (often by acronym), collectionCode identifies the collection or dataset within that institute. Collections cannot belong to multiple institutes, so all records within a collection should have the same institutionCode. The collectionID is an identifier for the record within the dataset or collection. bibliographicCitation allows for providing different citations on record level, while a single citation for the entire dataset can and should be provided in the metadata (see EML). The citation at record level can have the format of a chapter in a book, where the book is the dataset citation. The record citation will have preference over the dataset citation. We do not, however, recommend to create different citations for every record, as this will explode the number of citations and will hamper the re-use of data. modified is the most recent date-time on which the resource was changed. It is required to use the ISO 8601:2004(E) standard, for instructions see Time. dataGeneralizations refers to actions taken to make the shared data less specific or complete than in its original form. Suggests that alternative data of higher quality may be available on request. This can be the case for occurrences of vulnerable or endangered species and there positions are converted to the center of grid cells. 2.4.1.3.4 Location decimalLatitude and decimalLongitude (required terms) are the geographic latitude and longitude (in decimal degrees), using the spatial reference system given in geodeticDatum of the geographic center of a Location. The number of decimals should be appropriate for the level of uncertainty in coordinateUncertaintyInMeters (at least within an order of magnitude). coordinateUncertaintyInMeters is the radius of the smallest circle around the given position containing the whole location. Regarding decimalLatitude, positive values are north of the Equator, negative values are south of it. All values lie between -90 and 90, inclusive. Regarding decimalLongitude, positive values are east of the Greenwich Meridian, negative values are west of it. All values lie between -180 and 180, inclusive. In OBIS, the spatial reference system to be documented in geodeticDatum is EPSG:4326. Coordinates in degrees/minutes/seconds can be converted to decimal degrees using our coordinates tool. We also provide a tool to check coordinates or to determine coordinates for a location (point, transect or polygon) on a map. This tool also allows geocoding location names using marineregions.org. The name of the place or location can be provided in locality, and if possible linked by a locationID using a persistent ID from a gazetter, such as the MRGID from MarineRegions. If the species occurrence only contains the name of the locality, but not the exact coordinates, we recommend using a geocoding service to obtain the coordinates. Marine Regions has a search interface for geographic names, and provides coordinates and often precision in meters, which can go into coordinateUncertaintyInMeters. Another option is to use the Getty Thesaurus of Geographic Names or Google Maps: after looking up a location, the decimal coordinates can be found in the page URL. Additional information about the locality can also be stored in DwC terms such as waterBody, islandGroup, island and country. locationAccordingTo should provide the name of the gazetteer that is used to obtain the coordinates for the locality. When country is provided, countryCode should also be populated and must adhere to ISO 3166-1-alpha-2 (i.e. 2 letter country codes defined by ISO 3166-1). For locations in international waters, we recommend the code XZ. locationID is an identifier for the set of location information (e.g. station ID, or MRGID from marineregions), for example the Balearic Plain has MRGID: http://marineregions.org/mrgid/3956. A Well-Known Text (WKT) representation of the shape of the location can be provided in footprintWKT. This is particularly useful for tracks, transects, tows, trawls, habitat extent or when an exact location is not known. WKT strings can be created using our WKT tool. This tool also calculates a midpoint and a radius, which can then be added to decimalLongitude, decimalLatitude, and coordinateUncertaintyInMeters respectively. There is also an R tool to calculate the centroid and radius for WKT polygons. wktmap.com can be used to visualize and share WKT strings. Some examples of WKT strings: LINESTRING (30 10, 10 30, 40 40) POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10)) MULTILINESTRING ((10 10, 20 20, 10 40),(40 40, 30 30, 40 20, 30 10)) MULTIPOLYGON (((30 20, 45 40, 10 40, 30 20)),((15 5, 40 10, 10 20, 5 10, 15 5))) Example: decimalLatitude decimalLongitude geodeticDatum coordinateUncertaintyInMeters footprintWKT footprintSRS 38.698 20.95 EPSG:4326 75033.17 LINESTRING (20.31 39.15, 21.58 38.24) EPSG:4326 42.72 15.228 EPSG:4326 154338.87 LINESTRING (16.64 41.80, 13.82 43.64) EPSG:4326 39.292 20.364 EPSG:4326 162083.27 LINESTRING (19.05 40.34, 21.68 38.25) EPSG:4326 Data from Adriatic and Ionian Sea mega-fauna monitoring employing ferry as platform of observation along the Ancona-Igoumenitsa-Patras lane, from December 2014 to December 2018. Keep in mind while filling in minimumDepthInMeters and maximumDepthInMeters that this should be the depth at which the sample was taken and not the water column depth at that location. When filling in any depth fields (minimumDepthInMeters, maximumDepthInMeters, minimumDistanceAboveSurfaceInMeters, and maximumDistanceAboveSurfaceInMeters), you should also consider which information is needed to fully understand the data. In most cases (e.g. scenario 1 and 4 in the figure below), providing minimumDepthInMeters and maximumDepthInMeters is sufficient for observations of organisms at particular depths. However, in cases where an occurrence is above the sea surface, e.g. flying birds (scenario 2 and 5), you should populate minimumDistanceAboveSurfaceInMeters, maximumDistanceAboveSurfaceInMeters, and, where relevant, you should also include minimumElevationInMeters and maximumElevationInMeters. The minimumDistanceAboveSurfaceInMeters and maximumDistanceAboveSurfaceInMeters is the distance, in meters, above or below a reference surface or reference point. The reference surface is determined by the depth or elevation. If the depth and elevation are 0, then the reference surface is the sea surface. If a depth is given (i.e. value >0), the reference surface is the location of the depth. This can be especially useful for sediment cores taken from the sea bottom (scenario 3 in figure and table below). In these cases the minimumDistanceAboveSurfaceInMeters/maximumDistanceAboveSurfaceInMeters would be a negative value. If no depth is given, then the elevation is the reference surface (scenario 5). Example values for each depth, distance above surface, and elevation field for the scenarios in the figure above are provided in the table below: Scenario minimumDepthInMeters maximumDepthInMeters minimumDistanceAboveSurfaceInMeters maximumDistanceAboveSurfaceInMeters minimumElevationInMeters maximumElevationInMeters 1 fish 40 40 - - 0 0 1 crab 90 90 - - 0 0 2 0 0 10 15 0 0 3 100 100 0 -1.5 0 0 4 20 22 - - 0 0 5 0 0 10 15 10 10 2.4.1.3.5 Event eventID is an identifier for the sampling or observation event. parentEventID is an identifier for a parent event, which is composed of one or more sub-sampling (child) events (eventIDs). See identifiers for details on how these terms can be constructed. eventType should be used to describe the type of event and best practice is to use controlled vocabulary (e.g. Sample, Observation, Site Visit, Survey, Project, etc.). This can help distinguish event hierarchy, e.g. Sample nested within Site Visit, Site Visited nested within Survey, Survey nested within Expedition. Note that previous recommendation was to use type or eventRemarks to hold this information. With the ratification of the eventType term in 2023, we now recommend using eventType. type should only be used to describe the record or resource type (e.g. Sound, MovingImage, etc.). habitat is a category or description of the habitat in which the Event occurred (e.g. benthos, seamount, hydrothermal vent, seagrass, rocky shore, intertidal, ship wreck etc.) samplingProtocol, sampleSizeValue, sampleSizeUnit, and samplingEffort can be included in the Event table, but OBIS also recommends to add these sampling facts to eMoF. Terms are used to respectively describe: the names/descriptions/references to sampling methods or protocols used in the event; a numeric value associated with the sample size in an event (e.g. describing the time duration, length, area, or volume); the unit associated with the sampleSizeValue; the effort expended during an event (e.g. 5 observer hours). 2.4.1.3.6 Time The date and time at which an occurrence was recorded goes in eventDate. This term uses the ISO 8601 standard and OBIS recommends using the extended ISO 8601 format with hyphens. More specific guidelines on formatting dates and times can be found in the Common Data formatting issues page 2.4.1.3.7 Sampling Information on sampleSizeValue and sampleSizeUnit is very important when an organism quantity is specified. However, with OBIS-ENV-DATA it was felt that the extended MeasurementorFact (eMoF) extension would be better suited than the DwC Event Core to store the sampled area and/or volume because in some cases sampleSize by itself may not be detailed enough to allow interpretation of the sample. For instance, in the case of a plankton tow, the volume of water that passed through the net is relevant. In case of Niskin bottles, the volume of sieved water is more relevant than the actual volume in the bottle. In these examples, as well as generally when recording sampling effort for all protocols, eMoF enables greater flexibility to define parameters, as well as the ability to describe the entire sample and treatment protocol through multiple parameters. eMoF also allows you to standardize your terms to a controlled vocabulary. The next chapter deals with the metadata (description of the dataset) in Ecological Metadata Language. "],["data_format.html", "", " 2.4.2 Darwin Core Archive Contents Darwin Core Archive OBIS holds more than just species occurrences: the ENV-DATA approach ExtendedMeasurementOrFact Extension (eMoF) eDNA & DNA derived data Extension A special case: habitat types Recommended reading 2.4.2.1 Darwin Core Archive Darwin Core Archive (DwC-A) is the standard for packaging and publishing biodiversity data using Darwin Core terms. It is the preferred format for publishing data in OBIS and GBIF. The format is described in the Darwin Core text guide. A Darwin Core Archive contains a number of text files, including data tables formatted as CSV. The conceptual data model of the Darwin Core Archive is a star schema with a single core table, for example containing occurrence records or event records, at the center of the star. Extension tables can optionally be associated with the core table. It is not possible to link extension tables to other extension tables (to form a so-called snowflake schema). There is a one-to-many relationship between the core and extension records, so each core record can have zero or more extension records linked to it, and each extension record must be linked to exactly one core record. Definitions for the core and extension tables can be found here. Besides data tables, a Darwin Core Archive also contains two XML files: one file which describes the archive and data file structure (meta.xml), and one file which contains the dataset’s metadata (eml.xml). Figure: structure of a Darwin Core Archive. 2.4.2.2 OBIS holds more than just species occurrences: the ENV-DATA approach Data collected as part of marine biological research often include measurements of habitat features (such as physical and chemical parameters of the environment), biotic and biometric measurements (such as body size, abundance, biomass), as wel as details regarding the nature of the sampling or observation methods, equipment, and sampling effort. In the past, OBIS relied solely on the Occurrence Core, and additional measurements were added in a structured format (e.g. JSON) in the Darwin Core term dynamicProperties inside the occurrence records. This approach had significant downsides: the format is difficult to construct and deconstruct, there is no standardization of terms, and attributes which are shared by multiple records (think sampling methodology) have to be repeated many times. The formatting problem can be addressed by moving measurements to a MeasurementOrFacts extension table, but that doesn’t solve the redundancy and standardization problems. With the release and adoption of a new core type Event Core it became possible to associate measurements with nested events (such as cruises, stations, and samples), but the restrictive star schema of Darwin Core archive prohibited associating measurements with the event records in the Event core as well as with the occurrence records in the Occurrence extension. For this reason an extended version of the existing MeasurementOrFact extension was created. 2.4.2.2.1 ExtendedMeasurementOrFact Extension (eMoF) As part of the IODE pilot project Expanding OBIS with environmental data OBIS-ENV-DATA, OBIS introduced a custom ExtendedMeasurementOrFact or eMoF extension, which extends the existing MeasurementOrFact extension with 4 new terms: occurrenceID measurementTypeID measurementValueID measurementUnitID The occurrenceID term is used to circumvent the limitations of the star schema, and link measurement records in the ExtendedMeasurementOrFact extension to occurrence records in the Occurrence extension. Note that in order to comply with the Darwin Core Archive standard, these records still need to link to an event record in the Event core table as well. Thanks to this term we can now store a variety of measurements and facts linked to either events or occurrences: organism quantifications (e.g. counts, abundance, biomass, % live cover, etc.) species biometrics (e.g. body length, weight, etc.) facts documenting a specimen (e.g. living/dead, behaviour, invasiveness, etc.) abiotic measurements (e.g. temperature, salinity, oxygen, sediment grain size, habitat features) facts documenting the sampling activity (e.g. sampling device, sampled area, sampled volume, sieve mesh size). Figure: Overview of an OBIS-ENV-DATA format. Sampling parameters, abiotic measurements, and occurrences are linked to events using the eventID (full lines). Biotic measurements are linked to occurrences using the new occurrenceID field of the ExtendedMeasurementOrFact Extension (dashed lines). 2.4.2.2.2 eDNA & DNA derived data Extension DNA derived data are increasingly being used to document taxon occurrences. To ensure these data are useful to the broadest possible community, GBIF published a guide entitled Publishing DNA-derived data through biodiversity data platforms. This guide is supported by the DNA derived data extension for Darwin Core, which incorporates MIxS terms into the Darwin Core standard. eDNA and DNA derived data is linked to occurrence data with the use of occurrenceID and/ or eventID. Refer to the Examples: ENV-DATA and DNA derived data for use case examples of eDNA and DNA derived data. 2.4.2.2.3 A special case: habitat types Including information on habitats (biological community, biotope, or habitat type) is possible and encouraged with the use of Event Core. However, beware the unconstrained nature of the terms measurementTypeID, measurementValueID, and measurementUnitID which can lead to inconsistently documented habitat measurements within the Darwin Core Archive standard. To ensure this data is more easily discoverable, understood or usable, refer to Examples: habitat data and/or Duncan et al. (2021) for use case examples and more details. 2.4.2.2.4 Recommended reading De Pooter et al. 2017. Toward a new data standard for combined marine biological and environmental datasets - expanding OBIS beyond species occurrences. Biodiversity Data Journal 5: e10989. hdl.handle.net/10.3897/BDJ.5.e10989 Duncan et al. (2021). A standard approach to structuring classified habitat data using the Darwin Core Extended Measurement or Fact Extension. EMODnet report. (Note you must refine search to Technical Reports from 2021 to identify Duncan et al.’s report) "],["relational_db.html", "", " 2.4.3 Relational databases: the underlying framework of OBIS If you are not familiar with relational databases, it can be difficult to understand the underlying framework OBIS relies on. This section will help you understand relational databases, how they relate to OBIS, the data you will format for OBIS, and the data you may download from OBIS. Why do we use relational databases in the first place? You are probably familiar with flat databases which contain all data in one table - this is likely how your own data are formatted. Relational databases instead consist of multiple data tables that each contain related information. When all this information is presented in one table, the table becomes larger, very complicated, and the likelihood of data duplication increases. Relational databases seek to simplify complexities and reduce redundancy by allowing information to be self-contained, but linked to each other. You can think of a relational database as separate Excel sheets or data tables that are related to each other. One data table could be a “core” table, whereas others are “extensions”. Sometimes the relationships between core and extension tables are hierarchical, but this is not always the case. There is, however, always a relationship linking core and extension tables. Let’s review core and extension tables and how we use them for OBIS. Core tables contain information that is applicable to all extension tables, and extension tables contain more information about the records within the Core table. Each table, whether core or extension, contains records and attributes. Each row is a record (e.g., a sampling event, a species’ occurrence), whereas each column is an attribute (e.g., a date, a measurement). Records between tables are linked to each other by the use of identifiers. A description of measurements pertaining to a record in an Extension table will have the same identifier as the record it is describing in the Core table. By using identifiers to link records, we reduce data repetition, see below for examples. In the Darwin Core format that OBIS uses, the core table is either Event or Occurrence, and datasets can have one, none, or more extension tables. Further explanation of data formatting in OBIS is covered in the Data Formatting section of the OBIS manual. Let’s review an example to fully understand how relational databases work. We will look at a simple relational database used by a fictional country that tracks student performance in three different courses between three schools. Rather than trying to contain information about each school, course, and student performance in one place, this information is split into three separate tables. We see that the pink table gives us information about each school - its name, and the district it belongs to. Each school also has a schoolID, an identifier linking to the blue table where we can see student performance (course mean) in each course, the class size, and year. You will notice that the course mean and class size are bundled under columns called measurementType and measurementValue. These are similar to the eMoF vocabularies and are integral to reducing repeated data, especially when one dataset has reoccurring information. Finally we see that the courseID in the blue table links to the yellow one with the courseID identifier, giving us information about each course. A fourth table could easily be created to track total school population size through time. In contrast, if this information was only presented in the pink Schools in Country table, the school information would be duplicated as you add rows for each year. In this way, you can easily see how useful relational databases are. Of course, this is a simplified example but it demonstrates how related tables can be linked by identifiers to reduce table complexity and data replication. We elaborate on how this structure is applied within OBIS here. An example of how a relational database works. Three tables show the (1) student performance (blue table) in (2) different schools (pink table) in a fictional country, and (3) the names of the courses (yellow table). Information between each table is linked by the use of identifiers, indicated by the arrows Note that when OBIS harvests data, datasets are flattened - i.e., all separate data tables are combined into one. This is the kind of file you will receive when you download data from OBIS. The reason for this is that querying relational databases significantly reduces computational time, as opposed to querying a flat database. Relational databases also facilitate requests for subsets that meet particular criteria - e.g., all data from Norway for one species above a certain depth. 2.4.3.1 How to avoid redundancy Avoiding redundancy and data duplication within your dataset is built into the OBIS data structure. Utilizing the ENV-DATA approach, which delineates relationships between the core table and extension tables, we can limit the repetition of data. For example, let us consider the dates of a ship cruise where a series of bottom trawls were taken. The sampling information (e.g., date range, equipment used, etc.) for each species collected in these trawls is the same. Because of this, we know we are dealing with unique sampling events and thus we will use Event core. So, our Event core table will contain all information related to the sampling events (e.g., date, location). Then, information pertaining to each collected species (e.g., abundance, biomass, sampling methods, etc.) will be placed in an extension, the (Extended)MeasurementOrFact table. Here, each measurement for each species and sample will occur on a separate record. These records will be linked to the correct sampling event in the Event core by an identifier - the eventID. If we were to put this data in one file, the fields related to date and location (e.g., eventDate, decimalLongitude, decimalLatitude, etc.) would be repeated for each species. Let’s consider another example. If you took one temperature measurement from the water column where you took your sample, each species found in that sample would have the same temperature measurement. By linking such measurements to the event instead of each occurrence, we are able to reduce the amount of data being repeated. Example of how sample data is distributed to core and extension tables, and how these tables are connected in OBIS An advantage of structuring data this way is that if any mistakes are made, you only need to correct it once! So you can see that using relational event structures (when applicable) in combination with extension files can really simplify and reduce the number of times data are repeated. Caveat: However we would like to note that in some cases, data duplication may occur due to the star schema structure. For example, when publishing DNA-derived data, Occurrence core will have to be used, which necessitates the repetition of event data for each occurrence record. "],["eml.html", "", " 2.4.4 Ecological Metadata Language Content Introduction Gudiance on specific metadata sections Title Abstract People and Organizations License and IP Rights Coverage (geographic, taxonomic, temporal) Keywords Project Sampling methods Citations Bibliography Collection Data External Links 2.4.4.1 Introduction to EML OBIS (and GBIF) uses the Ecological Metadata Language (EML) as its metadata standard, which is specifically developed for the earth, environmental and ecological sciences. It is based on prior work done by the Ecological Society of America and associated efforts. EML is implemented as XML. See more information on EML. When publishing on the Integrated Publishing Toolkit (IPT), you can either upload XML files with your metadata or it will be generated for you after filling in metadata sections directly on the IPT. There are a few resources available to help you generate XML EML files, including: EML R package, ezEML, and Jinja EML template generator. OBIS uses the GBIF EML profile (version 1.1). In case data providers use ISO19115/ISO19139, there is a mapping available here. For OBIS, the following 4 terms are the bare minimum required: Title, Citation, Contact and Abstract. Below is an overview of all the EML terms used to describe datasets: title [xml:lang=\"...\"]: A good descriptive title is indispensable and can provide the user with valuable information, making the discovery of data easier. Multiple titles may be provided, particularly when trying to express the title in more than one language (use the “xml:lang” attribute to indicate the language if not English/en). creator ; metadataProvider ; associatedParty ; contact : These are the people and organizations responsible for the dataset resource, either as the creator, the metadata provider, contact person or any other association. The following details can be provided: individualName givenName surName organizationName: Name of the institution. positionName: to be used as alternative to persons names (leave individualName blank and use positionName instead e.g. data manager). address deliveryPoint city administrativeArea postalCode country phone electronicMailAddress onlineUrl : personal website role: used with associatedParty to indicate the role of the associated person or organization. userID: e.g. ORCID. directory pubDate: The date that the resource was published. Use ISO 8601. language: The language in which the resource (not the metadata document) is written. Use ISO language code. abstract : Brief description of the data resource. para keywordSet keyword : Note only one keyword per keyword field is allowed. keywordThesaurus : e.g. ASFA additionalInfo : OBIS checks this EML field for harvesting. It should contain marine, harvested by iOBIS. para coverage geographicCoverage geographicDescription: a short text description of the area. E.g. the river mounth of the Scheldt Estuary. boundingCoordinates westBoundingCoordinate eastBoundingCoordinate northBoundingCoordinate southBoundingCoordinate temporalCoverage : Use ISO 8601 singleDateTime rangeOfDates beginDate calendarDate endDate calendarDate taxonomicCoverage: taxonomic information about the dataset. It can include a species list. generalTaxonomicCoverage taxonomicClassification taxonRankName taxonRankValue commonName intellectualRights: Statement about IPR, Copyright or various Property Rights. Also read the guidelines on the sharing and use of data in OBIS. para purpose: A description of the purpose of this dataset. para methods methodStep: Descriptions of procedures, relevant literature, software, instrumentation, source data and any quality control measures taken. sampling: Description of sampling procedures including the geographic, temporal and taxonomic coverage of the study. studyExtent: Description of the specific sampling area, the sampling frequency (temporal boundaries, frequency of occurrence), and groups of living organisms sampled (taxonomic coverage). samplingDescription: Description of sampling procedures, similar to the one found in the methods section of a journal article. para qualityControl: Description of actions taken to either control or assess the quality of data resulting from the associated method step. project title identifier personnel: The personnel field is used to document people involved in a research project by providing contact information and their role in the project. description funding: The funding field is used to provide information about funding sources for the project such as: grant and contract numbers; names and addresses of funding sources. para studyAreaDescription designDescription: The description of research design. maintenance description para maintenanceUpdateFrequency additionalMetadata metadata dateStamp: The dateTime the metadata document was created or modified (ISO 8601). metadataLanguage: The language in which the metadata document (as opposed to the resource being described by the metadata) is written hierarchyLevel citation : A single citation for use when citing the dataset. The IPT can also auto-generate a citation based on the metadata (people, title, organization, onlineURL, DOI etc). bibliography: A list of citations that form a bibliography on literature related / used in the dataset resourceLogoUrl: URL of the logo associated with a dataset. parentCollectionIdentifier collectionIdentifier formationPeriod: Text description of the time period during which the collection was assembled. E.g., “Victorian”, or “1922 - 1932”, or “c. 1750”. livingTimePeriod: Time period during which biological material was alive (for palaeontological collections). specimenPreservationMethod physical objectName characterEncoding dataFormat externallyDefinedFormat formatName distribution: URL links online url function=\"download\" url function=\"information\" alternateIdentifier: It is a Universally Unique Identifier (UUID) for the EML document and not for the dataset. This term is optional. 2.4.4.2 Metadata Sections There are several categories/pages for metadata you must provide, which includes basic information about the: Dataset and data provider Geographic/taxonomic/temporal coverage Keywords Hosting institution information Information regarding associated project(s) Sampling methods How to cite the dataset Museum collection (if applicable) Other external links (e.g. a homepage) or additional metadata We review each of these sections below. 2.4.4.2.1 Title The IPT requires you to provide a Shortname. Shortnames serve as an identifier for the resource within the IPT installation (so should be unique within your IPT), and will be used as a parameter in the URL to access the resource via the Internet. Please use only alphanumeric characters, hyphens, or underscores. E.g. largenet_im in http://ipt.vliz.be/eurobis/resource?r=largenet_im. After creating a new dataset resource, the field title will be filled out with the short name you provided earlier. Please make sure you provide a dataset title following the guidelines below. Dataset titles provided to OBIS node managers are often very cryptic, such as an acronym, and often only understandable by the data provider. However, to increase the discoverability and be useful for a larger audience, the dataset title should be as descriptive and complete as possible. OBIS recommends titles to contain information about the taxonomic, geographic and temporal coverage. If the dataset title does not meet these criteria and you believe the title should be changed, then contact the data provider with a suggestion or ask for a more descriptive title. If the dataset has already been published (made publicly available) - and therefore known by that title elsewhere, then the same title should be kept (even if it would not meet the proposed guidelines)! Changing the title of an already published dataset cannot be done, as this will generate confusion and possible duplicates in systems like OBIS or GBIF in a later stage. The acronym or working title could still be documented in the metadata, so there is no confusion about how the full title is linked to the originally provided acronym or working title. Caution: Always consult the data provider when changing a dataset title to a more workable and descriptive version. Originally received title Title Recommended by Node Manager BIOCEAN BIOCEAN database on deep sea benthic fauna Biomôr Benthic data from the Southern Irish Sea from 1989-1991 Kyklades Zoobenthos of the Kyklades (Aegean Sea) REPHY Réseau de Surveillance phytoplanctonique 2.4.4.2.2 Abstract The abstract or description of a dataset provides basic information on the content of the dataset. The information in the abstract should improve understanding and interpretation of the data. It is recommended that the description indicates whether the dataset is a subset of a larger dataset and – if so – provide a link to the parent metadata and/or dataset. If the data provider or OBIS node require bi- or multilingual entries for the description (e.g. due to national obligations) then the following procedure can be followed: Indicate English as metadata language Enter the English description first Type a slash (/) Enter the description in the second language Example: The Louis-Marie herbarium grants a priority to the Arctic-alpine, subarctic and boreal species from the province of Quebec and the northern hemisphere. This dataset is mainly populated with specimens from the province of Quebec. / L’Herbier Louis-Marie accorde une priorité aux espèces arctiques-alpines, subarctiques et boréales du Québec, du Canada et de l’hémisphère nord. Ce jeu présente principalement des spécimens provenant du Québec. 2.4.4.2.3 People and Organizations The EML has several possible roles/functions to describe a contact, creator, metadata provider and associated party. The contact is the person or organization that curates the resource and who should be contacted to get more information or to whom questions with the resource or data should be addressed. Although a number of fields are not required, we strongly recommend providing as much information as possible, and in particular the email address. This will also be the contact information that appears on the OBIS metadata pages. The creator is the person or organization responsible for the original creation of the resource content. When there are multiple creators, the one that bears the greatest responsibility is the resource creator, and other people can be added as associated parties with a role such as ‘originator’, ‘content provider’, ‘principal investigator’, etc. Possible functions/roles: Originator (person/organization that originally gathered/prepared the dataset) Content provider (principal person/organization that contributed content to the dataset) If the resource contact and the resource creator are identical, the IPT allows you to easily copy the information. The metadata provider is the person or organization responsible for producing the resource metadata. If the metadata are provided by the original data provider, then his/her contact details should be filled in. If no metadata are available (e.g. for historical datasets, with no contact person), then the metadata can be completed by e.g. the OBIS node manager and the OBIS node manager becomes the metadata provider. The Associated Parties contains information about one or more people or organizations associated with the resource in addition to those already covered on the IPT Basic Metadata page. For example, if there would be multiple contact persons or metadata creators, they can be added in this IPT section. The principal contact/creator should, however, be added in the IPT Basic Metadata section, not the Associated Parties section. It is recommended to complete this section together with the IPT Basic Metadata page, to avoid confusion or overlap in added information. Possible functions/roles for associated parties are: Custodian steward (person/organization responsible for/takes care of the dataset paper) Owner (person/organization that owns the data – may or may not be the custodian) Point of contact (person/organization to contact for further information on the dataset) principal investigator (primary scientific contact associated with the dataset) Notes: The owner of a dataset will, in most cases, be an institute, and not an individual person. Although the fields ‘last name’, and ‘position’ are indicated as mandatory fields, it is possible to just add the institute name in the ‘last name’ field for the role ‘owner’. The contact persons in the metadata (contact, creator, metadata creator) are used in the dataset citation (auto-generation) and those added as ‘associated parties’ are not included as “co-authors”. 2.4.4.2.4 License and IP Rights OBIS has published its guidelines on the sharing and use of data here. The recommended licenses for datasets published in OBIS are the Creative Commons Licenses (CC-0, CC-BY, CC-BY-NC), of which CC-0 is the most preferred and CC-BY-NC is least preferred. A Creative Commons license means: You are free: to share => to copy, distribute and use the database to create => to produce works from the database to adapt => to modify, transform and build upon the database In case of CC-0: public domain: CC-0 is the preferred option identified by the OBIS steering group. You waive any copyright you might have over the data(set) and dedicate it to the public domain. You cannot be held liable for any (mis)use of the data either. Although CC-0 doesn’t legally require users of the data to cite the source, it does not take away the moral responsibility to give attribution, as is common in scientific research. A good blog on why using CC-0 can be found here. In case of CC-BY: Attribution: You must attribute any public use of the database, or works produced from the database, in the manner specified in the license. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database. In case of CC-BY-NC: non-commercial: like CC-BY but commercial use is not allowed. This licence can be problematic when the data is re-used in scientific journals. 2.4.4.2.5 Coverage 2.4.4.2.5.1 Geographic Coverage The IPT allows you to enter the geographic coverage by dragging the markers on the given map or by filling in the coordinates of the bounding box. In the description field, a more elaborate text can be provided to describe the spatial coverage indicating the larger geographical area where the samples were collected. For the latter, the sampling locations can be plotted on a map and – by making use of a Gazetteer – the wider geographical area can be derived: e.g. the relevant Exclusive Economic Zone (EEZ), IHO, FAO fishing area, Large Marine Ecosystem (LME), Marine Ecoregions of the World (MEOW), etc. The Marine Regions’ Gazetteer might prove to be a useful online tool to define the most relevant sea area(s). There are also LifeWatch Geographical Services that translate geographical positions to these wider geographical areas. The information given in this section can also help the OBIS node manager in geographic quality control. If the geographic coverage in the EML e.g. is “North Sea”, but a number of data points are outside of this scope, then this may indicate errors, and should be checked with the data provider. If the dataset covers multiple areas (e.g. samples from the North Sea and the Mediterranean Sea), then this should clearly be mentioned in the geographicDescription field. Note that the IPT only allows one bounding box, and you have to uncheck the “Set global coverage” box to change box bounds. Screenshot of the Geographical Coverage section of the metadata, emphasizing how to change the bounds of the coverage box in the map. 2.4.4.2.5.2 Taxonomic Coverage This section can capture two things: A description of the range of taxa that are addressed in the data set. OBIS recommends to only add the higher classification (Kingdom, Class or Order) of the involved groups (e.g. Bivalvia, Cetacea, Aves, Ophiuroidea…). You can easily draw a list of higher taxonomic ranks from the WoRMS taxon match service (or ask the data provider). The taxonomic coverage is not a mandatory field, but the information stored here can be very useful as background information. The description can also contain common names, such as e.g. benthic foraminifera or mussels. An overview of all the involved taxa (not recommended, as all the taxa are already listed in the dataset). OBIS also recommends to add information on the (higher) taxonomic groups in the (descriptive) dataset title and abstract. Example of the Taxonomic Coverage section of the metadata 2.4.4.2.5.3 Temporal Coverage The temporal coverage will be a date range, which can easily be documented. If it is a single date, the start and end date will be the same. The information added here can be used as a quality check for the actual dates in the datasets. You can also document the Formation Period or the Living Time Period in this section for specimens that may not have been alive during the collection period, or to indicate the time during which the collection occurred. Example of the Temporal Coverage section of the metadata 2.4.4.2.6 Keywords Relevant keywords facilitate the discovery of a dataset. An indication of the represented functional groups can help in a general search (e.g. plankton, benthos, zooplankton, phytoplankton, macrobenthos, meiobenthos …). Assigned keywords can be related to taxonomy, habitat, geography or relevant keywords extracted from thesauri such as the ASFA thesaurus, the CAB thesaurus or GCMD keywords. As taxonomy and geography are already covered in previous sections, there is no need to repeat related keywords here. Please consult your data provider which (relevant) keywords can be assigned. Example of the Keywords section of the metadata, showing input for a marine fishes dataset 2.4.4.2.7 Project If the dataset in this resource is produced under a certain project, the metadata on this project can be documented here. Part of the information entered here, can partly overlap with information given in other sections of the metadata (e.g. study area description can have lot of parallel with the geographic coverage section). Personnel involved in the project can be documented or repeated here as well. This is not a problem. 2.4.4.2.8 Sampling Methods The EML can contain descriptions of the sampling and data processing methods. Study extent can be documented here as well to report a more specific geographic area as well as the sampling frequency. Descriptions of sampling procedures, quality control, and steps (sample or data processing) can be given in the same way as the methods section of a scientific paper. OBIS best practice is to add sampling facts to the extended MeasurementorFact extension, linked to the sampling events in the Event core via eventID. 2.4.4.2.9 Citations The dataset citation allows users to properly cite your dataset in further publications or other uses of the data. When users download datasets from the OBIS download function, a list of the dataset citations packaged with the data in a zipped file is provided. A dataset citation is different from the data source citation (in case the data is digitized from a publication), and these references can be added to the additional metadata (see bibliography below). A dataset citation can have the same format of a journal article citation, and should include the authors (contact, creator, principle investigator, data managers, custodians, collectors…), the title of the dataset, the name of the data publisher (or custodian institute), and the access point URL to the resource. GBIF’s IPT has an auto-generation - Turn On/Off - tool to let the IPT auto-generate the resource citation for you. The citation includes a version number, which is especially important for datasets that are continuously updated. The dataset citation can also include a Citation Identifier - a DOI, URI, or other persistent identifier that resolves to an online dataset web page. The OBIS node data managers should try to implement a certain degree of format standardization for the dataset citations. The IPT provides an option to auto-generate a citation based on the EML and is formatted as follows: {dataset.authors} ({dataset.pubDate}) {dataset.title}. [Version {dataset.version}]. {organization.title}. {dataset.type} Dataset {dataset.doi}, {dataset.url} 2.4.4.2.10 Bibliography The EML can include the citation of the publications that are related to the described dataset. They can describe the dataset, be based on the dataset or be used in this dataset. Publications can be scientific papers, reports, PhD or master theses. If available, the citation should include the DOI at the end. This overview will contribute to a better understanding of the data as these publications can hold important additional information on the data and how they were acquired. 2.4.4.2.11 Collection Data This IPT section should only be filled out if there are specimens held in a museum. If relevant, it is strongly recommended that this information is supplied by the data provider or left blank. The collection name, specimen preservation method, and curatorial units should be provided, as applicable. Screenshot of the Collection Data page showing what information can be provided for museum specimens 2.4.4.2.12 External Links This section can include URLs to the resource homepage, to download or find additional information. You can also provide links to your resource if it is hosted elsewhere in different formats. Links to the online dataset on the OBIS website can be added once the data is available there. For these OBIS links, the required fields should be completed as follows: Name: online dataset Character set: UTF-8 Data format: html If other links are added, then the data format for web-based data is ‘html’. If the link refers to a file, the data format of the file will need to be added (e.g. .xlsx, .pdf …). The character set for all Darwin Core files is UTF-8, whereas for other web pages this can vary, so you may need to confirm. 2.4.4.2.13 Additional Metadata Any remaining information that could not be catalogued under any of the other metadata, can be mentioned here. This may include logos, purpose of the dataset, a description of how the dataset will be maintained, etc. "],["nodes.html", "3 OBIS Terms of Reference: Nodes, Coordination Groups, Secretariat 3.1 TOR of OBIS nodes 3.2 TOR of OBIS Steering Group 3.3 TOR of OBIS Co-Chairs 3.4 TOR of OBIS Secretariat 3.5 TOR of OBIS Coordination Groups 3.6 TOR of OBIS Executive Committee", " 3 OBIS Terms of Reference: Nodes, Coordination Groups, Secretariat Content OBIS Node Terms of Reference OBIS Steering Group Terms of Reference OBIS Co-Chair Terms of Reference OBIS Secretariat Terms of Reference Terms of Reference for OBIS Coordination Groups OBIS Executive Committee Terms of Reference 3.1 TOR of OBIS nodes OBIS Nodes are either national projects, programmes, institutes, or organizations, National Ocean Data Centers or regional or international projects, programmes and institutions or organizations that carry out data management functions. OBIS nodes Terms of Reference are detailed below. Required OBIS nodes can be national, regional or thematic Be an IODE National Oceanographic Data Centre (NODC), or IODE Associate Data Unit (ADU) Be responsible for ensuring the timely delivery of high-quality data (and metadata) and data flows from marine data providers to OBIS and/or other global aggregators (e.g. GBIF) following the principle of “publish once“, the guidelines in the OBIS manual (with respect to obtaining permission, licenses, QC…) and the FAIR Data Principles At least one staff member holds a current OBIS certification (completing recognized training OBIS/OTGA course “Contributing and publishing datasets to OBIS”) with a 12 month grace period for acquiring said certification after a Node is established Be compliant with the IOC data policy (http://iode.org/policy) and implementation guidelines from OBIS (Data Policy) with a 12 month grace period for achieving compliance after a Node is established Be a member of the Nodes Coordination Group (Nodes CG), and other OBIS coordination groups if desirable Provide an annual work plan to the Nodes CG and report on activities to the Nodes CG Provide user/community support (e.g data queries, data analyses, and feedback) Optional May maintain a presence on the Internet (e.g., a web page, social media or data portal) representing their specific area of responsibility May establish and/or join OBIS collaboration agreements with OBIS partners, and report on these collaborations to the Nodes CG Encouraged to undertake outreach and communication activities (e.g. social media, conferences), in accordance with the OBIS Communication Plan Encouraged to engage in capacity development activities in collaboration with the OBIS secretariat. These activities may include offering expertisel, conducting academic and professional training sessions, and providing support in various aspects of data management. Additionally, assistance in publishing data to global aggregators like OBIS and GBIF, utilizing relevant technologies, and applying our community standards and best practices Encouraged to engage in adjacent and affiliated user communities and actively seek new partners (e.g. data providers, data using partners) Notes: OBIS nodes are eligible to become a member of the SG and have voting rights. A formal letter expressing their commitment to contribute according to Terms of Reference of the SG must be renewed every 2 years OBIS nodes can become a member of and contribute to the various OBIS coordination groups 3.1.1 How to become an OBIS node OBIS nodes now operate under the IODE network as either National Oceanographic Data Centres (NODCs) or Associate Data Unites (ADUs). Prospective nodes are required to apply to the IODE for membership. The procedure to become an OBIS node is as follows: If you are an existing NODC (within the IODE network) and the OBIS node activities fall under the activities of the NODC: Send a letter expressing your interest to become an OBIS node (including contact information of the OBIS node manager, and geographical/thematic scope of your OBIS node) If you are not an existing NODC: Email your application form to become an IODE Associate Data Unit (ADU), with a specific role as OBIS node. Applications for ADU membership in OBIS shall be reviewed by the IODE Officers in consultation with the IODE Steering Group for OBIS. OBIS Node Health Status Check and Transition Strategy OBIS nodes should operate under IODE as either IODE/ADU or IODE/NODC. As such OBIS nodes are a member of the IODE network. The IODE Steering Group (SG) for OBIS evaluates the health status of OBIS nodes at each annual SG meeting, and considers an OBIS node as inactive when it meets any of the following conditions: The OBIS node manager recurrently fails to answer the communications from the project manager or the SG co-chairs in the last 12 months The OBIS node manager or a representative fails to attend (personally or virtually) the last 2 SG meetings without any written reason The OBIS node does not have an IPT The OBIS node has an IPT, but it has not been running for the last 12 months The datasets in the OBIS node’s IPT have been removed and not restored in the last 12 months (without any explanation) The OBIS node has not provided new data for the last 2 years The OBIS Secretariat prepares a health status check report of each OBIS node based on the six items above and informs the OBIS node manager on their status 3 months before the SG meeting. At the SG meeting, the SG-OBIS co-chair will present the results of the OBIS nodes health status check report including a listing of the inactive OBIS nodes. The SG-OBIS members representing active OBIS Nodes will make one of the following decisions: Request the inactive OBIS node to submit a plan with actions, deliverables and times to improve their performance, within 3 months, to the OBIS Secretariat. This plan is reviewed and accepted by the OBIS-Executive Committee Or Provide a recommendation to the IOC Committee on IODE to remove the OBIS node from the IODE network. In either case, the OBIS Secretariat will inform the OBIS node manager of the SG-OBIS decision, with a copy to the IODE officers and the IODE national coordinator for data management of the country concerned. The IODE Committee is requested to consider the recommendation from the OBIS Steering Group and it may either accept the recommendation or request the inactive OBIS node to submit an action plan (option 1). When the inactive OBIS node is removed from the IODE network, the SG-OBIS will ask whether another OBIS node is interested in taking over the responsibilities of the removed OBIS node, until a new OBIS node in the country/region is established. 3.2 TOR of OBIS Steering Group Required Propose, and revise as necessary, the vision, mission, objectives, strategies (e.g. sustainability), management structure, work plans, budgets and timetables for OBIS Review and respond to reports from OBIS coordination groups Monitor the implementation of the adopted work plan, identify any technical, scientific or capacity challenges and suggest appropriate responses Seek to support each other with resource mobilisation to advance the mission of OBIS together Report to the IODE Management Group (every year) and IODE Committee (every two years) Notes Regular meetings of the Steering Group will be held at least once a year. The Co-Chairs can decide, in consultation with the members, to have additional meetings if needed OBIS Steering Group decisions are made by consensus. Voting will only be organized if co-chairs identify that all means have been exhausted and no consensus can be reached with decisions accepted by two-thirds majority of members in attendance, with one vote per node in attendance Co-chairs can act on behalf of their Nodes for portions of these meetings if they explicitly state the role in which they are acting Membership Two Co-Chairs (2 seats), who are elected from the membership of the Steering Group Co-Chairs of the OBIS coordination groups (6 seats). Appointments shall be for a term of 2 years, renewable for one further term. OBIS nodes (up to 16 seats): OBIS node managers (or one alternate) who are not already represented as co-chairs or CG co-chairs. Each OBIS node should inform the OBIS secretariat of any changes to their representative(s) as soon as possible. OBIS nodes need reconfirmation after two 2-year terms OBIS secretariat (2 seats) OBIS Programme manager as SG-OBIS technical secretary, and OBIS technical coordinator 3.3 TOR of OBIS Co-Chairs The role of (Co-)Chair(s) is: To (Co-)Chair sessions of the OBIS Steering Group and OBIS Executive Committee To call for meetings of the OBIS Steering Group, in close consultation with the Programme Manager and/or IODE/OBIS Secretariat To assist the Programme Manager and/or IODE/OBIS Secretariat with the preparation of OBIS Programme Component reports To represent OBIS and its Steering Group, and to promote OBIS at meetings and other events Notes: Co-Chairs must act independently of other affiliations and will not represent their institution, node or country while performing co-chair duties. Co-Chairs may focus (in addition to the tasks outlined above) on particular elements of the OBIS work plan if they have specific expertise in those elements. Co-Chairs should expect to dedicate a minimum of 160 working hours per year to their duties, including monthly video conferences, one or more representations at IODE management group and Committee meetings, major international conferences or special events on behalf of OBIS, and leadership duties in preparation and execution of annual SG-OBIS meetings. Co-Chairs positions are not funded by IODE/OBIS, and SG-OBIS members seeking nomination to become Co-Chair are encouraged to obtain full support from their home institutions to cover time and expenses of the role as part of core duties. Travel support may be provided by the IODE OBIS Programme component when available. It is required that Programme Managers, IODE/OBIS Secretariat and OBIS Co-Chairs communicate frequently. Co-Chairs are elected based on their level of activity in the work of the Steering Group over a period of time, the respect they have gained from the membership of the Group and their professional expertise related to the subject of OBIS. Proficiency in the working language of the group is essential. Experience in mobilizing resources is an additional element but not a requirement. Elections of Co-Chairs can only be held during regular meetings of the Steering Group during which a quorum of the membership is present. Co-Chairs are elected for one inter-sessional period of the Steering Group (regular meeting), with the possibility of re-election for an additional term. In exceptional circumstances Co-Chairs (or one of them) can be re-elected for a third term. If more than one regular meeting is organized within a calendar year then the term of office will be one year. Exceptions (e.g. further extensions) are possible by a decision of the IODE Committee. In exceptional cases one or more Co-Chairs can be invited from outside the Steering Group if they can bring in exceptional expertise or experience or if no other candidates have come forward. In such cases the Steering Group will need to invite them as members first after which he/she/they can be elected (Co-)Chair(s). Non-voting members are ineligible to serve as Co-Chair. When (Co-)Chair(s) step down after one or two terms they may remain as members of the Steering Group and Executive Committee for the subsequent term. They should inform the OBIS Programme Manager at the time they inform of their decision to step down as co-chair. When Co-Chairs step down after one or two terms, they will become Past Co-Chairs and will be invited to provide guidance to the incoming Co-Chairs during the first term of the new Co-Chairs. This will be a voluntary service to the new Co-Chairs and the outgoing Co-Chairs may decline. Co-Chairs should inform the OBIS Programme Manager and IODE Secretariat of their desire to step down at least 6 months before the expiry of their term of office or next regular meeting of the Steering Group, whichever comes first. This will allow the Programme Manager and IODE Secretariat to start the election procedure for the new Co-Chairs. The Co-Chairs are not involved in the day-to-day implementation of OBIS which is the remit of the OBIS Programme Manager. Nominations for Co-Chairs shall be accepted from members of the SG-OBIS as either nomination on behalf of another or self-nominations. 3.4 TOR of OBIS Secretariat The OBIS secretariat, hosted at the UNESCO/IOC project office for IODE in Oostende (Belgium), provides training and technical assistance to its network of partners including OBIS nodes and data providers, guides and advices on the development of new data standards and technical developments, and encourages international cooperation and implement the OBIS work plan and oversees the budget, to foster the group benefits of the network. The OBIS Secretariat currently has 2 fixed term regular programme positions: an OBIS programme manager and an OBIS technical coordinator with the following tasks and objectives: The OBIS programme manager will: Coordinate the day-to-day planning and implementation of the OBIS work plan, including organizing meetings of the OBIS steering group, OBIS executive committee, OBIS coordination groups (data, products, and OBIS nodes), and supervising the OBIS secretariat staff. Coordinate the planning and implementation of other Extra Budgetary projects. Support the implementation of the IODE work plan and assist with the planning and implementation of the IODE/OBIS contribution to the UN Decade of Ocean Science for Sustainable Development. Establish, maintain, and strengthen (international) partnerships (e.g., GBIF, MBON, GOOS, MarineLife2030, CBD, BBNJ, IPBES etc). Ensure a performance management culture within the secretariat team. Through regular staff team meetings: listen, seek views, monitor performance, solve problems, and meet needs of colleagues and collaborators. More specifically: set clear objectives for the team and ensure effective/timely completion of all performance management related tasks. provide regular performance feedback on the implementation of set task objectives, including expected behavior and development needs. address all performance related issues in a timely manner and propose corrective measures for any under-performance. In cases of strong performance, commend staff and seek ways to leverage for improved overall team performance. Set professional and inter-personal development objectives. The OBIS technical coordinator will: Be responsible for the technical and scientific coordination, implementation and maintenance of the OBIS data system and planning of related programme activities. More specifically: In close collaboration with the Head of the IOC Project Office for IODE, OBIS programme component manager and OBIS team, establish technical programme goals, plan of activities, budget proposals, and performance measures - in accordance with technical guidance from the IODE Committee and OBIS coordination and steering groups. Coordinate and provide technical maintenance and further development of the OBIS technology stack and infrastructure; Manage the technical implementation of the OBIS work plan; Manage and provide support to the OBIS data and products coordination groups; Provide terms of reference for, and monitor/evaluate progress of, contractors hired to provide technical work; Collaborate closely with the Ocean Data and Information System (ODIS) team to ensure interoperability between ODIS and OBIS. Resource mobilization: Manage drafting technical and scientific aspects of extra-budgetary project proposals, as well as the technical and scientific implementation of approved extra-budgetary projects. Networking and Outreach: Participate in meetings of technical groups as well as the OBIS steering group and other relevant events; Assist with the promotion and communication of the OBIS programme component. Provide assistance with technical support of IODE databases and other IT services of, or hosted by, the IOC Project Office for IODE, Oostende, Belgium. 3.5 TOR of OBIS Coordination Groups Each of the coordination groups will have one or two Co-Chair(s) who will: Chair meetings according to the group’s agreed-upon schedule Ensure that tasks are appropriately distributed amongst the group members Coordinate their activities with the other coordination groups Report to and represent their group at the OBIS SG Participate/report in OBIS Executive Committee meetings Each group should also maintain a secretary position to promote consistent notetaking across meetings. All coordination groups should use the same collaborative tools, proposed by the Secretariat and available in all countries, to maintain a clear record of group activities and progress accessible to the Secretariat and members of the OBIS Coordination Groups. The Coordination Group reports will be made public. OBIS Nodes Coordination group The Nodes Coordination group (NCG) provides a forum for all OBIS nodes to discuss ongoing activities, priorities, and barriers they may be facing. Membership to this group is mandatory for all OBIS nodes, with at least one representative from each node attending. Meetings will occur every two months. Chairs will lead the membership in pursuing methods to promote communication from all parts of the world and overcome the challenge of time-zone coordination (e.g. leveraging asynchronous communication methods). Group activities will include: Facilitate inter-Node communication and exchange of expertise Define priority objectives to guide the efforts of OBIS Nodes Coordinate and distribute the work related to mobilisation of datasets Provide advice to the OBIS Steering Group on the OBIS science mission, policy, and management relevance and strategic priorities Identify and pursue new directions, potential pilot projects, potential resources, and areas of development for data-driven research and ocean policy and management applications, as well as investigate timely topics to help set future strategic directions This will be done in coordination with the Data Coordination Group and Products Coordination Group Coordinate communication of Node successes and achievements Provide annual OBIS node activity reports and mutual support for the challenges of individual nodes Identify and resolve training needs within the community Coordinate and implement any inter-Node related activities agreed-upon by the SG to achieve the OBIS mission OBIS Data Coordination group The OBIS Data Coordination Group (DCG) will focus on topics/issues related to OBIS Priority Area 1: Data mobilisation and input. The OBIS data coordination group will consist of two Co-Chairs and a body of voluntary members. There will be no limit to the number of members who can join. Members can include OBIS Sec, node staff, as well as interested or invited experts. The OBIS data coordination group will: Identify, prioritise, and propose solutions for issues around the following topics: Data and metadata standards and formats Data QC Taxonomy Vocabularies Methods of bringing new data into the system Identify data gaps and, in collaboration with the OBIS Nodes Coordination group, prioritise and coordinate data mobilisation efforts Ensure the fitness of quality metrics for assessing the current status of OBIS data (e.g. spatial and taxonomic completeness) Maintain the OBIS Manual; especially regarding standards or methods Engage with activities of other relevant bodies (e.g. TDWG, GBIF, GOOS, SCOR) by identifying representatives who will report back to the group OBIS Products Coordination group The OBIS Products Coordination group (PCG) is a collaborative and interdisciplinary group that is driven by the importance of creating data and information products (i.e. indicators) that are scientifically sound, practical, and relevant to decision-makers in government, industry, and civil society. PCG focuses on OBIS Priority Area 2: Data application and output. The PCG will consist of one or two co-chairs and a body of voluntary members. There will be no limit to the number of members who can join. Members can include OBIS Sec, node staff, as well as interested or invited experts. We consider data and information products any type of analysis (description, data visualisation, etc.) that synthesises and generates new information from data hosted on OBIS and other sources. The OBIS Products Coordination Group will: Identify, prioritise, and coordinate the development of data and information products that are of interest to our user community Advise on how best to showcase and catalogue data and information products developed by the wider OBIS community, in line with the OBIS Data Policy, including proper acknowledgment of other formats of resources (e.g. software applications, workflows, papers, etc.) Set minimum metadata and quality requirements for data and information products Propose and develop tools, pipelines, and documentation that can bolster the development of products based on OBIS data. Support groups/institutions working on products development (e.g. early warning systems, ecological synthesis groups, etc.) to identify potential collaborations Propose a process for frequent expert validation of data and information products by consulting with local scientific experts and end-users (including local communities and indigenous people) Engage with activities of other relevant bodies by identifying representatives and report back to the group 3.6 TOR of OBIS Executive Committee The OBIS Executive Committee will: Support the secretariat and assist with the management of OBIS Address issues that arise intersessionally Work with the secretariat to draft documents, monitor the work plan, and otherwise ensure that the work of the Steering Group and Coordination Groups progresses To monitor the implementation of the adopted work plan, identify any technical, scientific and capacity challenges and suggest appropriate responses Notes: The OBIS EC meets 4 times per year, online and in-person if necessary The OBIS EC can invite experts to join a meeting as required Members: SG-OBIS Co-Chairs and the most recent outgoing Co-Chair(s) Coordination groups Co-Chairs Secretariat/ Programme Manager "],["formatting.html", "4 Dataset structure 4.1 When to use Event Core 4.2 When to use Occurrence Core 4.3 Extensions in OBIS 4.4 Data formatting tools", " 4 Dataset structure Note: OBIS guidelines have not yet been updated to reflect changes that allow DNA Derived data to be linked to Event core tables. We will update this page soon. Formatting data can be challenging. This section of the manual deals with how to format data for OBIS, beginning with an overview of dataset structure. Determining how your dataset will be structured is one of the first steps towards getting your data ready for publishing. At this first step it is important to determine which structure best suits your dataset before proceeding because it will determine which Darwin Core fields will need to be included in your data. Once you have decided on the dataset structure, you can continue formatting the dataset. We have created the following flow chart for an overview on how to determine what structure best suits your data. Flow chart to help determine which dataset structure will suit your data For more guidance, see the sections below. 4.1 When to use Event Core Event Core describes when and where a specific sampling event happened and contains information such as location and date. Event Core is often used to organize your data tables when there are more than one sampling occasion and/or location, and different occurrences linked to each sampling. This organization follows the rationale of most ecological studies and typical marine sampling design. It covers: When specific details are known about how a biological sample was taken and processed. These details can then be defined in the eMoF Extension with the Q01 vocabulary When the dataset contains abiotic measurements, or other biological measurements which are related to an entire sample (not a single specimen). For example a biomass measurement for an entire sample, not each species within the sample Event Core can be used in combination with the Occurrence and eMoF extensions. The identifier that links Event Core to the extension is the eventID. parentID can also be used to give information on hierarchical sampling. occurrenceID can also be used in datasets with Event Core in order to link information between the Occurrence extension and the eMoF extension. 4.2 When to use Occurrence Core Occurrence Core datasets describe observations and specimen records and cover instances when: No information on how the data was sampled or how samples were processed is available No abiotic measurements are taken or provided You have eDNA and DNA-derived data Biological measurements are made on individual specimens (each specimen is a single occurrence record) Occurrence Core is also often the preferred structure for museum collections, citations of occurrences from literature, and sampling activities. Datasets formatted in Occurrence Core can use the eMoF Extension for when you have biotic measurements or facts about your specimen. The DNA derived data extension can also be used to link to DNA sequences. The identifier that links Occurrence Core to the extension(s) is the occurrenceID. 4.3 Extensions in OBIS Currently OBIS accepts the following extensions: Occurrence MeasurementOrFact extendedMeasurementOrFact DNADerivedData How are extensions linked to core tables in OBIS? As established in the relational database section, OBIS relies on datasets being formatted according to a relational database structure. The ENV-DATA approach that OBIS implements means your dataset will have a Core table and (optional) Extension tables. As a review, a core file contains information relevant and applicable to each record in the extension(s). An extension file then contains records that link back to a record in the core file with more specific information (e.g., methods, measurements, facts, DNA sequences, etc.). The extension file(s) accepted by OBIS ((e)MoF, Occurrence, DNA) are linked to your core tables by the use of identifying ID codes. These codes could be either eventID or occurrenceID. For details on how to construct these IDs, click here. See the diagram below for an example of how core and extension tables can be linked. Linking extensions via identifiers If your core file is based on occurrences (e.g., a record of one or more taxa specimens), then any extensions are linked with occurrenceID. If your core file is based on events (e.g., a sampling event, cruise, observation, etc.), then the linking identifier is eventID. In the Core tables, identifiers are always unique, which means, they do not repeat and each line has a different identifier. On the other hand, multiple records in an extension file can have the same identifier which will link them to the same event or occurrence record (depending on which is the Core). The different linking identifiers are shown in the figure below. Diagram of how the different core tables are linked to their extensions by different identifiers. Let us consider a fictional plankton trawl sampling event to demonstrate how identifiers link Core and Extension tables in OBIS. This trawl used two types of nets, occurred in March 2013, and has an eventID plankton-northsea-2013-03. Suppose we have information about the types of trawl used and the species abundance from this trawling event. The information (e.g., date) of the sampling event itself would be found in the Event Core, whereas the abundance data and sampling methods would be in the eMoF table. How do we ensure the abundance and sampling method data is properly linked to the correct event? By using the same eventID for each record in the eMoF table, plankton-northsea-2013-03, the information is properly linked between the Event Core and the eMoF extension. 4.4 Data formatting tools The GBIF Norwegian Node created the DwC Excel Template Generator. This tool will generate four different types of blank Excel spreadsheets: Occurrence Core, MeasurementOrFact, Metadata, and a README. This tool works best if you already know which Darwin Core fields you need, although a default template can be generated. Another tool from Norway is the Excel to Darwin Core Standard (DwC) Tool. This is a macro Excel spreadsheet that helps create templates for Event (aka Sampling-Event) and Occurrence core tables, as well as MeasurementsOrFacts, Extended MeasurementsOrFacts, and Simple Multimedia extensions. GBIF provides an Occurrence core template and an Event core template. If you use these templates from GBIF, be aware that GBIF’s required terms are different from OBIS. There are also some tools that can help you unpivot (or flatten) data tables. These can be used to flatten many columns into one, particularly useful for the eMoF table. GBIF Norway’s crosstab to list converter. Note that this tool is not completely automated Excel’s built-in unpivot function "],["identifiers.html", "4.5 Constructing and using identifier codes", " 4.5 Constructing and using identifier codes Content eventID occurrenceID 4.5.1 eventID Using a unique identifier for each physical sample or subsample in your dataset taken at each location and time is highly recommended to ensure sample traceability and data provenance. eventID is an identifier for an individual sampling or observation event, whereas parentEventID is an identifier for a parent event, which is composed of one or more sub-sampling (child) events (eventIDs). eventID can be used for replicated samples or sub-samples. It is important to make sure each replicate sample receives a unique eventID, which could be based on the unique sample ID in your dataset. Sample ID can also be recorded in materialSampleID, as OBIS does not need to have separate eventIDs and materialSampleIDs. Rather OBIS can treat these two terms as equivalent. Be sure to still fill in the eventID field if you want to use materialSampleID, as OBIS only uses eventID and parentEventID for structuring datasets, not sample ID. This does not prevent you from using the field if you would like to. If you do not already have a materialSampleID, creating a unique eventID for your data records can be as straightforward as combining different fields from your data. Note: You should consider carefully what combination of fields will generate a unique event. Combinations including date, time, location, and depth are common elements to help generate such unique codes. Including the event type can also be useful for datasets with hierarchical sampling methods (e.g., samples taken from a station within a cruise). Repeating the parentEventID in the child event (use : as delimiter) can make the structure of the dataset easier to understand. Nesting event information in this way also allows you to reduce redundancy and still provide information relevant to each level of sampling. Broadly, an eventID can take the form of [parentEventID]:[sample type]_[sample ID] Thus to construct a unique eventID for parent and child events, you join relevant sampling information. Possible configurations (with examples) could include: Project_cruise_station_date_sample STAR_arcticsea_st3520_1989-04-04_s01 Project_habitat_Genus_species_year_sampletype_samplenumber BEE_seamount_Genus_species_2013_cruise_s123 Institution_year_location_samplemethod_sample Concordia_2003_Coast_Station1_seine_s01 Concordia_2003_Coast_Station1_trap_s01 These examples are not exhaustive and other similarly structured variations that fit your data are acceptable. Consider also including year within your eventIDs to ensure codes remain globally unique in subsequent years, which is particularly useful if your sampling protocol is repeated temporally. Remember, what is the main information about a sampling event that helps you identify it? For instance, it is helpful when we know the location, date, project, habitat. So you can build your eventID code based on this information and ensuring they will not repeat (e.g., will result in a unique identifier). Information related to your sampling events can be assigned to the highest relevant event level in order to avoid repetition of information. For example, if all samples taken from a station occurred at the same depth, this information can be listed once. Variation between samples (e.g., exact time or coordinates) can also be easily reflected for each event. See the table below for a demonstration. eventID parentEventID eventRemarks eventDate maximumDepthInMeters cruise_1 cruise cruise_1:station_1 cruise_1 station 15 cruise_1:station_1:core_1 cruise_1:station_1 sample 2011-03-06T08:35 cruise_1:station_1:core_2 cruise_1:station_1 sample 2011-03-06T08:52 cruise_1:station_1:core_1:subsample_1 cruise_1:station_1:core_1 subsample We recommend using controlled vocabulary for the “type” column. Although no standards have been agreed upon yet, commonly used terms for event type included are cruise, stationVisit, transect, quadrat, sample, subSample. Consider another example from a real dataset below: eventID parentEventID eventDate eventRemarks IOF_benthos_Plominski_zaljev_2000_crs cruise IOF_benthos_Plominski_zaljev_2000_stat1 IOF_benthos_Plominski_zaljev_2000_crs 2000-08 stationVisit IOF_benthos_Plominski_zaljev_2000_stat2 IOF_benthos_Plominski_zaljev_2000_crs 2000-08 stationVisit IOF_benthos_Plominski_zaljev_2000_s01 IOF_benthos_Plominski_zaljev_2000_stat1 sample IOF_benthos_Plominski_zaljev_2000_s02 IOF_benthos_Plominski_zaljev_2000_stat2 sample Data from Environmental impact assessments in the eastern part of Adriatic sea - species list of benthic invertebrates and phytobenthos (2000-2010). We can see that each record has a similar eventID structure, except for the last part which indicates the event type - documented in the eventRemarks column. In this dataset, records with the eventID IOF_benthos_Plominski_zaljev_2000_crs has information applicable for records with eventIDs ending with _stat1, _stat2, _s01, and _s02 because _crs is their parent event. Similarly, information (e.g., date of station visit, coordinates) documented in records with eventID IOF_benthos_Plominski_zaljev_2000_stat1 is applicable for the two sample records (eventID _s01 and _s02), because these samples were taken at Station 1 (indicated by the parentEventID). These eventIDs could have been nested in another way, such as IOF_benthos_Plominsku_zaljev_2000_crs:stat1:s01 which would embed the parentEventID into the identifier. See also De Pooter et al. 2017 for an example of an event hierarchy in a complex benthos dataset. Watch this video for a demonstration on how to construct eventIDs: 4.5.2 occurrenceID occurrenceID is an identifier for occurrence records. Each occurrence record should have a globally unique identifier. Because occurrenceID is a required term, you may have to construct a persistent and globally unique identifier for each of your data records if none already exists (e.g., if records were not labeled with unique identifiers before, such as during sample processing or image/sensor detection). There are no standardized guidelines yet on designing the persistence of this ID, the level of uniqueness (from within a dataset to globally in OBIS), and the precise algorithm and format for generating the ID. But in the absence of a persistent globally unique identifier, one can be constructed by combining the institutionCode, the collectionCode and the catalogNumber (or autonumber in the absence of a catalogNumber). This is similar to how eventID is constructed. You may also follow Life Science Identifiers guidelines. Note that the inclusion of occurrenceID is also necessary for datasets in the OBIS-ENV-DATA format. An important consideration for museum specimens: there is the possibility that the institution a specimen is housed at may change. Therefore you may consider omitting institution identifiers within an occurrenceID, because occurrenceID should not change over time. See the example below: modified institutionCode collectionCode 2017-02-27 15:47:31 Ugent Vegetation_Gazi_Bay(Kenya)1987 2017-02-27 15:47:31 Ugent Vegetation_Gazi_Bay(Kenya)1987 2017-02-27 15:47:31 Ugent Vegetation_Gazi_Bay(Kenya)1987 basisOfRecord occurrenceID catalogNumber HumanObservation Ugent_Vegetation_Gazi_Bay(Kenya)1987_7553 Ugent_Vegetation_Gazi_Bay(Kenya)1987_7553 HumanObservation Ugent_Vegetation_Gazi_Bay(Kenya)1987_7554 Ugent_Vegetation_Gazi_Bay(Kenya)1987_7554 HumanObservation Ugent_Vegetation_Gazi_Bay(Kenya)1987_7555 Ugent_Vegetation_Gazi_Bay(Kenya)1987_7555 Data from Algal community on the pneumatophores of mangrove trees of Gazi Bay in July and August 1987. "],["checklist.html", "5 Formatting data tables 5.1 Darwin Core Term Checklist for OBIS", " 5 Formatting data tables 5.1 Darwin Core Term Checklist for OBIS There are many Darwin Core terms listed in the TDWG quick reference guide. However, not all these terms are necessary for publishing data to OBIS. For your convenience, we have created a checklist of all the Darwin Core terms relevant for OBIS data providers. You can reference this list to quickly see which terms are required by OBIS, which data table (Event, Occurrence, eMoF, DNA) they can be found in, and which Darwin Core class each term belongs to. These terms correlate with the IPT vocabulary mapping you will do when it comes time to publish your dataset. You may notice some terms are accepted in multiple data tables (e.g., Event and Occurrence) - this is because it depends on your dataset structure. If you have an Event Core, you will include some terms that would not be included if you had Occurrence Core. For guidance on specific class terms (e.g., location, taxonomy, etc.), see the Darwin Core section of the manual. Note that when you publish your dataset on the IPT, if you use a term not listed below it will be an unmapped field and will not be published alongside your data. You may still wish to include such fields in your dataset if you are publishing to other repositories, just know that they will not be included in your OBIS dataset. You may include this information either by putting it in the dynamicProperties field in JSON format, or putting the information into the eMoF. Alternatively, you may have fields that you do not wish to be published and that do not correspond to one of these terms (e.g. personal notes). This is okay - if they are not mapped to one of the terms, that column in your dataset will not be published. Table Legend: Terms marked with ^ indicate that the specified term is accepted in the Event or Occurrence table, but preference is that they be recorded in the eMoF table to link with controlled vocabulary For the Occurrence table, distinction is made if the term could be used in the core or the extension table x/x = term can be used in either Occurrence core or extension x/- term is recommended only for the Occurrence core -/x term is recommended only for the Occurrence extension Click here to download the checklist. Term OBIS Required Term’s DarwinCore Class Event Table Occurrence Table (Core/Extension) eMoF Table DNA Table eventDate required event x x/- eventID required event x -/x x decimalLatitude required location x x/- decimalLongitude required location x x/- occurrenceID required occurrence x/x x x occurrenceStatus required occurrence x/x basisOfRecord required record x/x scientificName required taxon x/x scientificNameID strongly recommended taxon x/x DNA_sequence strongly recommended dna x env_broad_scale strongly recommended dna x env_local scale recommended dna x env_medium strongly recommended dna x lib_layout recommended dna x nucl_acid_amp recommended dna x nucl_acid_ext recommended dna x otu_class_appr recommended dna x otu_db recommended dna x otu_seq_comp_appr recommended dna x pcr_primer_forward strongly recommended dna x pcr_primer_name_forward strongly recommended dna x pcr_primer_name_reverse strongly recommended dna x pcr_primer_reference strongly recommended dna x pcr_primer_reverse strongly recommended dna x samp_name recommended dna x samp_vol_we_dna_ext recommended dna x seq_meth recommended dna x sop recommended dna x target_gene strongly recommended dna x target_subfragment strongly recommended dna x day recommended event x x/- endDayOfYear recommended event x x/- eventType strongly recommended event x eventRemarks optional event x x/- eventTime recommended event x x/- fieldNotes optional event x fieldNumber optional event x habitat recommended event x x month strongly recommended event x x/- parentEventID required (if exists) event x -/x sampleSizeUnit strongly recommended event x^ x^/- x sampleSizeValue strongly recommended event x^ x^/- x samplingEffort strongly recommended event x^ x^/- x samplingProtocol strongly recommended event x^ x^/- x startDayOfYear recommended event x verbatimEventDate recommended event x year strongly recommended event x x/- bed optional geologicalContext x x/x earliestAgeOrLowestStage optional geologicalContext x x/x earliestEonOrLowestEonothem optional geologicalContext x x/x earliestEpochOrLowestSeries optional geologicalContext x x/x earliestEraOrLowestErathem optional geologicalContext x x/x earliestPeriodOrLowestSystem optional geologicalContext x x/x formation optional geologicalContext x x/x group optional geologicalContext x x/x highestBiostratigraphicZone optional geologicalContext x x/x latestAgeOrHighestStage optional geologicalContext x x/x latestEonOrHighestEonothem optional geologicalContext x x/x latestEpochOrHighestSeries optional geologicalContext x x/x latestEraOrHighestErathem optional geologicalContext x x/x latestPeriodOrHighestSystem optional geologicalContext x x/x lithostratigraphicTerms optional geologicalContext x x/x lowestBiostratigraphicZone optional geologicalContext x x/x member optional geologicalContext x x/x dateIdentified optional identification x/x identificationID optional identification x/x identificationQualifier recommended identification x/x identificationReferences optional (required for imaging data) identification x/x identificationRemarks recommended identification x/x identificationVerificationStatus optional (required for imaging data) identification x/x identifiedBy optional (required for imaging data) identification x/x identifiedByID optional identification x/x typeStatus optional identification x/x continent strongly recommended location x x/- coordinatePrecision strongly recommended location x x/- coordinateUncertaintyInMeters strongly recommended location x x/- country recommended location x x/- countryCode optional location x x/- county optional location x x/- footprintSpatialFit optional location x x/- footprintSRS optional location x x/- footprintWKT recommended location x x/- geodeticDatum recommended location x x/- georeferencedBy optional location x x/- georeferencedDate optional location x x/- georeferenceProtocol optional location x x/- georeferenceSources optional location x x/- higherGeography optional location x x/- higherGeographyID optional location x x/- island optional location x x/- islandGroup optional location x x/- locality recommended location x x/- locationAccordingTo recommended location x x/- locationID strongly recommended location x x/- locationRemarks recommended location x x/- maximumDepthInMeters strongly recommended location x x/- maximumDistanceAboveSurfaceInMeters optional location x x/- maximumElevationInMeters optional location x x/- minimumDepthInMeters strongly recommended location x x/- minimumDistanceAboveSurfaceInMeters optional location x x/- minimumElevationInMeters optional location x x/- municipality optional location x x/- pointRadiusSpatialFit optional location x x/- stateProvince optional location x x/- verbatimCoordinates optional location x x/- verbatimCoordinateSystem optional location x x/- verbatimDepth optional location x x/- verbatimElevation optional location x x/- verbatimLatitude optional location x x/- verbatimLocality optional location x x/- verbatimLongitude optional location x x/- verbatimSRS optional location x x/- waterBody recommended location x x/- materialSampleID recommended materialSample x/x measurementAccuracy recommended measurementOrFact x measurementDeterminedBy optional measurementOrFact x measurementDeterminedDate optional measurementOrFact x measurementID recommended measurementOrFact x measurementMethod recommended measurementOrFact x measurementRemarks recommended measurementOrFact x measurementType strongly recommended measurementOrFact x measurementTypeID strongly recommended measurementOrFact x measurementUnit strongly recommended measurementOrFact x measurementUnitID strongly recommended measurementOrFact x measurementValue strongly recommended measurementOrFact x measurementValueID strongly recommended measurementOrFact x associatedMedia recommended occurrence x/x associatedReferences optional occurrence x/x associatedSequences recommended occurrence x/x associatedTaxa optional occurrence x/x behavior optional occurrence x/x x catalogNumber recommended occurrence x/x disposition optional occurrence x/x establishmentMeans optional occurrence x/x georeferenceVerificationStatus recommended occurrence x x/- organismQuantity (preferred over individualCount) strongly recommended occurrence x/x x organismQuantityType (preferred over individualCount) strongly recommended occurrence x/x x individualCount recommended occurrence x/x x lifeStage optional occurrence x/x x occurrenceRemarks recommended occurrence x/x otherCatalogNumbers optional occurrence x/x preparations optional occurrence x/x recordedBy recommended occurrence x/x recordedByID recommended occurrence x/x recordNumber recommended occurrence x/x reproductiveCondition optional occurrence x/x x sex optional occurrence x/x x associatedOccurrences optional organsim x/x associatedOrganisms optional organsim x/x organismID recommended organsim x/x organismName optional organsim x/x organismRemarks optional organsim x/x organismScope optional organsim x/x previousIdentifications recommended organsim x/x accessRights recommended record x x/- bibliographicCitation recommended record x x/- collectionCode optional record x x/x collectionID optional record x x/x dataGeneralizations optional record x x/x datasetID recommended record x x/x datasetName recommended record x x/x dynamicProperties recommended record x x/x informationWithheld optional record x x/x institutionCode optional record x x/x institutionID optional record x x/x language recommended record x x/x license strongly recommended record x x/x modified recommended record x x/x ownerInstitutionCode optional record x x/x references recommended record x x/x rightsHolder recommended record x x/x type strongly recommended record x x/x x acceptedNameUsage recommended taxon x/x acceptedNameUsageID recommended taxon x/x higherClassification recommended taxon x/x infraspecificEpithet recommended taxon x/x nameAccordingToID recommended taxon x/x namePublishedInID optional taxon x/x namePublishedInYear optional taxon x/x nomenclaturalCode optional taxon x/x nomenclaturalStatus optional taxon x/x verbatimIdentification recommended identification x/x originalNameUsage recommended taxon x/x originalNameUsageID recommended taxon x/x parentNameUsage recommended taxon x/x parentNameUsageID recommended taxon x/x phylum recommended taxon x/x scientificNameAuthorship recommended taxon x/x specificEpithet recommended taxon x/x subgenus recommended taxon x/x taxonConceptID optional taxon x/x taxonID optional taxon x/x taxonomicStatus optional taxon x/x taxonRank strongly recommended taxon x/x taxonRemarks recommended taxon x/x verbatimTaxonRank recommended taxon x/x vernacularName recommended taxon x/x class recommended taxon x/x family recommended taxon x/x genus strongly recommended taxon x/x kingdom strongly recommended taxon x/x order strongly recommended taxon x/x "],["name_matching.html", "5.2 Name matching for taxonomic quality control", " 5.2 Name matching for taxonomic quality control OBIS requires all your specimens to be classified and matched against an authoritative taxonomic register. This effectively attaches unique stable identifiers (and digitally traceable) to each of your species. Meaning, if a taxonomic ranking or a species name changes in the future, there will be no question as to which species your dataset is actually referring to. Matching to registers also helps to avoid misspelled or unused terms. OBIS currently accepts identifiers from three authoritative lists: World Register Marine Species (WoRMS) LSIDs Integrated Taxonomic Information System (ITIS) TSNs Barcode of Life Data Systems (BOLD) and NCBI identifiers The identifiers (LSID, TSN, ID) from these registers will be used to populate the scientificNameID field. OBIS can accept other LSIDS besides WoRMS, as long as they are mapped in WoRMS. If you would like to include multiple identifiers, please use a concatenated list where each register is clearly identified (e.g. urn:lsid:itis.gov:itis_tsn:12345, NCBI:12345, BOLD:12345). You should prioritize using LSIDs because they are unique identifiers that indicate the authority the ID comes from. WoRMS LSIDs are also the taxonomic backbone that OBIS relies on, as it is built on marine systems and is linked to the other taxonomic authoritative lists. You can also use the Interim Register of Marine and Nonmarine Genera (IRMNG) to distinguish marine genera from freshwater genera. Taxon Matching Workflow The OBIS node managers have agreed to match all the scientific names in their datasets according to the following Name Matching workflow: Workflow for matching a list of taxon names to WoRMS 5.2.0.1 Step 1: Match with WoRMS The procedure for matching to WoRMS and then attaching successful matches back to your data can be simplified to: Prepare a file (.csv, .txt, .xlsx, etc.) with the list of your specimens/taxa Upload the file to WoRMS taxon match tool Check relevant boxes Review returned file Resolve any ambiguous matches Download file and identify data to include in your Occurrence data table for OBIS LSIDs, taxonomic fields, etc. Attach LSIDs back to your data using e.g.: R (merge) Excel (vlookup) The taxon match tool of the World Register of Marine Species (WoRMS) is an automatic way to download the taxonomic information about your occurrence records, without having to look for each name in the site. It is available at http://www.marinespecies.org/aphia.php?p=match. The WoRMS taxon match will compare your taxon list to the taxa available in WoRMS. The following video demonstrates the basic steps for using the WoRMS Taxon match. The taxon match takes into account exact matches and fuzzy matches. Fuzzy matches include possible spelling variations of a name available in WoRMS. WoRMS also identifies ambiguous matches, indicating that several potential matching options are available (e.g. homonyms). You can check these ambiguous matches and select the correct one, based on e.g., the general group information (a sponge dataset) or the authority. If this would be impossible with the available information (e.g., missing authority or very diverse dataset), then you need to contact the data provider for clarification. Watch the video below for a demonstration on how to resolve ambiguous or fuzzy matches. For performance reasons, the limit is set to 1,500 rows for the taxon match tool. Larger files can be sent to info@marinespecies.org and will be returned as quickly as possible. In case you have recorded a taxon that is not registered in WoRMS (e.g., newly discovered species), you should contact them so the database can be updated. After matching, the tool will return you a file with the AphiaIDs, LSIDs, valid names, authorities, classification, and any other output you have selected. The WoRMS LSID is used for DwC:scientificNameID. A complete online manual is available at http://www.marinespecies.org/tutorial/taxonmatch.php. You can attach IDs obtained from WoRMS back to your own data using Excel’s vlookup function. R script to do this is shown below. R script for attaching Taxon Lists to ID Lists: If you are familiar enough with R, you can use the merge function to attach the two lists to your data. We provide a short example of how to use this function below. #Generate example data table with species occurences, for this example we will only have one column with the scientificName data<-data.frame(scientificName=c("Thunnus thynnus", "Rhincodon typus", "Luidia maculata","Ginglymostoma cirratum")) # this would be your matched file from WoRMS, but for example we are generating a simple list with the species names and LSIDs lsids<- data.frame(scientificName=c("Ginglymostoma cirratum","Luidia maculata","Thunnus thynnus", "Rhincodon typus"), LSID = c("urn:lsid:marinespecies.org:taxname:105846", "urn:lsid:marinespecies.org:taxname:213112","urn:lsid:marinespecies.org:taxname:127029","urn:lsid:marinespecies.org:taxname:105847")) #merge data frames together matched_data<-merge(data, lsids, by = "scientificName") matched_data 5.2.0.2 How to fetch a full classification for a list of species from WoRMS? When setting up your WoRMS taxon match, to obtain the full classification for your list of species, simply check the box labeled “Classification”. This will add classification output in addition to the requested identifiers to your taxon match file, including Kingdom, Phylum, Class, Order, Family, Genus, Subgenus, Species, and Subspecies. WoRMS classification box 5.2.0.3 What to do with non-matching names? If your scientificName does not find an exact match to the WoRMS database, you may get an ambiguous match. According to WoRMS guidelines, ambiguous matches can be marked as one of the following: phonetic near_1 near_2 near_3 match_quarantine match_deleted See https://www.marinespecies.org/tutorial_taxonmatch.php for definitions of each of these terms. In each of these cases, WoRMS will try to suggest a species to match your uncertain taxon. Take care to ensure the correct species name is selected given the information in the original documentation. This is especially true for near_2 or near_3 matches. When checking a potential matched name, we recommend referencing the authority and higher taxonomic levels of a given suggestion when possible. For example, if you know the ambiguous species is a sponge, but one of the suggestions is for a mammal, you know that is not the correct name. Sometimes the species name as is it was originally documented is a name that is not accepted in WoRMS (i.e. Status = unaccepted). In these cases scientificNameID should reflect the name in the scientificName field, even if this name is not accepted. If you or the data provider can confirm with certainty that the name in scientificName can be changed to an accepted name, then verbatimIdentification must be populated with the scientific name as it was originally documented. In general, it is good practice to populate verbatimIdentification with the original taxon identification. Example of choices from an abiguous match In cases where no match can be found, WoRMS will indicate none. For these cases you should follow these steps: Ensure the name was entered correctly and any other information (e.g., authority, year, identification qualifiers) are included in separate columns, not the same cell as the name, as this can sometimes prevent matching Match with LifeWatch or another register (see Step 2 below) Check that the species is marine If a scientific name does not appear in any register, you should contact the original data provider, where possible, to confirm taxonomic spelling, authority, and obtain any original description documents, then attempt to match again. If even after this there are no matches, please contact the WoRMS data management team at info@marinespecies.org to see if the taxon should be added to the WoRMS register. 5.2.0.4 Step 2: Check name in other registers If you do not find a match with WoRMS, you should next check other registers. The LifeWatch taxon match compares your taxon list to multiple taxonomic standards. Matching with multiple registers gives an indication of the correct spelling of a name, regardless of its environment. If a name would not appear in any of the registers, this could indicate a mistake in the scientific name and the name should go back to the provider for additional checking/verification. Contrary to the WoRMS taxon match, when several matching options are available, the LifeWatch taxon match only mentions “no exact match found, multiple possibilities” instead of listing the available options. If multiple options are available, these should be looked up and matched manually. Currently, this web service matches the scientific names with the following taxonomic registers: World Register of Marine Species – WoRMS Catalogue of Life – CoL Integrated Taxonomic Information System – ITIS Pan-European Species-directories Infrastructure – PESI Index Fungorum – IF International Plant Names Index – IPNI Global Names Index - GNI Paleobiology Database - PaleoDB 5.2.0.5 Step 3: Is taxon marine? The Interim Register of Marine and Non-marine Genera (IRMNG) matching services are available through http://www.irmng.org/, as well as through the LifeWatch taxon match. This service allows you to search for a genus (or other taxonomic rank when you uncheck the “genera” box) to check if it is known to be marine, brackish, freshwater, or terrestrial. You can find this information in the row labeled “Environment”. If the taxa is marine, you may have to contact the WoRMS data management team (info@marinespecies.org) to have the taxon added to the WoRMS register (note you may have to provide supporting information confirming taxonomic and marine status). R packages for taxon matching If you are familiar with R, you may use the obistools function match_taxa to conduct taxon matching for your dataset. There is also a WoRMS package called worrms that has a function called wm_records_taxamatch you can use to conduct taxon matching. The output will be the same as that from the WoRMS tool, so you should check ambiguous matches as described above, confirming with other registers as necessary. Taxon Match Tools Overview See the table below for a summary of the different tools available. Tool Advantage Disadvantage WoRMS taxon match Accessible online, Does not require coding knowledge Requires rematch information back to your data obistools::match_taxa Produces same output as WoRMS taxon match, Already in R so easier to merge back with data Requires knowledge of R or python worrms::wm_records_taxamatch Outputs all WoRMS matching information Outputs a tibble for each taxa name specified; Requires knowledge of R or python "],["format_occurrence.html", "5.3 How to format Occurrence tables", " 5.3 How to format Occurrence tables If your dataset structure is based on Occurrence core, or has an Occurrence extension (remember that all OBIS data have at least one occurrence record associated, regardless of what organization structure you have chosen), there are several terms that are required in your dataset by OBIS. These required data fields include the following eight terms: occurrenceID eventID (required for Occurrence extension, not required for Occurrence Core) occurrenceStatus basisOfRecord scientificName scientificNameID (strongly recommended) eventDate (not required for Occurrence extension, required for Occurrence Core) decimalLatitude (not required for Occurrence extension) decimalLongitude (not required for Occurrence extension) While these are the bare minimum, you should strongly consider adding other terms if you have the corresponding information/data in your dataset or documentation. Other terms you should consider adding are identified by their associated Darwin Core class below. See the term checklist for a more complete list of potential terms for Occurrence table. Class Occurrence| DwC: associatedMedia Class Occurrence| DwC: associatedReferences Class Occurrence| DwC: associatedSequences Class Occurrence| DwC: associatedTaxa Class Occurrence| DwC: preparations Class Occurrence| DwC: recordedBy Class Occurrence| DwC: materialSample Class Occurrence| DwC: materialSampleID Class Record | DwC: bibliographicCitation Class Record | DwC: catalogNumber Class Record | DwC: collectionCode Class Record | DwC: collectionID Class Record | DwC: dataGeneralizations Class Record | DwC: datasetName Class Record | DwC: institutionCode Class Record | DwC: modified Class Taxon | DwC: kingdom Class Taxon | DwC: scientificNameAuthorship Class Taxon | DwC: taxonRank Class Taxon | DwC: taxonRemarks Note that any terms related to measurements, either biotic (e.g., sex, lifestage, biomass) or abiotic will also be included in the extendedMeasurementOrFact table. Measurements can remain in the Occurrence table as long as they can be mapped to the appropriate DwC term (e.g. DwC:Occurrence:sex). Nnot every data aggregator outside of OBIS indexes the eMoF table, so otherwise this information may be lost. 5.3.0.1 Stepwise Guidance to Format an Occurrence Table (with spreadsheets) Before proceeding with formatting the Occurrence table, be sure you have completed taxon matching to obtain WoRMS LSIDs for the scientificNameID field. Identify columns in your raw data that match with Occurrence fields Include columns with measurements for now, but they will be moved to an eMoF table(s) Copy these columns to a new sheet named Occurrence (note it is good practice to never make changes to your original datasheet) Create and add occurrenceIDs for each unique occurrence record Add and fill basisOfRecord and occurrenceStatus fields Ensure your column names map to Darwin Core terms scientificName + scientificNameID Watch our video tutorial for a demonstration of this procedure: After formatting your Occurrence Core or Extension table, you can format your extendedMeasurementOrFact table. "],["format_event.html", "5.4 How to format Event tables", " 5.4 How to format Event tables If your dataset uses an Event Core structure, data fields included in your dataset should include the following required terms: eventDate eventID parentEventID (if applicable) decimalLatitude decimalLongitude Other terms you should consider adding are grouped by their associated Darwin Core class in the table below. See the term checklist for a more complete list of DwC terms for the Event table. Class Event | DwC:parentEventID Class Event | DwC:eventType Class Event | DwC:eventRemarks Class Event | DwC:year Class Event | DwC:month Class Event | DwC:day Class Location | DwC:country Class Location | DwC:island Class Location | DwC:coordinateUncertaintyInMeters Class Location | DwC:countryCode Class Location | DwC:footprintWKT Class Location | DwC:geodeticDatum Class Location | DwC:islandGroup Class Location | DwC:locality Class Location | DwC:locationAccordingTo Class Location | DwC:locationID Class Location | DwC:locationRemarks Class Location | DwC:maximumDepthInMeters Class Location | DwC:minimumDepthInMeters Class Location | DwC:stateProvince Class Location | DwC:verbatimCoordinates Class Location | DwC:verbatimDepth Class Location | DwC:waterBody Terms related to measurements, either biotic (e.g., sex, lifestage) or abiotic will be included in extendedMeasurementOrFact table not the Event Core or Occurrence extension table. 5.4.0.1 Stepwise Guidance to Format Event Table (with spreadsheets) Before proceeding with the below, make sure each record already has an eventID. Add and fill the parentEventID, eventType, and eventRemarks fields as applicable Identify the hierarchical event structure in your data, if present, and create new records for parent events, filling in any relevant fields Identify all columns in your data that will match with Darwin Core Event fields Include any relevant abiotic measurements (ENV-DATA) related to sampling events (e.g. sampling protocols). We will add these to the eMoF table later Copy these columns to a new sheet and name it Event Delete duplicate data so only unique events are left Ensure dates and time are formatted according to ISO 8601 standards in the eventDate field Add any other relevant fields as indicated above Map fields to Darwin Core Watch the video tutorial of this process below. After completing the formatting of your Event Core table, you can next format your Occurrence and extendedMeasurementOrFact tables. To format the Occurrence extension table, see the Occurrence table section of this manual. 5.4.1 Populating parent and child events In OBIS, child events will inherit information from the parent event when the field is left empty. For example, if you provided the information “1x1m quadrat intertidal sampling” to samplingProtocol for a parent event but nothing was provided to the child event, as below: eventID parentEventID samplingProtocol site123 1x1m quadrat intertidal sampling site123_quad1 site123 the child event will automatically be populated with the same text “1x1m quadrat intertidal sampling” once published to OBIS. eventID parentEventID samplingProtocol site123 1x1m quadrat intertidal sampling site123_quad1 site123 1x1m quadrat intertidal sampling This can be convenient so that you do not have to fill in the same information repeatedly. However, it is important to understand that GBIF does not currently implement inheritance! In OBIS, child events inherit information from parent events as described above. However, if the parentEvent contains data such as latitude/longitude coordinates, and child events do not, occurrences associated with the child events will have blank latitude/longitude coordinates when data is accessed from GBIF. This is again because child event fields are not currently inherited from parent events in GBIF. If you plan for your dataset to be additionally accessed through GBIF, it is recommended to populate information in both parent and child events. A discussion of this and the implications can be found here. Therefore, in cases where the data will be accessible by both OBIS and GBIF, we recommend populating important information (e.g. decimalLongitude, decimalLatitude) in the child events to ensure the data will be useful for both user bases. "],["format_emof.html", "5.5 How to format extendedMeasurementOrFact tables", " 5.5 How to format extendedMeasurementOrFact tables 5.5.0.1 What data goes into eMoF Any data related to abiotic or biotic measurements, including sampling information and protocols can be included in the eMoF table. Measurement data can also go into the MeasurementOrFact extension, however OBIS recommends using the extendedMeasurementOrFact instead, particularly if your data is based on an Event core table. Required terms for eMoF include: eventID (this links the record to the Event Core table) occurrenceID (this links the record to the Occurrence Core or Occurrence Extension table) Other potential fields are shown in the table below (also listed in the checklist): Class Event | DwC:habitat Class Event | DwC:sampleSizeUnit Class Event | DwC:sampleSizeValue Class Event | DwC:samplingEffort Class Event | DwC:samplingProtocol Class Occurrence | DwC:behavior Class Occurrence | DwC:individualCount Class Occurrence | DwC:lifeStage Class Occurrence | DwC:organismQuantity Class Occurrence | DwC:organismQuantityType Class Occurrence | DwC:sex Class Occurrence | DwC:type Class Measurement | DwC:measurementAccuracy Class Measurement | DwC:measurementDeterminedBy Class Measurement | DwC:measurementDeterminedDate Class Measurement | DwC:measurementID Class Measurement | DwC:measurementMethod Class Measurement | DwC:measurementRemarks Class Measurement | DwC:measurementType* Class Measurement | DwC:measurementTypeID* Class Measurement | DwC:measurementUnit* Class Measurement | DwC:measurementUnitID* Class Measurement | DwC:measurementValue Class Measurement | DwC:measurementValueID *For measurementTypeID, measurementUnitID, and measurementValueID you must use controlled vocabulary terms. We know choosing the correct vocabulary term can be challenging, so we have provided some guidance on how to select the correct vocabulary and are continuously working to improve guidelines. It is strongly recommended to ensure these fields are filled as correctly as possible. Missing or incorrect terms will be documented in the measurementOrFact reports. 5.5.0.2 How to structure eMoF Structuring data for the eMoF extension may be one of the more confusing extensions in the data formatting process. It may help to think of this extension as the table that contains all information related to any kind of measurement. Rather than documenting each of your measurements in separate columns (e.g., columns for biomass, abundance, length, gear size, percent cover, etc.), these measurements will be condensed into one column: measurementValue. measurementType describes what the measurement actually is, for example whether it is an abundance value, length, percent cover, or any other biotic/abiotic measurement. measurementUnit is used to indicate the unit of the measurement. By linking measurementType and measurementValue with the identifiers eventID and/or occurrenceID, you can have measurements linked to one event (e.g. temperature), measurements link to occurrence records (e.g. length), as well as sampling facts that are linked to events (size, gear, etc.). Information specifically related to how samples were taken will have the measurementTypes: sampleSizeValue, sampleSizeUnit, samplingEffort, and samplingProtocol. 5.5.0.3 Stepwise Guidance to Format eMoF Table (in Excel) Create a blank sheet and name it eMoF Add 9 column headers for: eventID, occurrenceID, measurementType, measurementValue, measurementUnit, measurementTypeID, measurementValueID, measurementUnitID, measurementRemarks Copy eventID values from your Occurrence table and paste into the eventID field in your new, blank eMoF table Repeat for occurrenceID from the Occurrence table Copy the first column of measurement values, paste into the measurementValue field Fill measurementType with the name of the variable (e.g., count, length, etc.) Add unit of measurements where applicable to the measurementUnit field For any other measurements related to occurrences, repeat steps 3-5, pasting additional measurements below the preceding ones Be sure to copy and paste the associated occurrenceIDs and/or eventIDs for the additional measurements Fill the fields measurementTypeID, measurementUnitID, and measurementValueID with controlled vocabularies that suit your data (see vocabulary guidelines) Repeat steps 3-7 for any measurements in the Event table Note the fields sampleSizeValue, samplingEffort, and samplingProtocol from the Occurrence table can be documented as separate measurements on different rows in the eMoF table. E.g., measurementType = samplingProtocol, measurementValue = description of protocol. Any values in sampleSizeUnit fields should be placed in the measurementUnit field when transferred to the eMoF. Watch the video tutorial for how to format the eMoF table below. If you would like to export Event data to the eMoF, see some example R code below. This example was provided by Abby Benson from the OBIS-USA node. library(dplyr) cruise <- unique(eventCore[c("eventID")]) #create a list of all unique eventIDs from your event table cruise <- cruise %>% #add sampling information mutate(measurementType = "Sampling platform name", measurementValue = "R/V Cruise Id = SR1812", measurementValueID = "https://doi.org/10.7284/908021", measurementUnit = "", measurementTypeID = "http://vocab.nerc.ac.uk/collection/Q01/current/Q0100001/", measurementUnitID = "", occurrenceID = "") "],["dna_data.html", "5.6 DNA derived data", " 5.6 DNA derived data Note: OBIS guidelines have not yet been updated to reflect changes that allow DNA Derived data to be linked to Event core tables. We will update this page soon. Contents: Introduction How to find genetic data in OBIS Guidelines for compiling eDNA and metabarcoding data eDNA & DNA Derived use cases 16S rRNA metabarcoding example Unknown sequences Guidelines for compiling qPCR data Introduction to DNA data DNA derived data are increasingly being used to document taxon occurrences. This genetic data may come from a sampling event, an individual organism, may be linked to physical material (or not), or may result from DNA detection methods e.g., metabarcoding or qPCR. Thus genetic data may reflect a single organism, or may include information from bulk samples with many individuals. Still, DNA-derived occurrence data of species should be documented as standardized and as reproducible as possible. To ensure DNA data are useful to the broadest possible community, a community guide entitled Publishing DNA-derived data through biodiversity data platforms was published by GBIF, OBIS, and others. This guide is supported by the DNA derived data extension for Darwin Core, which incorporates MIxS terms into the Darwin Core standard. There are 5 categories for which genetic data could fall into: DNA-derived occurrences Enriched occurrences Targeted species detection Name references Metadata only For a guide and decision tree on determining which category your data falls into, see the Data packaging and mapping section of the GBIF guide. Refer to the examples below for use case examples of eDNA and DNA derived data (Category 1). Currently, genetic data must be published with Occurrence core, not Event core. eDNA and DNA derived data are then linked to the Occurrence core data table with the use of occurrenceID and/or eventID. See below for further guidance on compiling genetic data. A new data model is being developed by GBIF and the OBIS community that may change this, however as it is not implemented yet, we focus on the current Darwin Core recommendations here. To format datasets, you will need to have information on the sequence and possible taxonomy for each occurrence record associated with a DNA sample. Genetic data is often recorded in multiple different files, and this might be the type of format received from data providers. Important data tables can include: an OTU-table, a taxonomy table, a sample information table, and a .fasta file with sequences. The OTU-table is a sequence by sample table, which records the quantity of each unique sequence found in each sample. Sequences are usually referred to by an ID, which is unique only in the dataset (e.g. asv1, asv2, asv3 …). The taxonomy table is a sequence by taxonomy table, which records the taxonomy linked to each unique sequence, as defined by the annotation method. The sample information table records the metadata of each sample (e.g. location, time, and collection method). Finally the .fasta file records the actual DNA sequence that is linked to each sequence id. Although this data is in multiple files, each unique sequence by sample combination is considered one occurrence. Therefore the data from these tables will need to be formatted to the “long format”, including a row for each sequence in each sample. See the figure below for a demonstration of how this can be done. Combining multiple DNA data tables into one “long format” table Doing this will help you when following the guidelines below. How to find genetic data in OBIS To find genetic data in OBIS we recommend using the R package robis, using the occurrence function. You must set extensions and/or hasextensions to “DNADerivedData” to ensure extension records are included in the results. hasextensions will exclude any occurrence that does not have the specified extension, in our case, DNADerivedData. The extensions parameter specifies which extensions to include. To obtain the DNA data, you have to extract the information from the extension using the unnest_extension() function. You can specify as many fields from the Occurrence table to be included, and pass them to the fields parameter. See the code below for an example. See also this vignette for a more detailed example, including how you can work further with these sequences in R. dna_occ<-occurrence("Dinophyceae",hasextensions="DNADerivedData", extensions="DNADerivedData") dnaseqs <-unnest_extension(dna_datasets, "DNADerivedData", fields = c("id", "phylum", "class", "family", "genus", "species")) dnaseqs["DNA_sequence"] # A tibble: 706 × 1 DNA_sequence <chr> 1 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 2 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 3 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG… 4 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 5 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 6 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG… 7 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 8 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… 9 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG… 10 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC… # … with 696 more rows Additionally, a prototype Sequence Search tool is in development that allows you to search for sequences or related sequences in OBIS. Note that the tool is not always up to date so please use caution until the prototype is fully developed. To use the tool: Copy your sequence in the provided box (an example sequence is provided for testing) Press the Search button View results below You can also change the Minimum Alignment Score slider in the map view to see location of sequences The search result will show you taxonomic information for species sequences that align to your sequence, the alignment score, and a link to the respective datasets. OBIS Mapper You can use the OBIS Mapper to obtain records that include the DNADerivedData extension by addding a filter for the extension when using the tool. To do this: From the OBIS Mapper, navigate to the Criteria tab (the plus (+) sign) Open the Extensions dropdown section Check the box for DNADerivedData Add any other filters you want, e.g. taxonomic, then click save to create the layer Switch to the Layers tab Download the data from the layer by clicking the green button (see Data Access for more on using the OBIS Mapper) 5.6.1 Compiling eDNA and metabarcoding datasets As mentioned above, you will need to have information on the taxonomy and sequences for each occurrence record associated with a DNA sample. You should first fill in the Occurrence core table, and then complete the DNA Derived Data extension (as well as the eMoF extension, if applicable, for any measurements taken). Occurrence core table In addition to the usual required terms for Occurrence datasets, you should consider the following additional terms: Class Occurrence | DwC: organismQuantity Class Occurrence | DwC: OrganismQuantityType Class Occurrence| DwC: associatedSequences Class Event | DwC: sampleSizeValue Class Event | DwC: sampleSizeUnit Class Event | DwC: samplingProtocol Class Identification | DwC: identificationRemarks Class Identification | DwC: identificationReferences Class Identification | DwC: verbatimIdentification Class Taxon | DwC: taxonConceptID Class Material Sample | DwC:materialSampleID For organismQuantity and sampleSizeValue in eDNA datasets, the quantities recorded with sequencing studies always represent relative abundance to the total reads in the sample, and cannot be directly compared across samples. This is due to the nature of the sample processing protocol and the amplification of DNA with PCR, which biases the original quantities. In organismQuantity, record the amount of a unique sequence in a specific sample (i.e. 33 reads). In sampleSizeValue, record the total number of all reads in that specific sample (i.e. 15310 reads). This information will allow people accessing the data to calculate the relative abundance of that sequence in the sample. The fields organismQuantityType, and sampleSizeUnit, should be populated with “DNA sequence reads”, as it is of high importance that sequence abundances are not confused with organism abundances recorded by traditional methods. The abundance information can usually be found in the “OTU-table”. associatedSequences should contain a reference to the URL domain where genetic sequence information associated with the Occurrence can be found, e.g. a link, identifier, or list (concatenated and separated) of identifiers. Can link to archived raw barcode reads and/or associated genome sequences, like a public repository. It is recommended that links contain the domain name (e.g. NCBI) in the URL, for example: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA887898/. The actual sequence of the occurrence will be documented in the DNA Derived Data extension. identificationRemarks should be used to record information on how the taxonomic information of the occurrence was reached against which reference database, and, if possible, with which confidence. For example “RDP annotation confidence: 0.96, against reference database: GTDB”. This information should be recorded in the bioinformatic protocol of the study. Note: this information will also be recorded in the DNA derived extension in the fields otu_seq_comp_appr and otu_db. identificationReferences should include a link to the bioinformatic pipeline or publication where the identification process is explained in detail. taxonConceptID should include the taxonomic ID of the sequence (non-Linnean). Often genetic sequences can be assigned an ID linked to a reference database that is not a linnean name. These taxonomic names or IDs from other taxonomic databases (like the NCBI taxonomic database) can be recorded in this field. For example: NCBI:txid9771. The name linked to this ID can then be recorded in the field verbatimIdentification. samplingProtocol can contain free-text that briefly describes the methods used to obtain the sample, or a link to a protocol that is recorded elsewhere. DNA Derived Data extension The DNADerivedData extension is meant to capture information related to the sampled DNA, including sampling, processing, and other bioinformatic methods. The following (free-text) terms are required or highly recommended for eDNA and metabarcoding datasets. Note that some terms will be different for qPCR data (see below) DNA Derived | DwC: DNA_sequence DNA Derived | DwC: sop DNA Derived | DwC: target_gene DNA Derived | DwC: target_subfragment DNA Derived | DwC: pcr_primer_forward DNA Derived | DwC: pcr_primer_reverse DNA Derived | DwC: pcr_primer_name_forward DNA Derived | DwC: pcr_primer_name_reverse DNA Derived | DwC: pcr_primer_reference DNA Derived | DwC: Pcr_cond DNA Derived | DwC: annealingTemp DNA Derived | DwC: annealinTempUnit DNA Derived | DwC: ampliconSize DNA Derived | DwC: env_broad_scale DNA Derived | DwC: env_local_scale DNA Derived | DwC: env_medium DNA Derived | DwC: lib_layout DNA Derived | DwC: seq_meth DNA Derived | DwC: otu_class_appr DNA Derived | DwC: otu_seq_comp_appr DNA Derived | DwC: otu_db For a complete list of terms you can map to, see the DwC DNA Derived Data extension page. See the examples below for use case examples. The Marine Biological Data Mobilization Workshop also has a tutorial for this type of data. DNA_sequence is the most important field, where the ASV/OTU sequence will be recorded. This field can then be searched with sequence alignment methods to, for example, find closely related sequences recorded in other studies, and will allow very powerful data comparison and analysis in the future. It will also make your sequence available in the OBIS sequence search tool. The remaining metadata fields will help the person accessing the data to filter data of interest (e.g. specific genetic region with target_gene, target_subfragment, or pcr_primer fields), link to the public sequence databases with the MixS specific fields (e.g. env_ fields), and evaluate the reliability of the sequence annotation method (e.g. otu_ fields). Environmental systems are described in the two fields env_broad_scale and env_local_scale and it is recommended to use Environment Ontology (ENVO)’s biome classes to describe the environmental system from which the sample was extracted. Like other identifiers, provide the exact ENVO reference identifier. env_broad_scale provides a coarse resolution for which environment your sample came from. Likely this will be marine biome (ENVO:00000447) for OBIS data. For local scale, identify the specific environment your sample was obtained from (e.g., coastal water, benthic zone, etc.). When data tables are formatted and you are ready to publish it on the IPT, it will follow the same process for publishing on an IPT. You will upload your source files, and add the Occurrence core Darwin Core mappings, and then the DNA Derived Data Darwin Core mappings. However the extension must first be installed by the IPT administrator (often the node manager). Once the extension is installed, you can add the Darwin Core DNA Derived Data mapping for that file. 5.6.1.1 eDNA and DNA derived data example The following example use cases draw on both the DNA-derived data guide and the DNA derived data extension to illustrate how to incorporate a DNA derived data extension file into a Darwin Core archive. Note: for the purposes of this section, only required Occurrence core terms are shown, in addition to all eDNA & DNA specific terms. For additional Occurrence core terms, refer to Occurrence. 5.6.1.1.1 eDNA data from Monterey Bay, California The data for this example is from the use case “18S Monterey Bay Time Series: an eDNA data set from Monterey Bay, California, including years 2006, 2013 - 2016’. The data from this study originate from marine filtered seawater samples that have undergone metabarcoding of the 18S V9 region. Occurrence core: We can populate the Occurrence core with all the required and highly recommended fields, as well as considering the eDNA and DNA specific fields. The Occurrence core contain the taxonomic identification of each ASV observed; its number of reads, as well as relevant metadata including the sample collection location, references for the identification procedure, and links to archived sequences. OccurrenceID and basisOfRecord are some of the required Occurrence core terms, in addition to the highly recommended fields of organismQuantity and organismQuantityType. A selection of samples from this plate were included in another publication (Djurhuus et al., 2020),which is recorded in identificationReferences along with the GitHub repository where the data can be found. occurrenceID basisOfRecord organismQuantity OrganismQuantityType associatedSequences 11216c01_12_edna_1_S_occ1 MaterialSample 19312 DNA sequence reads NCBI BioProject acc. nr. PRJNA433203 11216c01_12_edna_2_S_occ1 MaterialSample 16491 DNA sequence reads NCBI BioProject acc. nr. PRJNA433203 11216c01_12_edna_3_S_occ1 MaterialSample 21670 DNA sequence reads NCBI BioProject acc. nr. PRJNA433203 sampleSizeValue sampleSizeUnit identificationReferences identificationRemarks 147220 DNA sequence reads GitHub repository Djurhuus et al. 2020 unassigned, Genbank nr Release 221 September 20 2017 121419 DNA sequence reads GitHub repository Djurhuus et al. 2020 unassigned, Genbank nr Release 221 September 20 2017 161525 DNA sequence reads GitHub repository Djurhuus et al. 2020 unassigned, Genbank nr Release 221 September 20 2017 DNA Derived Data extension: Next, we can create the DNA Derived Data extension which will be connected to the Occurrence core with the use of occurrenceID. This extension contains the DNA sequences and relevant DNA metadata, including sequencing procedures, primers used and SOP’s. The recommended use of ENVO’s biome classes were applied to describe the environmental system from which the sample was extracted. The samples were collected by CTD rosette and filtered by a peristaltic pump system. Illumina MiSeq metabarcoding was applied for the target_gene 18S and the target_subfragment, V9 region. URL’s are provided for the protocols followed for nucleic acids extraction and amplification. For a detailed description of the steps taken to process the data, including algorithms used, see the original publication. Adding Operational Taxonomic Unit (OTU) related data are highly recommended and should be as complete as possible, for example: occurrenceID env-broad_scale env_local_scale env_medium 11216c01_12_edna_1_S_occ1 marine biome (ENVO:00000447) coastal water (ENVO:00001250) waterborne particulate matter (ENVO:01000436) 11216c01_12_edna_2_S_occ1 marine biome (ENVO:00000447) coastal water (ENVO:00001250) waterborne particulate matter (ENVO:01000436) 11216c01_12_edna_3_S_occ1 marine biome (ENVO:00000447) coastal water (ENVO:00001250) waterborne particulate matter (ENVO:01000436) samp_vol_we_dna_ext nucl_acid_ext nucl_acid_amp lib_layout target_gene 1000ml dx.doi.org/10.17504/protocols.io.xjufknw dx.doi.org/10.17504/protocols.io.n2vdge6 paired 18S 1000ml dx.doi.org/10.17504/protocols.io.xjufknw dx.doi.org/10.17504/protocols.io.n2vdge6 paired 18S 1000ml dx.doi.org/10.17504/protocols.io.xjufknw dx.doi.org/10.17504/protocols.io.n2vdge6 paired 18S target_subfragment seq_meth otu_class_appr otu_seq_comp_appr V9 Illumina MiSeq 2x250 dada2;1.14.0;ASV blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2% V9 Illumina MiSeq 2x250 dada2;1.14.0;ASV blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2% V9 Illumina MiSeq 2x250 dada2;1.14.0;ASV blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2% otu_db sop DNA_sequence Genbank nr;221 dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository GCTACTACCGATT… Genbank nr;221 dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository GCTACTACCGATT… Genbank nr;221 dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository GCTACTACCGATT… pcr_primer_forward pcr_primer_reverse pcr_primer_name_forward pcr_primer_name_reverse pcr_primer_reference GTACACACCGCCCGTC TGATCCTTCTGCAGGTTCACCTAC 1391f EukBr Amaral-Zettler et al. 2009 GTACACACCGCCCGTC TGATCCTTCTGCAGGTTCACCTAC 1391f EukBr Amaral-Zettler et al. 2009 GTACACACCGCCCGTC TGATCCTTCTGCAGGTTCACCTAC 1391f EukBr Amaral-Zettler et al. 2009 5.6.1.1.2 16S rRNA gene metabarcoding data of Pico to Mesoplankton DNA derived datasets can also include an extendedMeasurementsOrFact (eMoF) extension file, in addition to the Occurrence and DNA derived extensions. In this example, environmental measurements were provided in an eMoF file, in addition to the DNA derived data and occurrence data. Here we show how to incorporate such measurements in the extensions. In the publication “Diversity of Pico- to Mesoplankton along the 2000 km Salinity Gradient of the Baltic Sea”, a dataset with 16S rRNA gene metabarcoding data of surface water microbial communities was created from 21 off-shore stations, following a transect from Kattegat to the Gulf of Bothnia in the Baltic Sea. The full dataset entitled “Diversity of Pico- to Mesoplankton along the 2000 km Salinity Gradient of the Baltic Sea (Hu et al. 2016) is available from GBIF. Occurrence core: The Occurrence core contain information about the organisms in the sample including the taxonomy and quantity of organisms detected, the collection location, references for the identification procedure, and links to the sequences generated. Important note: even though this dataset uses OTU identifiers for taxonomy (therefore not including scientificNameID) OBIS still recommends using scientificNameID. basisOfRecord occurrenceID eventID eventDate MaterialSample SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730 SBDI-ASV-3:16S_1 2013-07-13 07:08:00 MaterialSample SBDI-ASV-3:16S_1:43e088977eba5732bfa45e20b1d8cdd2 SBDI-ASV-3:16S_1 2013-07-13 07:08:00 MaterialSample SBDI-ASV-3:16S_1:887bc7033b46d960e893caceb711700b SBDI-ASV-3:16S_1 2013-07-13 07:08:00 organismQuantity organismQuantityType sampleSizeValue sampleSizeUnit 2235 DNA sequence reads 12393 DNA sequence reads 795 DNA sequence reads 12393 DNA sequence reads 40 DNA sequence reads 12393 DNA sequence reads samplingProtocol associatedSequences identificationReferences identificationRemarks 200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679] [https://www.ebi.ac.uk/ena/browser/view/ERR1202034] [https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation] DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.5 200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679] [https://www.ebi.ac.uk/ena/browser/view/ERR1202034] [https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation] DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.56 200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679] [https://www.ebi.ac.uk/ena/browser/view/ERR1202034] [https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation] DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.99 decimalLatitude decimalLongitude taxonID scientificName 55.185 13.791 ASV:919a2aa9d306e4cf3fa9ca02a2aa5730 UBA6821 55.185 13.791 ASV:43e088977eba5732bfa45e20b1d8cdd2 Chthoniobacterales 55.185 13.791 ASV:887bc7033b46d960e893caceb711700b BACL27 sp014190055 kingdom phylum class order family genus Bacteria Verrucomicrobiota Verrucomicrobiae Chthoniobacterales UBA6821 UBA6821 Bacteria Verrucomicrobiota Verrucomicrobiae Chthoniobacterales NA NA Bacteria Actinobacteriota Acidimicrobiia Acidimicrobiales Ilumatobacteraceae BACL27 DNA Derived Data extension: The DNA Derived Data extension for metabarcoding data contains the DNA sequences and relevant DNA metadata, primers and procedures. This example table contains the highly recommended and recommended fields as populated with the example dataset data. For this dataset, authors additionally provided measurements of of water sample temperature and salinity, which are provided in an extendedMeasurementOrFact extension file: id env_broad_scale env_local_scale env_medium SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730 aquatic biome [ENVO_00002030] marine biome [ENVO_00000447] brackish water [ENVO_00002019] SBDI-ASV-3:16S_1:43e088977eba5732bfa45e20b1d8cdd2 aquatic biome [ENVO_00002030] marine biome [ENVO_00000447] brackish water [ENVO_00002019] SBDI-ASV-3:16S_1:887bc7033b46d960e893caceb711700b aquatic biome [ENVO_00002030] marine biome [ENVO_00000447] brackish water [ENVO_00002019] lib_layout target_gene target_subfragment seq_meth sop paired 16S rRNA V3-V4 Illumina MiSeq https://nf-co.re/ampliseq paired 16S rRNA V3-V4 Illumina MiSeq https://nf-co.re/ampliseq paired 16S rRNA V3-V4 Illumina MiSeq https://nf-co.re/ampliseq pcr_primer_forward pcr_primer_reverse pcr_primer_name_forward pcr_primer_name_reverse DNA_sequence CCTACGGGNGGCWGCAG GACTACHVGGGTATCTAATCC 341 805R TCGAGAATTTTTCACAATG… CCTACGGGNGGCWGCAG GACTACHVGGGTATCTAATCC 341 805R TCGAGAATTTTTCACAATG… CCTACGGGNGGCWGCAG GACTACHVGGGTATCTAATCC 341 805R TGGGGAATCTTGCGCAATG… extendedMeasurementOrFact (eMoF) extension: measurementID occurrenceID measurementType measurementValue measurementUnit SBDI-ASV-3:16S_1:temperature SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730 temperature 16.9 °C SBDI-ASV-3:16S_1:salinity SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730 salinity 7.25 psu SBDI-ASV-3:16S_1:temperature SBDI-ASV-3:16S_1:1ead98754d34073a4606f7ff1e94126e temperature 16.9 °C 5.6.2 Unknown sequences It is important to understand the significance of unknown and uncharacterized sequences in genetic studies. Sequences are given taxonomic names based on comparisons to a reference database. The reference databases contain sequences that have been submitted with a name. Ideally, the reference database is a collection of sequences that are derived from vouchered, morphologically identified specimens. Notably, this is frequently often not the case and sequences can also have erratic annotations. Furthermore, only a small portion of species have sequences in reference databases. Due to this reason, typically many sequences in any given study will remain uncharacterized. This is especially the case for tropical regions with high biodiversity. By also recording all sequences, including uncharacterized sequences, we make sure that the information is not lost, even if the annotation is currently incorrect or missing. These uncharacterized sequences can then still be compared to other studies, and can be given a taxonomic name as more specimens are sequenced and added to the reference databases. For unknown sequences it is required to populate the scientificName field with “Incertae sedis”, or the lowest taxonomic information if available. For example, if it is only known which Class a sequence belongs to, populate scientificName with the associated Class name. Similarly, scientificNameID should be populated with the WoRMS LSID for the name given to scientificName. For records recorded as Incertae sedis, scientificNameID should be populated with urn:lsid:marinespecies.org:taxname:12. We recommend also populating verbatimIdentification with the name that was originally documented (e.g. phototrophic eukaryote). 5.6.3 Compiling qPCR datasets Compiling qPCR data is a little bit different than compiling eDNA or metabarcoding data. One of the main differences is that there are no sequences recorded in the DNA_sequence field of the DNA derived data extension. Instead, occurrences are based on detections made using species-specific primers and either qPCR (Quantitative Polymerase Chain Reaction) or ddPCR (Droplet-Digital Polymerase Chain Reaction), no sequencing is done. It is very important to document the methods used for this type of data because the results can be sensitive to the specificity of the primers/assays used. Therefore documenting as much detail on the methodologies is important to ensure data interpretability. Occurrence core table In addition to the Occurrence core terms, it is strongly recommend to including the following terms for qPCR data: Class Occurrence | DwC:recordedBy Class Occurrence | DwC: organismQuantity Class Occurrence | DwC: organismQuantityType Class Event | DwC: sampleSizeValue Class Event | DwC: sampleSizeUnit Class Event | DwC: samplingProtocol Class Material Sample | DwC:materialSampleID For ddPCR, organismQuantity refers to the number of positive droplets/chambers in the sample, and organismQuantityType is the partition type (e.g., ddPCR droplets, dPCR chambers). sampleSizeValue will be populated with the number of accepted partitions, e.g. meaning accepted droplets in ddPCR or chambers in dPCR. sampleSizeUnit is the partition type, which should be the same as organismQuantityType. All four of these fields are particularly important to include for ddPCR data. For qPCR, these fields can be used for recording e.g. the number of copies that were calculated for the target gene in the sample. In this case organismQuantityType needs to contain the exact type of the measurement reported in the results. The field accepts any string, but the best practice would be to add a URI pointing to a vocabulary, as is done in the extendedMeasurementOrFact extension. The terms sampleSizeValue and sampleSizeUnit would not be used in this case. materialSampleID should contain an identifier for the MaterialSample (i.e. occurrence record), rather than a digital record of the material sample. If an ID was obtained from a nucleotide archive, use the associated biosample ID. Otherwise, construct a persistent unique identifier from a combination of elements in the data that will make the materialSampleID globally unique, similar to eventIDs and occurrenceIDs. recordedBy can be populated with the names of the people, groups, or organizations responsible for recording the original Occurrence. You can use a concatenated list for multiple names, by separating values with a vertical bar (’ | ’). DNA Derived Data extension For qPCR datasets, it is strongly recommended to document as much detail as possible in this extension, particularly details about the PCR primers used and the target gene. We recommend you include the following terms, where relevant. For term definitions see DNA derived data extension. Terms related to the sampling event: DNA Derived | DwC: env_broad_scale DNA Derived | DwC: env_local_scale DNA Derived | DwC: env_medium DNA Derived | DwC: samp_collect_device DNA Derived | DwC: samp_mat_process DNA Derived | DwC: samp_size DNA Derived | DwC: size_frac Terms related to DNA and PCR methods: DNA Derived | DwC: sop DNA Derived | DwC: concentration DNA Derived | DwC: concentrationUnit DNA Derived | DwC: methodDeterminationConcentrationAndRatios DNA Derived | DwC: contaminationAssessment DNA Derived | DwC: target_gene DNA Derived | DwC: target_subfragment DNA Derived | DwC: ampliconSize DNA Derived | DwC: amplificationReactionVolume DNA Derived | DwC: amplificationReactionVolumeUnit DNA Derived | DwC: baselineValue DNA Derived | DwC: automaticBaselineValue DNA Derived | DwC: automaticThresholdQuantificationCycle DNA Derived | DwC: thresholdQuantificationCycle DNA Derived | DwC: pcr_analysis_software DNA Derived | DwC: pcr_primer_forward DNA Derived | DwC: pcr_primer_reverse DNA Derived | DwC: pcr_primer_name_forward DNA Derived | DwC: pcr_primer_name_reverse DNA Derived | DwC: pcr_primer_reference DNA Derived | DwC: pcr_cond DNA Derived | DwC: pcr_primer_lod DNA Derived | DwC: pcr_primer_loq DNA Derived | DwC: annealingTemp DNA Derived | DwC: annealinTempUnit DNA Derived | DwC: probeQuencher DNA Derived | DwC: probeReporter DNA Derived | DwC: quantificationCycle DNA Derived | DwC: ratioOfAbsorbance260_230 DNA Derived | DwC: ratioOfAbsorbance260_280 There are many specialized qPCR terms that are possible to add to the dataset. The terms concentration, concentrationUnit, ratioOfAbsorbance260_230, ratioOfAbsorbance260_280, and methodDeterminationConcentrationAndRatios are related to the original DNA sample before qPCR analysis, and can be useful for evaluating the prevalence of the marker in the sample as well as the purity of the DNA for any indication of PCR inhibition. As with the metabarcoding dataset, the details of the PCR conditions and primers can be recorded in the multiple pcr_ terms as well as target_ terms, and amplificationReactionVolume and amplificationReactionVolumeUnit. The main terms that are important for the quantification information and are different from the metabarcoding dataset are baselineValue, thresholdQuantificationCycle and quantificationCycle. The terms pcr_primer_lod, pcr_primer_loq, probeQuencher, probeReporter are additional terms specific for qPCR assays. The baselineValue indicates the number of cycles below which the signal is considered only background noise. The quantificationCycle is the most important and indicates at which cycle the particular sample crossed the detection threshold, this will be different for each sample. It is recommended to record this information, but not all of this may be easily available. 5.6.4 OBIS Bioinformatics Pipeline OBIS recognizes the vast amount of data generated from marine DNA sampling, especially from eDNA sequencing. Thus we have been developing a bioinformatics pipeline to facilitate publication of this data into OBIS. The pipeline was initially developed for the PacMAN project (Pacific Islands Marine Bioinvasions Alert Network). Broadly speaking, it creates a framework that receives raw sequence data from eDNA samples, cleans, aligns, classifies sequences, and finally outputs a DwC-compatible table. To note, the pipeline automatically searches for aphia-IDs from WoRMS to include in the DWC-compatible tables. In addition the output also contains a phyloseq object, which is compatible with the commonly used phyloseq R package for sequence data analysis. The pipeline is under active development. More details about the PacMAN pipeline can be found on its associated GitHub repository. OBIS is developing guidelines and pipelines to accept other data types, such as: Acoustic Imaging Tracking Habitat "],["vocabulary.html", "6 Standardizing terms & vocabularies 6.1 Map data fields to Darwin Core 6.2 Map eMoF measurement identifiers to preferred vocabulary", " 6 Standardizing terms & vocabularies Content Map data fields to Darwin Core Map eMoF measurement identifiers MeasurementOrFact vocabulary background Guidelines to populate measurementUnitID Guidelines to populate measurementValueID Guidelines to populate measurementTypeID 6.1 Map data fields to Darwin Core There are many possible ways of setting up your datasheets, and if you are new to OBIS you likely did not use controlled Darwin Core (DwC) or BODC vocabulary before samples were collected. In mapping your data fields to DwC we recommend documenting your choices so you have a reference to go back to should the need arise. In such a document you should take notes on the choices you made, as well as any actions you had to take (e.g. separate one column into many, convert dates or coordinates, etc.). For example, a DwC mapping reference table could look like the following: Verbatim field name Mapped DwC term Actions taken Notes date eventDate convert dates to ISO coordinates decimalLongitude, decimalLatitude convert ddmmss to decimal degrees, separated one column into 2 for longitude and latitude put original coordinates into verbatimCoordinates In order to help you map your data to DwC terms, we have provided the table below which outlines some common data fields, their associated Darwin Core vocabulary, and which data table the field is likely to go in: Common Raw Terms DwC Field Data table Date, Time eventDate Event, Occurrence Species, g_s, taxa scientificName Occurrence Any biotic/abiotic measurements* measurementType, measurementValue, measurementUnit* eMoF Depth maximumDepthInMeters or minimumDepthInMeters Event, Occurrence Lat/Latitude, Lon/Long/Longitude, dd decimalLatitude, decimalLongitude Event, Occurrence Sampling method samplingProtocol Event, eMoF Sample size, N, #, No. sampleSizeValue Event, eMoF Location locality Event Presence, absence occurrenceStatus Occurrence Type of record/ specimen basisofRecord Occurrence Person/ people that recorded the original Occurrence recordedBy Occurrence OrcID of person/ people that recorded the original Occurrence recordedByID Occurrence Person/ people that identified the organism identifiedBy Occurrence OrcID of person/ people that identified the organism identifiedByID Occurrence Data collector, data creator recordedBy Event, Occurrence Taxonomist, identifier identifiedBy Occurrence Record number, sample number, observation number occurrenceID (either ID or incorporated into ID) Occurrence Note that mapping abiotic/biotic measurement fields (sex, temperature, abundance, lengths, etc.) will occur within the extendedMeasurementOrFact extension. Here this data will go from being a separate column to being condensed into the measurementType and measurementValue fields. The obistools R package also has the map_fields function that you can use to map your dataset fields to a DwC term. 6.2 Map eMoF measurement identifiers to preferred vocabulary MeasurementOrFact vocabulary background The MeasurementOrFact terms measurementType, measurementValue, and measurementUnit are completely unconstrained and can be populated with free text. While free text offers the advantage of capturing complex and as yet unclassified information, there is inevitable semantic heterogeneity (e.g., of spelling, wording, or language) that becomes a challenge for effective data interoperability and analysis. For example, if you were interested in finding all records related to length measurements, you would have to try to account for all the different ways “length” was recorded by data providers (length, Length, len, fork length, etc.). Caution: You can use the OBIS Measurement Type search tool to see the diversity of measurementTypes that exist across published datasets in OBIS. However note that any measurementTypeIDs listed in this tool are solely for consultation purposes. In some cases codes may have been incorrectly chosen for the associated measurementType. You should always choose measurementTypeIDs based on your own data and the guidelines in this manual. The 3 identifier terms measurementTypeID, measurementValueID and measurementUnitID are used to standardize the measurement types, values and units. These three terms should be populated using controlled vocabularies referenced using Unique Resource Identifiers (URIs). For OBIS, we recommend using the internationally recognized NERC Vocabulary Server, developed by the British Oceanographic Data Centre (BODC). This server can be accessed through: SeaDataNet facet search https://vocab.seadatanet.org/p01-facet-search (recommended for searching the P01 collection, detailed below) NERC Vocabulary Server (NVS) search https://www.bodc.ac.uk/resources/vocabularies/vocabulary_search/ Semantic Model Vocabulary Builder https://www.bodc.ac.uk/resources/vocabularies/vocabulary_builder/ Controlled vocabularies are incredibly important to ensure data are interoperable - readable by both humans and machines and that the information is presented in an unambiguous manner. Vocabulary collections like NERC NVS2 compile vocabularies from different institutions and authorities (e.g., ISO, ICES, EUNIS), allowing you to map your data to them. In this way, you could search for a single measurementTypeID and obtain all related records, regardless of differences in wording or language used in the data. Each vocabulary “term” in NVS is a concept that describes a specific idea or meaning. For consistency, we refer to individual vocabularies in NVS as concepts. Concepts within NVS are organized into collections that group concepts with commonalities (e.g. all concepts pertaining to units). Sometimes collections contain concepts that are deprecated. Terms can be deprecated due to duplication of concepts, or when a term becomes obsolete. You should not use any deprecated concepts for any measurement ID. Deprecated concepts can be identified from lists on NVS because their identifier will have a red warning symbol, and the page for the term itself will indicate the concept is deprecated in red lettering. Unfortunately, there is currently no notification system in place to automatically warn you if a previously used concept has become deprecated. We recommend occasionally confirming that the concepts you or your institution use are still available for use. Guidelines for populating each mesaurement ID are described below. 6.2.1 Populate measurementUnitID The measurementUnitID field is the easiest measurement ID field to populate. It is used to provide a URI for the unit associated with the value provided to measurementValue (e.g. cm, kg, kg/m2). This field should be populated with concepts from the P06 collection, BODC-approved data storage units. Documentation for this collection can be found here. The entire vocabulary list, including deprecated terms can be found at http://vocab.nerc.ac.uk/collection/P06/current. However, we strongly recommend using https://www.bodc.ac.uk/resources/vocabularies/vocabulary_search/P06/ to avoid potentially selecting deprecated terms. Some examples of measurementUnits and the associated measurementUnitID include: Metres: http://vocab.nerc.ac.uk/collection/P06/current/ULAA/ Days: http://vocab.nerc.ac.uk/collection/P06/current/UTAA/ Metres per second: http://vocab.nerc.ac.uk/collection/P06/current/UVAA/ Percent: http://vocab.nerc.ac.uk/collection/P06/current/UPCT/ Milligrams per litre: http://vocab.nerc.ac.uk/collection/P06/current/UMGL/ 6.2.2 Populate measurementValueID The measurementValueID field is used to provide an identifying code for measurementValues that are non-numerical (e.g. sampling related, sex or life stage designation, etc.). measurementValueID is NOT used for standardizing numeric measurements! Unlike measurementUnitID, there is more than one collection which may be used to search for and use concepts from. The collection is dependent on which type of measurementValue you have. See the table below for some common, non-exhaustive examples. Note that behaviour is an exception in that we recommend to use codes from the ICES vocabulary server, instead of NERC. The video below, from the OBIS YouTube Vocabulary series , demonstrates how to find and select vocabularies for measurementValueID from both NERC and ICES. Type of measurementValue Collection Collection Documentation Complete Vocabulary List Sex (gender) S10 https://github.com/nvs-vocabs/S10 <http://vocab.nerc.ac.uk/collection/S10/current/ Lifestage S11 https://github.com/nvs-vocabs/S11 http://vocab.nerc.ac.uk/collection/S11/current/ Sampling instruments and sensors (SeaVoX Device Catalogue) L22 https://github.com/nvs-vocabs/L22 http://vocab.nerc.ac.uk/collection/L22/current Sampling instrument categories (SeaDataNet device categories) L05 https://github.com/nvs-vocabs/L05 <http://vocab.nerc.ac.uk/collection/L05/current Vessels (ICES Platform Codes) C17 - http://vocab.nerc.ac.uk/collection/C17/current European Nature Information System Level 3 Habitats C35 - https://vocab.nerc.ac.uk/collection/C35/current/ Behaviour ICES Behaviour collection https://vocab.ices.dk/?codetypeguid=d0268a96-afc9-436e-a4db-4f61c2b9f6ba You can also populate measurementValueID with references to papers or manuals that document the sampling protocol used to obtain the measurement. To do this you should use either: The DOI of the paper/manual Handle for publications on IOC’s Ocean Best Practices repository, e.g. http://hdl.handle.net/11329/304 6.2.3 Populate measurementTypeID 6.2.3.1 The P01 Collection One of the more important collections for OBIS is the P01 collection. Important note! P01 codes are required for the measurementTypeID field. The P01 is a large collection with >45,000 concepts. Each concept within this collection is composed of different elements that, together, construct a label you can use for a measurement type. Frequently, concepts from the P01 collection are referred to as a “P01 code”. P01 codes are used to populate the measurementTypeID field. It is important to know that a semantic model, shown below, underlies each P01 code and the elements that compose them. There are 5 potential elements in this semantic model that, together, unambiguously describe a measurement type. See the P01 wheel for example of how these elements relate and combine to make one P01 code. Elements of the semantic model for P01 codes Property/attribute: the measurement or observation of either an object of interest or a matrix, or both Object of interest: a chemical object, a biological object, a physical phenomenon, or a material object In relation to: how the measurement is related to the environment Environmental matrix: what environment the measurement is in (e.g. water body, seabed); needed for most environmental measurements, but may not be necessary for e.g. biological measurements Method: any specific methods used that are important to interpret the measurement Note: Not every element is required, but it is important to think about each piece of the model and how it may or may not apply to your measurement. More details about this are described below in the mesaurementTypeID section. You can use codes from other collections (e.g. P06, QL22) for measurementValueID and measurementUnitID fields, but for measurementTypeID you must always use a code from the P01 collection. 6.2.3.1.1 Selecting P01 codes for measurementTypeID When selection P01 codes, it is important to understand that each element within a P01 code is meant to describe an aspect of the measurement: what is the measurement, what is the object or entity being measured, in what environment was the measurement taken, by what kind of methods, etc.? By taking together all these elements, we are able to have a unique and specific description to differentiate one measurement from another. More documentation about the P01 code and the semantic model it is based on can be found here. The P01 collection is found here and can be searched through the NERC vocabulary server. You may notice when searching for measurement types related to an occurrence that specific taxonomic codes are available to you, e.g., abundance of Notommata. For OBIS, all P01 codes should be generalized - i.e. do not select species-specific codes. Instead only choose codes for “biological entities specified elsewhere” when the measurement is related to an occurrence record. There are several ways of searching for a P01 code, but we highly recommend using the SeaDataNet P01 Facet Search. You may notice when searching for measurement types related to an occurrence that specific taxonomic options are available to you, e.g., abundance of Notommata. For OBIS, all P01 codes should be generalized - i.e. do not select species-specific codes. Instead, only choose codes for “biological entities specified elsewhere”! This is due to the Darwin Core Archive structure - taxonomic identification is already specified in the Occurrence table, but measurements are recorded in the ExtendedMeasurementOrFact table. When you are comfortable and understand P01 codes, you can also use the BODC Vocabulary Builder or simply search for terms directly on the NERC Vocabulary Server. Vocabulary for measurements related to sampling instruments and/or sampling methods can also be found in the P01 collection and are mostly classified as sampling parameters (a search keyword that can be used in the SeaDataNet Facet search). These vocabulary used to fall under the Q01 collection, however this collection has been deprecated and the terms have been remapped to P01 codes (see GitHub issue on this here). See below for examples of sampling-related measurements and their associated P01 code: Description of sub-sampling protocol: https://vocab.nerc.ac.uk/collection/P01/current/DSPSSP01/ Length of sampling track https://vocab.nerc.ac.uk/collection/P01/current/LENTRACK/ Height of sample collector (e.g. a grab) https://vocab.nerc.ac.uk/collection/P01/current/MTHHGHT1/0 Name of sampling instrument https://vocab.nerc.ac.uk/collection/P01/current/NMSPINST/ Sample duration https://vocab.nerc.ac.uk/collection/P01/current/AZDRZZ01/ Use the following decision tree to help you select P01 codes for biological, chemical, physical, and/or sampling measurements. See the OBIS YouTube Vocabulary series for guidance on how to use this tree and examples for different types of measurementTypeIDs. Note that the measurementTypeID:sampling measurements branch is newly developed, and we are happy to receive feedback if the suggestions are not working for you. _Decision tree to assist with finding and selecting appropriate controlled vocabulary to be used in the eMoF. For those unfamiliar with the tree see the OBIS YouTube Vocabulary series. 6.2.4 Request new vocabulary terms If you have already tried looking for a P01 code and were unable to identify a suitable code for your measurementType you must then request a code to be created. Before doing so, make sure you have not over filtered the search results. Then, to request a new term, your request must be submitted via: Submit request through the OBIS Vocabulary GitHub repository (https://github.com/nvs-vocabs/OBISVocabs/issues) Requests can also be emailed to vocab.services@bodc.ac.uk if you cannot access GitHub Registration with the BODC Vocabulary Builder Note: these requests should be based on combinations of existing concepts We strongly recommend you use GitHub if you can, as it allows longer-term documentation, and can be relevant for other users who may be interested in the same type of code creation. Finally, if you are unsure about whether a code fits your specific case, please feel free to ask questions to the Vocab channel on the OBIS Slack. 6.2.4.1 How to Submit a GitHub Vocabulary Request Navigate to https://github.com/nvs-vocabs/OBISVocabs/issues and click on the New Issue button. Screenshot of how to request a new vocabulary on Github Click Get started Screenshot of submitting an issue to Github Fill in the title with short details of your request or issue. Then fill in the description. It is recommended to list any existing terms that are similar to your request, or concepts that are sub-components of the request. Screenshot for how to request a new measurementType on Github Example: An issue was created to address difficulties in identifying P01 codes for sex rather than gender. Gender is a concept generally applied to humans, whereas “sex” is more applicable for animals. Thus the request was to either modify the current gender P01 code, or create a P01 code that specifies sex, not gender. At the time the request was issued, when users searched for a P01 term for “sex”, only species-specific terms were available. Example of previously requested new term on Github "],["common_formatissues.html", "7 Other Important Data Formatting Steps 7.1 Temporal: Dates and times 7.2 Historical data 7.3 Spatial 7.4 Missing required fields", " 7 Other Important Data Formatting Steps After creating data tables and mapping terms to DwC, additional data formatting steps may be needed and/or some common issues may arise. On this page, you will find guidelines for specific data formatting cases, including: Temporal issues: dates/times Historical data Spatial issues: coordinate conversion Convert geographical formats Missing required fields 7.1 Temporal: Dates and times The date and time at which an event took place or an occurrence was recorded goes in eventDate. This field uses the ISO 8601 standard. OBIS recommends using the extended ISO 8601 format with hyphens. All dates in OBIS become translated to UTC during the quality control process implemented by OBIS. Formatting your dates correctly ensures there will be no errors during this process. ISO 8601 dates can represent moments in time at different resolutions, as well as time intervals, which use / as a separator. Date and times are separated by T. Timezones can be indicated at the end by using + or - the number of hours offset from UTC. If no timezone is indicated, then the time is assumed to be local time. When a date/time is recorded in UTC, a Z should be added at the end. Times must be written in the 24-hour clock system. If you do not know the time, you do not have to provide it. Please do not indicate unknown times as “00:00” as this indicates midnight. Not every piece of time information is necessary, but a generalization of how to format dates and times looks like: YYYY-MM-DDT[hh]:[mm]:[ss] [+/-XX OR Z] Some specific examples of acceptable ISO 8601 dates are: Dates: 1948-09-13 1993-01/02 1993-01 1993 Dates with Specific Times: 1973-02-28T15:25:00 2008-04-25T09:53 Dates with Time Zones: 2005-08-31T12:11+12 2013-02-16T04:28Z Date and Time Intervals: 1993-01-26T04:39+12/1993-01-26T05:48+12 It is important to note that although ISO 8601 also supports ordinal dates (YYYY-DDD) and week dates (YYYY-Www-D), these formats are not supported by OBIS. Additionally, ISO 8601 guidelines for durations should not be used. Durations for an event (e.g., length of observation) can instead be indicated with the DwC terms startDayOfYear and endDayOfYear. Durations refer to the actual length of time an event (e.g., occurrence) occurred, whereas intervals indicate the time period during which an event was recorded. A note about intervals: Take care when entering date intervals as, for example, entering 1960/1975-08-04 indicates that the event or observation started any time in 1960, and ended any time on 1975-08-04. If you know the exact date and time, you should specify that information. This also helps for continuous samplings and time-series integrated datasets. If you have a mix of dates and times for different aspects of a sampling event, you can embed this information in the Event Core table using hierarchies of date structure. To do this, you can use separate records for events, and specify each event date individually. See example. For uncertainty regarding the date of the event, see guidelines. Tips To ensure your date is formatted correctly, it may be easiest to begin by populating the year, month, and day fields first. If the specific time of sampling is known, populate that into eventTime as well. When you fill these fields, we recommend ensuring the numbers are encoded as Text, not as General or numeric as Excel often tries to interpret what it thinks the content “should” be. Otherwise you may run into problems with Excel auto formatting your numbers in ways you don’t want. A caution about dates and Excel: Excel is unfortunately notorious for causing issues in saving dates. The Data Carpentries have produced this exercise which demonstrates how Excel interprets dates and numbers, sometimes converting numbers into dates and vice versa. The exercise is simply a demonstration of Excel - it does not provide advice on formatting dates for OBIS. You can encode fields as text in Excel by highlighting the cells of interest, navigating to the Number Format on the Home ribbon and selecting “Text”, see screenshot below. Be careful when you do this change of format, as some columns (e.g. time) may become formatted into a decimal or other unexpected format! Screenshot of how to change data type in Excel Then you can use Excel to concatenate each field together, adding the time zone at the end, using the general format: =CONCAT(YEAR, "-", MONTH, "-", DAY, "T", EVENTTIME, TIMEZONE) Example of how to concatenate dates in Excel Note: You can also use the Canadensys date parsing tool to help you convert dates or parse them into component parts. Date formats in Excel can be very dependent on your computer system region custom and not all of them have the ISO 8601 format included. Therefore you can type the date in the requested format but it will automatically revert the format according to your Windows system region settings. You can change your system region by: navigating to Control Panel > All Control Panel Items > Region and then select “English (United States)” or “English (United Kingdom)”. The YYYY-MM-DD format will appear among the choices within the Format cells - Date options. If your computer language is not set to English, you may encounter additional issues with Excel. It may change the format of your date even after you save the document. Changing your computer system’s language to English can help, but you may still run into issues. You may also try using other office management softwares, like LibreOffice which in this case is more friendly. In general, we advise you to be very careful when formatting the eventDate field, and to select the “Text” formatting (as above) and to save your file as a .CSV. It is good practice to place the verbatim event date/time description into the verbatimEventDate field. Any modifications you make during data formatting should be recorded in the eventRemarks field, and we recommend taking good notes in a personal reference file. This video provides a demonstration of how you can format dates to ISO 8601, including how to resolve difficulties you might run into (some which are also discussed below). How to handle mixed date information When the sampling date is unclear due to a mix of date types and durations, you can use the hierarchical event structure in the Event core to help format and associate dates (and other information) with the correct sampling event. Often times it is useful to break up each type of event into separate records according to the event hierarchy. Let’s look at a fictional dataset to demonstrate this. In this example, there is a project called Maple. This project has a cruise that takes place in May and June which takes samples at three different sites. The project has data for three years, beginning in 1993. How do we capture all this information in our dataset? And how do we format eventDate to reflect these different times and durations? We can embed this information using a different eventID and parentEventID for each level within the project - the reoccurring cruise, the station sites, and then the samples themselves. We have added a column here for “Event Type” for the sake of example. At this time, there is no Darwin Core event type term, however there is discussion for its creation. Our example dataset would then look like the following: parentEventID eventID Event type eventDate eventRemarks MAPLE_1993_crs cruise This is the first year MAPLE_1994_crs cruise This is the second year MAPLE_1995_crs cruise This is the third year MAPLE_1993_crs MAPLE_1993_crs_st1 station This is the first year, first site MAPLE_1993_crs MAPLE_1993_crs_st2 station This is the first year, second site where samples were taken over two days MAPLE_1993_crs MAPLE_1993_crs_st3 station This is the first year, third site MAPLE_1993_crs_st1 MAPLE_1993_crs_st1_s1 sample 1993-05-05T10:13-04 This is the first year, first site, first sample MAPLE_1993_crs_st1 MAPLE_1993_crs_st1_s2 sample 1993-05-05T10:38-04 This is the first year, first site, second sample MAPLE_1993_crs_st2 MAPLE_1993_crs_st2_s1 sample 1993-05-19T23:40-04 This is the first year, second site, first sample MAPLE_1993_crs_st2 MAPLE_1993_crs_st2_s2 sample 1993-05-20T01:24-04 This is the first year, second site, second sample MAPLE_1993_crs_st3 MAPLE_1993_crs_st3_s1 sample 1993-06-01T09:21-04 This is the first year, third site, first sample MAPLE_1993_crs_st3 MAPLE_1993_crs_st3_s2 sample 1993-06-01T09:57-04 This is the first year, third site, second sample You can see that the eventDate for the parent events does not need to be provided - only the dates for the actual samples are required. 7.2 Historical data OBIS recognizes the difficulties in formatting historical, archaeological and paleontological data. This kind of data is sometimes seen as “specialist” or “niche” when it comes to sharing in globally accepted databases that are accessible and recognised in academic, research and scientific forums. Some of the nuances associated with historical data have to do with the change in calendar systems, from the Julian calendar to the currently used (by most countries) Gregorian calendar metric system. This change was implemented in 1582, so any datasets with data representing periods that predate this year must be checked and converted to the standard Gregorian calendar system. Additionally, there is no year zero, only -1 and 1, where -1 is BCE (Before Common Era) and 1 is CE (Common Era). This can make interpretation of historical dates more challenging as such dates will need to be converted to align with ISO 8601 standards. To accommodate such challenges the OBIS Historical Data Project Team recommends the following: Always populate verbatimEventDate with the originally documented date so that it can be preserved. Place converted dates that align to ISO 8601 in the eventDate field, and document the changes you made to the original in eventRemarks. When the exact date is unknown, provide a date range, e.g. the period 21 November 1521 to 29 August 1612 records as 1521-11-21/1612-08-29. For archaeological data, use terms from the Darwin Core class GeologicalContext and/or the Chronometric Age Extension. GeologicalContext terms can be used to capture information such as periods or ages, however the Chronometric Age extension allows for more thorough descriptions of the time period and can link to the Event core table. For such records, eventDate would be populated with the date of collection. N.B. currently, the Chronometric Age extension will not be aggregated when publishing to OBIS, but the extension will be available when an individual dataset is downloaded. If the historical record contains uncertain or sensitive location information, generalize the location information using polygons or lines as described in this Manual. For historical data originating from old records, such as ship logs or other archival records, we understand there can be a variety of issues in interpreting and formatting data according to DwC standards. If you need further help with historical data formatting, we recommend submitting a Github issue, or contacting the OBIS-OPI node who focuses on Oceans Past historical, archaeological, and paleontological data series. 7.3 Spatial Converting Coordinates All coordinates provided in the decimalLatitude or decimalLongitude fields in OBIS must be in decimal degrees. To convert coordinates from degrees-minutes-seconds into decimal degrees, you can use this Coordinate Conversion tool that OBIS has developed. This tool will convert any coordinate (or list of coordinates on a separate line) in a degrees-minutes-seconds format into decimal degrees, even partial coordinates. To use it, simply copy and paste your coordinates into the box provided and click Convert. For example: Screenshot of how to use the OBIS coordinate converter Watch this video for a demonstration on use of this tool. If your coordinates are in UTMs, then coordinate conversion can be a bit trickier. We suggest using the following conversion tool to convert from UTM to decimal degrees. Note it is very important to ensure you have the correct UTM zone, otherwise the coordinate conversion will be incorrect. You can use this ArcGIS map tool to visually confirm UTM zones. Geographical format conversion In OBIS, the spatial reference system to be documented in geodeticDatum is EPSG:4326 (WGS84). If your spatial data are not already in this format, you may have to convert it. To do this there are a few approaches: QGIS (or ArcGIS), R, or Python. We provide some short guidance for each, however if you are struggling to convert your data to WGS84 please contact helpdesk@obis.org or send a message on the OBIS Slack. QGIS You can load a .csv file containing your coordinates to be reprojected into QGIS. Opening a new project, first set the global projection to WGS84 EPSG:4326. In the bottom right corner, click the Project Properties to change the Project Coordinate Reference System (CRS). A pop up window will allow you to search for and select WGS84 EPSG:4326. Click OK. Screenshot of QGIS interface To load your .csv file containing the longitude and latitude coordinates, go to Layer < Add Layer < Add Delimited Text layer… How to add a .csv with coordinate data in QGIS A popup window will allow you to browse and select your .csv file. Open the Geometry Definition portion of the window and map the field containing longitude values to the X field and latitude to the Y field. Select the CRS that these coordinates were recorded as from the drop down menu. Then click Add and close the window. Screenshot showing how to specify CRS of a .csv file when importing into QGIS Go to Vector < Geometry Tools < Add Geometry Attributes Screenshot showing where to find the Geometry Attributes in QGIS menu Make sure the input layer is your coordinate file. Under the Calculate using, select Project CRS (because we set the Project CRS to the desired projection). Click Run. This will create a new layer with an additional two columns called Xcoord (longitude) and Ycoord (latitude). These fields contain the coordinates in the desired projection (i.e., WGS84). You can view these columns by right clicking and opening the layer’s attribute table. To export the file, right click the layer and click Make Permanent. Then save the .csv. Screenshot showing how to save a temporary layer in QGIS for export For more details see this QGIS guide on reprojection. R To reproject coordinates in R, you can use functions in the sf package. A thorough tutorial using this package can be found here. Python You also have the option to reproject data using the Python library Geopandas. In this package there is a utility called to_crs that will reproject data. A tutorial to do this can be found here. 7.4 Missing required fields If you are a node manager and one of the datasets in your IPT is missing data, you should prepare a brief report to contact the data provider and outline what is missing. Get in contact with original data provider if possible. If it is not possible to contact the original data provider (frequently the case for historical datasets), do your best to follow the guidelines below to fill in data. However, do not guess or make assumptions if you are unsure. For all fields inferred, please record notes in the eventRemarks, occurrenceRemarks, or ’identificationRemarks` field, as applicable. If you are a data provider and notice or been notified that you are missing one (or more) of the eight required terms for OBIS, please proceed accordingly for each term. To resolve missing fields marked as required by OBIS, there are several things you can do, depending on which required field is missing. Follow the guidelines below for each term. occurrenceID or eventID Create a unique occurrenceID for each of your observations. These IDs can be generated by combining dates, location names, and sampling methods. eventDate Ensure your eventDate is specified for each event, formatted according to ISO 8601 standards (e.g., YYYY-MM-DD). We have developed step by step guidelines to help you format contemporary dates and durations into ISO formatting. If your date falls outside the range of acceptable dates - i.e., historical or geological data occurring before 1583 - please follow recommendations for historical data. For any eventDate that is inferred from literature, you should document the original date in the verbatimEventDate field. decimalLongitude and decimalLatitude First, if you have coordinate data, make sure they are converted into decimal degrees. If you do not have specific coordinate data then you must approximate the coordinates based on locality name. You can use the Marine Regions gazetteer to search for your region of interest and obtain midpoint coordinates. Guidelines for using this tool and for dealing with uncertain geolocations can be found here. You will have to make some comments in the georeferenceRemarks field if you are estimating coordinates. scientificName This field should contain only the originally documented scientific name down to the lowest possible taxon rank, even if there are misspellings or if it is a current synonym. Class or even Kingdom levels are accepted if more specific taxonomic levels are unknown. Comments about misspellings, etc. can be documented in the taxonRemarks field. Note that there may be special cases for eDNA and DNA derived data, see specific guidelines for these cases. You may encounter challenges filling this field if the species name is based on description or if its taxonomy was uncertain at the time of sampling. For such uncertain taxonomy situations, see our guidelines here. scientificNameID (strongly recommended) If you cannot obtain the required Life Science Identifier (LSID) from taxon matching with WoRMS then you must contact World Register Marine Species to have an LSID created for your taxon. You will need to confirm that the species is marine. OBIS does not parse LSIDs from other sources (e.g., Integrated Taxonomic Information System, Catalog of Life), but if you want to include other LSIDs alongside the WoRMS LSID, they must be specified in a predictable format. occurrenceStatus Because occurrenceStatus is a binary field, “presence” or “absence”, this field can usually be easily inferred by data. If there are associated measurements or a record of an observation, the taxon in question would be present. If a particular species/taxa is present in one sample, but missing from another, then you could identify that species as absent from the second sample. basisOfRecord basisOfRecord distinguishes what type of record is in your data. For records pertaining to a collected or stored specimen, you must choose one of the following terms: PreservedSpecimen FossilSpecimen LivingSpecimen For records pertaining to an observation in the wild, you should use: HumanObservation (e.g., observation in the wild) MachineObservation (e.g., photograph, acoustic detection, etc.) MaterialSample (e.g. DNA sequences, DNA detection) For records pertaining to literature data, basisOfRecord should always reflect the evidence upon which the Occurrence record was based. For example, a researcher’s record based on photographs should specify MachineObservation, otherwise specifications should be HumanObservation (see relevant GitHub discussion). For specifics on when to use each of these and which other fields should be populated along with them, see the guidelines on record-level terms. "],["examples.html", "8 Dataset Examples: ENV-DATA", " 8 Dataset Examples: ENV-DATA Contents Fish abundance & distribution Hard coral cover & composition Invertebrates abundance & distribution Macroalgae canopy cover & composition Mangroves cover & composition Marine birds abundance & distribution Marine mammals abundance & distribution Marine turtles abundance & distribution Survey & sighting data Microbes biomass & diversity Phytoplankton biomass & diversity Seagrass cover & composition Zooplankton biomass & diversity Special data types: DNA derived data eDNA data from Monterey Bay, California 16S rRNA gene metabarcoding data of Pico- to Mesoplankton Acoustic, imaging, or other multimedia data Tracking data Habitat 8.0.0.1 Fish abundance and distribution (example coming soon)   8.0.0.2 Hard coral cover and composition (example coming soon)   8.0.0.3 Invertebrates abundance and distribution (example coming soon)   8.0.0.4 Macroalgae canopy cover and composition In this section we will encode a fictional macroalgal survey dataset into Darwin Core using the ENV-DATA approach, i.e. using an Event core with an Occurrence extension and an extendedMeasurementOrFact extension. Figure: A fictional macroalgae survey with a single site, multiple zones, quadrats, and different types of transects. Event core: First we can create the Event core table by extracting all events in a broad sense and populating attributes such as time, location, and depth at the appropriate level. The events at the different levels are linked together using eventID and parentEventID. As the survey sites has a fixed location we can populate decimalLongitude and decimalLatitude at the top level event. The zones have different depths, so minimumDepthInMeters and maximumDepthInMeters are populated at the zone level. Finally, as not all sampling was done on the same day, eventDate is populated at the quadrat and transect level. eventID parentEventID eventDate decimalLongitude decimalLatitude minimumDepthInMeters maximumDepthInMeters site_1 54.7943 16.9425 zone_1 site_1 0 0 zone_2 site_1 0 5 zone_3 site_1 5 10 quadrat_1 zone_1 2019-01-02 transect_1 zone_2 2019-01-03 transect_2 zone_3 2019-01-04 Occurrence extension: Next we can construct the Occurrence extension table. This table has the scientific names and links to the World Register of Marine Species in scientificNameID. The first column of the table references the events in the core table (see quadrat_1 for example highlighted in green). id occurrenceID scientificName scientificNameID quadrat_1 occ_1 Ulva rigida urn:lsid:marinespecies.org:taxname:145990 quadrat_1 occ_2 Ulva lactuca urn:lsid:marinespecies.org:taxname:145984 transect_1 occ_3 Plantae urn:lsid:marinespecies.org:taxname:3 transect_1 occ_4 Plantae urn:lsid:marinespecies.org:taxname:3 transect_2 occ_5 Gracilaria urn:lsid:marinespecies.org:taxname:144188 transect_2 occ_6 Laurencia urn:lsid:marinespecies.org:taxname:143914 extendedMeasurementOrFact (eMoF) extension: And finally there is the MeasurementOrFact extension table, which has attributes of the zones (shore height), the quadrats (surface area), the transects (surface area and length), and the occurrences (percentage cover and functional group). Attributes of occurrences point to the Occurrence extension table using the occurrenceID column (see occ_1 and occ_2 highlighted in blue and orange). Note that besides NERC vocabulary terms we are also referencing the CATAMI vocabulary for macroalgal functional groups. id occurrenceID measurementType measurementTypeID measurementValue measurementValueID measurementUnit measurementUnitID zone_1 shore height ? high ? quadrat_1 surface area P01/current/AREABEDS 0.25 m2 P06/current/UMSQ quadrat_1 occ_1 cover P01/current/SDBIOL10 24 percent P06/current/UPCT quadrat_1 occ_2 cover P01/current/SDBIOL10 56 percent P06/current/UPCT transect_1 surface area P01/current/AREABEDS 60 m2 P06/current/UMSQ transect_1 length P01/current/LENTRACK 30 m P06/current/ULAA transect_1 occ_3 functional group ? sheet-like red CATAMI:80300925 transect_1 occ_4 functional group ? filamentous brown CATAMI:80300931 transect_1 occ_3 cover P01/current/SDBIOL10 8 percent P06/current/UPCT transect_1 occ_4 cover P01/current/SDBIOL10 24 percent P06/current/UPCT transect_2 occ_5 cover P01/current/SDBIOL10 4 percent P06/current/UPCT transect_2 occ_6 cover P01/current/SDBIOL10 16 percent P06/current/UPCT 8.0.0.5 Mangroves cover and composition (example coming soon)   8.0.0.6 Marine birds abundance and distribution The example for ENV-DATA collected with marine bird sightings/occurrences is based on the dataset “RV Investigator Voyage IN2017_V02 Seabird Observations, Australia (2017)”. In this dataset, seabird sightings were recorded continuously during daylight hours during a voyage to recover and redeploy moorings at the SOTS site, southwest of Tasmania, Australia, in March 2017. Observations were made from c.30 minutes before sunrise to c.30 minutes after sunset, extending to 300m in the forward quadrant with the best viewing conditions. There were 1200 observations from 38 species of birds along with 3 cetacean species and one seal. This example will focus on the ENV-DATA associated with the bird sightings. The most frequently sighted bird species were Puffinus tenuirostris (Short-tailed Shearwater) and Pachyptila turtur (Fairy Prion). For this dataset, human observation recorded individual bird sightings (thus, each specimen is a single occurrence). The dataset contains abiotic measurements (ENV-DATA) which are related to each individual sighting, instead of an entire sample. Therefore, we can create an Occurrence core with an eMoF extension that contain the abiotic environmental measurements or facts. Occurrence core: The Occurrence core is populated with the occurrence records of seabirds sighted during the RV voyages. Occurrence details and scientific names are provided here. All birds were observed above sea level, all minimumDepthInMeters and maximumDepthInMeters values equal zero. occurrenceID eventDate institutionCode collectionCode basisOfRecord recordedBy organismQuantity organismQuantityType occurrenceStatus decimalLatitude decimalLongitude coordinateUncertaintyInMeters coordinatePrecision footprintWKT in2017_v02_00998 2017-03-17 01:07:00 Australasian Seabird Group, BirdLife Australia in2017_v02_wov HumanObservation EJW+CRC+TAH 2 individuals present -43.40741 147.45576 200 0.0018 POINT (147.45576 -43.40741) in2017_v02_01380 2017-03-19 22:26:00 Australasian Seabird Group, BirdLife Australia in2017_v02_wov HumanObservation EJW+CRC+TAH 1 individuals present -45.98644 142.1445 200 0.0018 POINT (142.14450 -45.98644) in2017_v02_01012 2017-03-17 02:38:00 Australasian Seabird Group, BirdLife Australia in2017_v02_wov HumanObservation EJW+CRC+TAH 1 individuals present -43.40728 147.45549 200 0.0018 POINT (147.45549 -43.40728) extendedMeasurementOrFact (eMoF) extension: As shown in previous examples, the MeasurementOrFact extension table contains abiotic measurements or facts corresponding to an occurrence / sighting. Individual sightings and abiotic measurements are linked with occurrenceID. In the example dataset, the ENV-DATA consist of measurements taken during the moorings deployment at the SOTS site, at the time of the marine bird sightings. In addition to NERC vocabulary terms, authors also referenced the Australian Ocean Data Network (AODN) Discovery Parameter Vocabulary for Sea-floor depth (m) and Sea Surface Temperature as measurementType. NERC equivalents to the AODN terms are added as additional MeasurementOrFact (MoF) records. occurrenceID measurementID measurementType measurementTypeID measurementValue measurementValueID measurementUnit measurementUnitID in2017_v02_00998 in2017_v02_00998-depth Sea-floor depth (m) http://vocab.aodn.org.au/def/discovery_parameter/entity/574 73.0313 NA Metres http://vocab.nerc.ac.uk/collection/P06/current/ULAA in2017_v02_00998 in2017_v02_00998-depth Sea-floor depth http://vocab.nerc.ac.uk/collection/P01/current/MBANZZZZ/ 73.0313 NA Metres http://vocab.nerc.ac.uk/collection/P06/current/ULAA in2017_v02_00998 in2017_v02_00998-air_pressure Air Pressure (hPa) http://vocab.nerc.ac.uk/collection/P01/current/CAPHZZ01 1024.91385 NA hPa http://vocab.nerc.ac.uk/collection/P06/current/HPAX in2017_v02_00998 in2017_v02_00998-air_temp Atmospheric temperature (deg C) http://vocab.nerc.ac.uk/collection/P01/current/CTMPZZ01 15.3 NA degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA in2017_v02_00998 in2017_v02_00998-wov_sea_state Sea state http://vocab.nerc.ac.uk/collection/C39/current/ moderate 1.25 - 2.5 m http://vocab.nerc.ac.uk/collection/C39/current/4/ in2017_v02_00998 in2017_v02_00998-sea_surface_temp Sea surface temperature http://vocab.aodn.org.au/def/discovery_parameter/entity/97 17.32 NA degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA in2017_v02_00998 in2017_v02_00998-sea_surface_temp Sea surface temperature http://vocab.nerc.ac.uk/standard_name/sea_surface_temperature/ 17.32 NA degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA in2017_v02_00998 in2017_v02_00998-wind_direction Wind direction (deg) http://vocab.nerc.ac.uk/collection/P01/current/EWDAZZ01 283 NA degrees http://vocab.nerc.ac.uk/collection/P06/current/UABB in2017_v02_00998 in2017_v02_00998-wind_speed Wind Speed (knt) http://vocab.nerc.ac.uk/collection/P01/current/ESSAZZ01 5.49 NA Knots (nautical miles per hour) http://vocab.nerc.ac.uk/collection/P06/current/UKNT 8.0.0.7 Marine mammals abundance and distribution In this section we will explore how to encode a survey data set into Darwin Core using the ENV-DATA approach. As an example, sections of the actual data set of CETUS: Cetacean monitoring surveys in the Eastern North Atlantic, is used. Figure: A representation of the observation events of CETUS: Cetacean monitoring surveys in the Eastern North Atlantic, presenting the route Madeira as a site with three cruises (zones). Each Cruise is divided into different Transects and each transect contains a number of Positions. Event core: Create the Event core table by extracting all events and populating attributes. As in the previous example, the events at the different levels are linked together using eventID and parentEventID. As the survey observations were made at locations of cetacean sightings instead of fixed locations, we can populate footprintWKT and footprintSRS as location information. Not all sampling was done on the same day, therefore eventDate is populated at the transect level. eventID parentEventID eventDate footprintWKT footprintSRS Madeira 2012-07/2017-09 POLYGON ((-16.74 31.49, -16.74 41.23, -8.70 41.23, -8.70 31.49, -16.74 31.49)) EPSG:4326 Madeira:Cruise-001 Madeira 2012-07 MULTIPOINT ( (-8.7 41.19), (-9.15 38.7)) EPSG:4326 Madeira:Cruise-002 Madeira 2012-07 MULTIPOINT ( (-9.15 38.7), (-16.73 32.74)) EPSG:4326 Madeira:Cruise-003 Madeira 2012-07 MULTIPOINT ( (-16.73 32.74), (-9.15 38.7)) EPSG:4326 Occurrence extension: Construct the Occurrence extension table with the scientific names and links to the World Register of Marine Species in scientificNameID. The first column of the table references the events in the core table (see Madeira:Cruise-001 highlighted in green).The occurrenceID corresponds to the Position of the observation (see Transect-01:Pos-0001 and CIIMAR-CETUS-0001 highlighted in blue, or Transect-01:Pos-0002 and CIIMAR-CETUS-0002 highlighted in orange). id occurrenceID scientificNameID scientificName Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-01:Pos-0002 CIIMAR-CETUS-0002 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-01:Pos-0003 CIIMAR-CETUS-0003 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-02:Pos-0004 CIIMAR-CETUS-0004 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-02:Pos-0005 CIIMAR-CETUS-0005 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-02:Pos-0006 CIIMAR-CETUS-0006 urn:lsid:marinespecies.org:taxname:2688 Cetacea Madeira:Cruise-001:Transect-02:Pos-0007 CIIMAR-CETUS-0007 urn:lsid:marinespecies.org:taxname:2688 Cetacea extendedMeasurementOrFact (eMoF) extension: And finally, the extendedMeasurementOrFact extension table has attributes of the zones (such as Vessel speed and Vessel Heading), the Transects (such as Wave height and Wind speed), and the Positions (such as Visibility and the Number of smaal/big ships >20m). Attributes of Positions point to the Occurrence extension table using the occurrenceID column (see Transect-01:Pos-0001 and Transect-01:Pos-0002 highlighted in blue and orange, respectively). id occurrenceID measurementType measurementTypeID measurementValue measurementUnit measurementUnitID Madeira:Cruise-001 Vessel name Q01/current/Q0100001 Monte da Guia Madeira:Cruise-001:Transect-01 Length of the track P01/current/DSRNCV01 39.75 km P06/current/ULKM Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Visibility 2000-4000 Meters P06/current/ULAA Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Wind speed P01/current/WMOCWFBF 1 Beaufort scale Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Wave height 2 Douglas scale Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Number of big ships (>20m) 3 Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Vessel heading P01/current/HDNGGP01 206 Degrees P06/current/UAAA Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Number of small ships (<20m) 0 Madeira:Cruise-001:Transect-01:Pos-0001 CIIMAR-CETUS-0001 Vessel speed P01/current/APSAGP01 16 Knots (nautical miles per hour) P06/current/UKNT 8.0.0.8 Marine turtles abundance and distribution 8.0.0.8.1 Survey and sighting data This section deals with encoding survey and/ or sighting data of sea turtles into Darwin Core using the ENV-DATA approach. Extracts from the actual data set of Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea (Civitavecchia-Barcelona route) between 2013 and 2017, are used as an example. Event core: The Event core is created by extracting all sighting events and populating the attributes at each event. The events at the different levels are linked together using eventID and parentEventID. In the example dataset, turtle sightings have been recorded since 2007, along a ferry route between Italy and Spain, as part of the monitoring project FLT Med Net (Fixed Line Transect Mediterranean monitoring Network). Turtle sighting locations can be given by populating the fields footprintWKT and footprintSRS with location information. Sightings were recorded at different dates, therefore eventDate is populated at the transect level. id modified datasetID datasetName eventID parentEventID eventDate eventRemarks minimumDepthInMeters maximumDepthInMeters decimalLatitude decimalLongitude geodeticDatum coordinateUncertaintyInMeters footprintWKT footprintSRS TURTLE_CBAR_0043 2021-05-05 07:59:08 https://marineinfo.org/id/dataset/6403 Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea TURTLE_CBAR_0043 2013-04-03T05:30:00+02:00/2013-04-03T16:00:00+02:00 transect 0 0 41.26179967 4.933265167 EPSG:4326 222970.2874 LINESTRING (7.602633333333 41.24378333333, 2.263897 41.279816) EPSG:4326 TURTLE_CBAR_0045 2021-05-05 07:59:08 https://marineinfo.org/id/dataset/6403 Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea TURTLE_CBAR_0045 2013-04-18T05:22:00+02:00/2013-04-18T15:53:00+02:00 transect 0 0 41.30371367 4.936571167 EPSG:4326 225420.0359 LINESTRING (7.636983333333 41.32418333333, 2.236159 41.283244) EPSG:4326 TURTLE_CBAR_0045_0001 2021-05-05 07:59:08 https://marineinfo.org/id/dataset/6403 Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea TURTLE_CBAR_0045_0001 TURTLE_CBAR_0045 2013-04-18T05:55:00+02:00 sample 0 0 41.3228 7.4984 EPSG:4326 POINT EPSG:4326 TURTLE_CBAR_0045_0002 2021-05-05 07:59:08 https://marineinfo.org/id/dataset/6403 Presence of sea turtles collected through Fixed-Line-Transect monitoring across the Western Mediterranean Sea TURTLE_CBAR_0045_0002 TURTLE_CBAR_0045 2013-04-18T08:35:00+02:00 sample 0 0 41.322845 5.995345 EPSG:4326 POINT EPSG:4326 Occurrence extension: The Occurrence extension contain details regarding the sighted animals and include scientificName and the links to the World Register of Marine Species in scientificNameID. The EventID references the events as in the Event core. This table further provides information on the basisOfRecord and occurrenceStatus. EventID occurrenceID datasetID collectionCode basisOfRecord catalogNumber recordedBy occurrenceStatus scientificNameID scientificName kingdom scientificNameAuthorship TURTLE_CBAR_0043 AdL_TURTLE_CBAR_0001 https://marineinfo.org/id/dataset/6403 TURTLE_CBAR_13-17 HumanObservation AdL_TURTLE_CBAR_0001 Ilaria Campana | Miriam Paraboschi | Erica Ercoli | Erica absent urn:lsid:marinespecies.org:taxname:136999 Cheloniidae Animalia Oppel, 1811 TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 https://marineinfo.org/id/dataset/6403 TURTLE_CBAR_13-17 HumanObservation AdL_TURTLE_CBAR_0004 Antonella Arcangeli | Cristina Berardi | Lucilla Giulietti | Claudia Boccardi absent urn:lsid:marinespecies.org:taxname:136999 Cheloniidae Animalia Oppel, 1811 TURTLE_CBAR_0045_0001 AdL_TURTLE_CBAR_0005 https://marineinfo.org/id/dataset/6403 TURTLE_CBAR_13-17 HumanObservation AdL_TURTLE_CBAR_0005 Antonella Arcangeli | Cristina Berardi | Lucilla Giulietti | Claudia Boccardi present urn:lsid:marinespecies.org:taxname:137205 Caretta caretta Animalia Linnaeus, 1758 TURTLE_CBAR_0045_0002 AdL_TURTLE_CBAR_0006 https://marineinfo.org/id/dataset/6403 TURTLE_CBAR_13-17 HumanObservation AdL_TURTLE_CBAR_0006 Antonella Arcangeli | Cristina Berardi | Lucilla Giulietti | Claudia Boccardi present urn:lsid:marinespecies.org:taxname:137205 Caretta caretta Animalia Linnaeus, 1758 extendedMeasurementOrFact (eMoF) extension: The extendedMeasurementOrFact extension (eMoF) for survey or sighting data contains additional attributes and measurements recorded during the survey, such as those regarding the Research Vessel, environmental conditions, and/ or animal measurements. These attributes are linked to the Occurrence extension using the occurrenceID. The example dataset contain measurements regarding the sampling method; speed and height of the Research Vessel as platform; wind force; sighting distance; as well as the count and developmental stage of the biological entity. id occurrenceID measurementType measurementTypeID measurementValue measurementUnit measurementUnitID TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 BEAUFORT WIND FORCE http://vocab.nerc.ac.uk/collection/P01/current/WMOCWFBF 0 Beaufort scale TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 Platform height http://vocab.nerc.ac.uk/collection/P01/current/AHSLZZ01 29 Metres http://vocab.nerc.ac.uk/collection/P06/current/ULAA/ TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 Sampling method http://vocab.nerc.ac.uk/collection/Q01/current/Q0100003 visual observation from ferries TURTLE_CBAR_0045 AdL_TURTLE_CBAR_0004 Speed of measurement platform relative to ground surface {speed over ground} by unspecified GPS system http://vocab.nerc.ac.uk/collection/P01/current/APSAGP01 23.291 Knots (nautical miles per hour) http://vocab.nerc.ac.uk/collection/P06/current/UKNT/ TURTLE_CBAR_0045_0001 AdL_TURTLE_CBAR_0005 Development stage of biological entity specified elsewhere http://vocab.nerc.ac.uk/collection/P01/current/LSTAGE01 TURTLE_CBAR_0045_0001 AdL_TURTLE_CBAR_0005 Count (in assayed sample) of biological entity specified elsewhere http://vocab.nerc.ac.uk/collection/P01/current/OCOUNT01 20 Metres http://vocab.nerc.ac.uk/collection/P06/current/ULAA/ TURTLE_CBAR_0045_0001 AdL_TURTLE_CBAR_0005 Sighting distance In addition to the measurements recorded by the example dataset, other measurements are also possible depending on the scope and aims of the survey project. The example dataset Incidental sea snake and turtle bycatch records from the RV Southern Surveyor voyage SS199510, Gulf of Carpentaria, Australia (Nov 1995) for example, contain information regarding the length and weight of the biological entity as follows: extendedMeasurementOrFact (eMoF) extension: id measurementID occurrenceID measurementType measurementTypeID measurementValue measurementUnit measurementUnitID SS199510-001 SS199510-001-length SS199510-001 Length http://vocab.nerc.ac.uk/collection/P01/current/OBSINDLX 1250 Millimetres http://vocab.nerc.ac.uk/collection/P06/current/UXMM SS199510-001 SS199510-001-weight SS199510-001 Weight http://vocab.nerc.ac.uk/collection/P01/current/SPWGXX01 800 Grams http://vocab.nerc.ac.uk/collection/P06/current/UGRM SS199510-002 SS199510-002-length SS199510-002 Length http://vocab.nerc.ac.uk/collection/P01/current/OBSINDLX 1630 Millimetres http://vocab.nerc.ac.uk/collection/P06/current/UXMM SS199510-002 SS199510-002-weight SS199510-002 Weight http://vocab.nerc.ac.uk/collection/P01/current/SPWGXX01 1477.7 Grams http://vocab.nerc.ac.uk/collection/P06/current/UGRM 8.0.0.9 Microbes biomass and diversity (example coming soon)   8.0.0.10 Phytoplankton biomass and diversity This example deals with encoding phytoplankton observation data, including environmental data, into Darwin Core. Extracts from the actual data set LifeWatch observatory data: phytoplankton observations by imaging flow cytometry (FlowCam) in the Belgian Part of the North Sea, are used as an example. Phytoplankton data should adhere to guidelines by Martin-Cabrera et al., 2022: Best practices and recommendations for plankton imaging data management: Ensuring effective data flow towards European data infrastructures. Version 1.. Specific guidelines for the Occurrence and eMoF tables are documented in this publication, and we emphasize important fields in this example. Event core: The Event core contains events at the different levels and are linked together with eventID and parentEventID. In this example,the dataset contains records pointing to the origin, the in-situ sampling position as well as a record referring to the ex-situ collection of living specimens. In this case, the the event type information is provided in type. The recommended practice for providing the countryCode is to use an ISO 3166-1-alpha-2 country code. If additional information regarding licencing is provided, these can be populated under rightsHolder and accessRights. The remaining Event core fields provide location data including datasetID and datasetName, locationID, waterBody, maximumDepthInMeters, minimumDepthInMeters, decimalLongitude, decimalLatitude, coordinateUncertaintyInMeters, geodeticDatum and footprintSRS. eventID parentEventID eventRemarks eventDate modified datasetID datasetName locationID waterBody country countryCode minimumDepthInMeters maximumDepthInMeters decimalLatitude decimalLongitude geodeticDatum coordinateUncertaintyInMeters footprintSRS TripNR3242 cruise 2017-05T13:18:00+00:00/2017-05T22:14:00+00:00 2021-10-21 15:52:00 https://marineinfo.org/id/dataset/4688 LifeWatch observatory data: phytoplankton observations… North Sea Belgium BE EPSG:4326 EPSG:4326 TripNR3242TripStationNR16781 TripNR3242 stationVisit 2017-05-08T20:44:00+00:00/2017-05-08T20:55:00+00:00 2021-10-21 15:52:00 https://marineinfo.org/id/dataset/4688 JN17_5 0 30.22 51.0131 1.90562 EPSG:4326 1.11 EPSG:4326 TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781 sample 2017-05-08T20:50:00+00:00 2021-10-21 15:52:00 https://marineinfo.org/id/dataset/4688 JN17_5 0 1 51.01203 1.90217 EPSG:4326 1.11 EPSG:4326 TripNR3242TripStationNR16781MidasTripActionID105598UW TripNR3242TripStationNR16781 sample 2017-05-08T20:50:00+00:00 2021-10-21 15:52:00 https://marineinfo.org/id/dataset/4688 JN17_5 3 3 51.01203 1.90217 EPSG:4326 1.11 EPSG:4326 Occurrence extension: The Occurrence extension contains data of each occurrence with an occurrenceID and is linked to the Event core with the eventID. The Occurrence extension should provide information on the basisOfRecord and occurrenceStatus. Scientific names and links to the World Register of Marine Species should be provided under scientificName and scientificNameID, respectively. Recommended best practice is to always use the term of MachineObservation for imaging datasets derived from imaging instruments like this example, Additionally, the fields identifiedBy and identificationVerificationStatus are crucial to indicate whether the data has been validated, and by whom. While not shown in this example, identificationReferences and associatedMedia are also recommended to document the citation or algorithm software used in identification, and the persistent URL of annotated images, respectively. We know this dataset was validated because of information provided in the abstract and the eMoF table below, “identified and counted by image analysis and normalised to a unit volume of water body, validated by human”. eventID occurrenceID modified basisOfRecord occurrenceStatus scientificNameID scientificName identifiedBy identificationVerificationStatus TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_(Pseudo-)pediastrum_5 2021-10-21 MachineObservation absent urn:lsid:marinespecies.org:taxname:160560 Hydrodictyaceae Flanders Marine Institute ValidatedByHuman TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus senarius_5 2021-10-21 MachineObservation present urn:lsid:marinespecies.org:taxname:148948 Actinoptychus senarius Flanders Marine Institute ValidatedByHuman TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus splendens_5 2021-10-21 MachineObservation present urn:lsid:marinespecies.org:taxname:148949 Actinoptychus splendens Flanders Marine Institute ValidatedByHuman TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus_5 2021-10-21 MachineObservation present urn:lsid:marinespecies.org:taxname:148947 Actinoptychus Flanders Marine Institute ValidatedByHuman extendedMeasurementOrFact (eMoF) extension: The eMoF extension contains the environmental and measurement information and data of each occurrence. This extension is also linked to the Event core using the eventID, and linked to the Occurrence extension table using the occurrenceID. The various measurements are populated with measurementID, measurementType, measurementTypeID, measurementUnit, measurementUnitID, measurementValue, measurementValueID, measurementAccuracy, measurementMethod, measurementDeterminedBy and measurementDeterminedDate. In the example dataset, the LifeWatch observatory data was compiled using imaging flow cytometry (FlowCam) to observe and identify phytoplankton in the Belgian Part of the North Sea and recorded a number of measurements including abundance, lifestages, sampling device information as well as environmental measurements such as water temperature, salinity and conductivity with accompanying vocabulary. id occurrenceID measurementType measurementTypeID measurementValue measurementValueID measurementUnit measurementUnitID measurementDeterminedBy measurementMethod TripNR3242 Platform Name http://vocab.nerc.ac.uk/collection/Q01/current/Q0100001/ Simon Stevin http://vocab.nerc.ac.uk/collection/C17/current/11SS/ Flanders Marine Institute TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus_5 Abundance of biological entity specified elsewhere per unit volume of the water body http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL01/ 2.24 specimens/L http://vocab.nerc.ac.uk/collection/P06/current/UCPL Flanders Marine Institute identified and counted by image analysis and normalised to a unit volume of water body, validated by human TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus senarius_5 Abundance of biological entity specified elsewhere per unit volume of the water body http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL01/ 1.12 specimens/L http://vocab.nerc.ac.uk/collection/P06/current/UCPL Flanders Marine Institute identified and counted by image analysis and normalised to a unit volume of water body, validated by human TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_(Pseudo-)pediastrum_5 Lifestage http://vocab.nerc.ac.uk/collection/P01/current/LSTAGE01/ adult http://vocab.nerc.ac.uk/collection/S11/current/S1116/ Flanders Marine Institute identified and counted by image analysis and normalised to a unit volume of water body, validated by human TripNR3242TripStationNR16781MidasTripActionID105598 TripNR3242TripStationNR16781MidasTripActionID105598occurenceIDTA_105598_Actinoptychus senarius_5 Lifestage http://vocab.nerc.ac.uk/collection/P01/current/LSTAGE01/ adult http://vocab.nerc.ac.uk/collection/S11/current/S1116/ TripNR3242TripStationNR16781MidasTripActionID105598 Sampling device aperture diameter http://vocab.nerc.ac.uk/collection/Q01/current/Q0100012/ 0.4 meter http://vocab.nerc.ac.uk/collection/P06/current/ULAA/ Flanders Marine Institute TripNR3242TripStationNR16781MidasTripActionID105598 Sampling instrument name http://vocab.nerc.ac.uk/collection/Q01/current/Q0100002/ Planktonnet Apstein http://vocab.nerc.ac.uk/collection/L22/current/TOOL0978/ Flanders Marine Institute TripNR3242TripStationNR16781MidasTripActionID105598 Sampling net mesh size http://vocab.nerc.ac.uk/collection/Q01/current/Q0100015/ 55 micrometer http://vocab.nerc.ac.uk/collection/P06/current/UMIC/ Flanders Marine Institute TripNR3242TripStationNR16781MidasTripActionID105598UW Conductivity of the water body http://vocab.nerc.ac.uk/collection/P01/current/CNDCZZ01/ 3.916 Siemens per metre http://vocab.nerc.ac.uk/collection/P06/current/UECA Flanders Marine Institute Electrical conductivity of the water body by thermosalinograph, based on the UnderWaySystem of the ship TripNR3242TripStationNR16781MidasTripActionID105598UW Practical salinity of the water body http://vocab.nerc.ac.uk/collection/P01/current/PSALPR01/ 34.295 Grams per kilogram http://vocab.nerc.ac.uk/collection/P06/current/UGKG/ Flanders Marine Institute Practical salinity of the water body based on water from the UnderWaySystem of the ship TripNR3242TripStationNR16781MidasTripActionID105598UW Temperature of the water body http://vocab.nerc.ac.uk/collection/P01/current/TEMPPR01/ 11.881 Degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA/ Flanders Marine Institute Temperature of the water body based on water from the UnderWaySystem of the ship 8.0.0.11 Seagrass cover and composition The structure of the Event, Occurrence and extendedMeasurementOrFact extensions for Seagrass Cover & Composition is based on community feedback organised through the the Scientific Committee on Oceanic Research (SCOR): Coordinated Global Research Assessment of Seagrass System (C-GRASS). We acknowledge the work that the C-grass SCOR work group has done to develop a proposed scheme for completing Seagrass related extension files. Here encode seagrass survey data into Darwin Core according to the ENV-DATA approach and using sections of the actual data set of Seagrass Monitoring at Chengue Bay, Colombia as an example dataset. Event core: The Event core table is created by extracting all events and attributes. All events are linked together using eventID and parentEventID. eventDate is populated at the transect level with the recommended format that conforms to ISO 8601-1:2019. habitat is populated as a category or description of the habitat in which the event occurred. Additional fieldNotes can also be provided if applicable. The recommended best practice for countryCode is to use an ISO 3166-1-alpha-2 country code. The remaining Event core fields comprise of location data including maximumDepthInMeters, minimumDepthInMeters, decimalLongitude, decimalLatitude, coordinateUncertaintyInMeters, footprintWKT and footprintSRS. Addtionally in the Event core, it is recommended to further include information regarding license, rightsHolder, bibliographicCitation, institutionID, datasetID, institutionCode and datasetName. eventID parentEventID eventDate habitat fieldNotes countryCode minimumDepthInMeters maximumDepthInMeters decimalLatitude decimalLongitude coordinateUncertaintyInMeters footprintWKT footprintSRS USBsg-chengue-pastocoral 2019-05-13 seagrass no notes CO 0.8 2 11.32021806 -74.12753684 10 POLYGON ((-74.1273259763024 11.320475512862,-74.1272182449138 11.3200031521579,-74.1277187815195 11.319600402505,-74.1278115962543 11.3205666701908,-74.1274900594943 11.3206860034213,-74.1273259763024 11.320475512862)) EPSG:4326 USBsg-chengue-pastomanglar 2019-05-14 seagrass no notes CO 0.8 0.8 11.31977189 -74.12536879 10 POLYGON ((-74.1273259763024 11.320475512862,-74.1272182449138 11.3200031521579,-74.1277187815195 11.319600402505,-74.1278115962543 11.3205666701908,-74.1274900594943 11.3206860034213,-74.1273259763024 11.320475512862)) EPSG:4326 USBsg-chengue-pastocoral-SquidPopTransect1 USBsg-chengue-pastocoral 2019-05-13 seagrass no notes CO 0.8 2 11.32039927 -74.12737404 50 POINT (-74.1273740410759 11.3203992721869) EPSG:4326 USBsg-chengue-pastocoral-SquidPopTransect2 USBsg-chengue-pastocoral 2019-05-13 seagrass no notes CO 0.8 2 11.32027662 -74.1273989 50 POINT (-74.1273989021655 11.3202766241445) EPSG:4326 Occurrence extension: The Occurrence extension table contain data for each occurrence with an occurrenceID and is linked to the Event core with the eventID. This table should provide information on the basisOfRecord and occurrenceStatus. Scientific names and links to the World Register of Marine Species should be provided under scientificName and scientificNameID, respectively. If a species was identified by an expert, the field identifiedBy can be populated. If the species is well-known by another common name, this name can be provided under vernacularName. eventID occurrenceID basisOfRecord occurrenceStatus scientificNameID scientificName USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt HumanObservation present urn:lsid:marinespecies.org:taxname:374720 Thalassia testudinum USBsg-chengue-pastomanglar USBsg-chengue-manglar-tt HumanObservation present urn:lsid:marinespecies.org:taxname:374720 Thalassia testudinum USBsg-chengue-pastocoral-SquidPopTransect1 USBsg-chengue-pastocoral-fish-001 HumanObservation present urn:lsid:marinespecies.org:taxname:158815 Halichoeres bivittatus USBsg-chengue-pastocoral-SquidPopTransect1 USBsg-chengue-pastocoral-fish-002 HumanObservation present urn:lsid:marinespecies.org:taxname:158932 Lactophrys triqueter extendedMeasurementOrFact (eMoF) extension: The eMoF table contains the measurement information and data of each occurrence. This extension is also linked to the Event core using the eventID, and linked to the Occurrence table using the occurrenceID. The various measurements are populated with measurementType, measurementTypeID, measurementUnit, measurementUnitID, measurementValue, measurementValueID, measurementAccuracy, measurementMethod, measurementDeterminedBy and measurementDeterminedDate. The example dataset of Seagrass Monitoring at Chengue Bay, Colombia recorded a number of measurements and can be used as an example of how to populate the respective fields. Other possible measurementTypes not captured in this example may include percent cover, canopy height, and of course any other biotic/abiotic measurements. eventID occurrenceID measurementID measurementType measurementTypeID measurementValue measurementUnit measurementUnitID USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-PhyQ01 WaterTemp http://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/ 29.23 Degrees Celsius http://vocab.nerc.ac.uk/collection/P06/current/UPAA/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-PhyQ02 Salinity http://vocab.nerc.ac.uk/collection/P01/current/SSALSL01/ 36 Parts per thousand http://vocab.nerc.ac.uk/collection/P06/current/UPPT/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-PhyQ03 Dissolved oxygen http://vocab.nerc.ac.uk/collection/P01/current/DOXYSE02/ 6.58 Milligrams per litre http://vocab.nerc.ac.uk/collection/P06/current/UMGL/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1C1-shoot-01 Shoot Density http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL02/ 128 Number per square metre http://vocab.nerc.ac.uk/collection/P06/current/UPMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1C1-leafLenght-01 Leaf Length http://vocab.nerc.ac.uk/collection/P01/current/OBSMAXLX/ 18 Centimetres http://vocab.nerc.ac.uk/collection/P06/current/ULCM/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N1-DryBiomass Total Dry Biomass http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.32055 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N1-biomassGL Dry biomass of green leaves http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.05575 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N1-biomassNGL Dry biomass of non green leaves http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.1469 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N1-biomassSH Dry biomass of the shoots http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.07625 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N2-biomassR Dry biomass of the roots http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.0385 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N2-biomassRIZ Dry biomass of the rizome http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0.02725 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ USBsg-chengue-pastocoral USBsg-chengue-pastocoral-tt USBsg-chengue-pastocoral-T1N2-biomassOTH Dry biomass of other seagrass species http://vocab.nerc.ac.uk/collection/S06/current/S0600087/ 0 Grams per square metre http://vocab.nerc.ac.uk/collection/P06/current/UGMS/ 8.0.0.12 Zooplankton biomass and diversity Here we will encode zooplankton observation and environmental data into Darwin Core. Extracts from the actual dataset LifeWatch observatory data: zooplankton observations by imaging (ZooScan) in the Belgian Part of the North Sea, are used as an example. As with the phytoplankton example, zooplankton data should also adhere to guidelines by Martin-Cabrera et al., 2022: Best practices and recommendations for plankton imaging data management: Ensuring effective data flow towards European data infrastructures. Version 1.. These Best Practices indicate that for imaging data like this example, the fields identificationVerificationStatus and identifiedBy are crucial to know whether the data has been validated, and by whom. identificationReferences is used to document the citation or algorithm software used in identification. When possible, associatedMedia should also be populated with the persistent URL of annotated images. Event core: The Event core contains events at the different levels and are linked together with eventID and parentEventID. In this example,the dataset contains records pointing to the origin, the in-situ sampling position as well as a record referring to the ex-situ collection of living specimens. In this case, the the event type information is provided in type. The recommended practice for providing the countryCode is to use an ISO 3166-1-alpha-2 country code. If additional information regarding licencing is provided, these can be populated under rightsHolder and accessRights. The remaining Event core fields provide location data including datasetID and datasetName, locationID, waterBody, maximumDepthInMeters, minimumDepthInMeters, decimalLongitude, decimalLatitude, coordinateUncertaintyInMeters, geodeticDatum and footprintSRS. eventID parentEventID eventRemarks eventDate modified datasetID datasetName locationID waterBody country minimumDepthInMeters maximumDepthInMeters decimalLatitude decimalLongitude geodeticDatum footprintSRS TripNR2547 cruise 2013-07-22T06:58:00+00:00/2013-07-22T16:58:00+00:00 2021-06-23 14:54:00 https://marineinfo.org/id/dataset/4687 LifeWatch observatory data: zooplankton observations… Belgian Part of the North Sea Belgium EPSG:4326 EPSG:4326 TripNR2547TripStationNR9781 TripNR2547 stationVisit 2013-07-22T07:13:00+00:00/2013-07-22T07:26:00+00:00 2021-06-23 14:54:00 https://marineinfo.org/id/dataset/4687 LifeWatch observatory data: zooplankton observations… 130 0 13.4 51.27083333 2.905 EPSG:4326 EPSG:4326 TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781 sample 2013-07-22T07:22:00+00:00 2021-06-23 14:54:00 https://marineinfo.org/id/dataset/4687 LifeWatch observatory data: zooplankton observations… 130 0 0 51.2687318 2.901797 EPSG:4326 EPSG:4326 TripNR2547TripStationNR9781MidasTripActionID23024UW TripNR2547TripStationNR9781 sample 2013-07-22T07:22:00+00:00 2021-06-23 14:54:00 https://marineinfo.org/id/dataset/4687 LifeWatch observatory data: zooplankton observations… 130 3 3 51.2687318 2.901797 EPSG:4326 EPSG:4326 Occurrence extension: The Occurrence extension contains data of each occurrence with an occurrenceID and is linked to the Event core with the eventID. The Occurrence extension should provide information on the basisOfRecord and occurrenceStatus. Scientific names and links to the World Register of Marine Species should be provided under scientificName and scientificNameID, respectively. eventID occurrenceID modified basisOfRecord occurrenceStatus scientificNameID scientificName identifiedBy identificationVerificationStatus identificationReferences TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Amphipoda_sub2_130 2021-06-22 MachineObservation absent urn:lsid:marinespecies.org:taxname:1135 Amphipoda Jonas Mortelmans ValidatedByHuman RandomForest TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Annelida_sub2_130 2021-06-22 MachineObservation present urn:lsid:marinespecies.org:taxname:882 Annelida Jonas Mortelmans ValidatedByHuman RandomForest TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Anomura_sub2_130 2021-06-22 MachineObservation absent urn:lsid:marinespecies.org:taxname:106671 Anomura Jonas Mortelmans ValidatedByHuman RandomForest TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Appendicularia_sub2_130 2021-06-22 MachineObservation absent urn:lsid:marinespecies.org:taxname:146421 Appendicularia Jonas Mortelmans ValidatedByHuman RandomForest extendedMeasurementOrFact (eMoF) extension: The eMoF extension table contains the measurement information and data of each occurrence. This extension is also linked to the Event core using the eventID, and linked to the Occurrence table using the occurrenceID. The various measurements are populated with measurementType, measurementTypeID, measurementUnit, measurementUnitID, measurementValue, measurementValueID, measurementAccuracy, measurementMethod, measurementDeterminedBy and measurementDeterminedDate. The example dataset of LifeWatch observatory data: zooplankton observations by imaging (ZooScan) in the Belgian Part of the North Sea recorded some ENV-DATA and organism measurements the can be used as an example of how to populate the respective fields, including conductivity of the water body; concentration of chlorophyll-a per unit volume of the water body; sampling instrument name; sampling net mesh size; lifestage of the organism observed; and abundance of the organism observed. id occurrenceID measurementType measurementTypeID measurementValue measurementValueID measurementUnit measurementUnitID measurementDeterminedBy measurementMethod TripNR3256TripStationNR17157MidasTripActionID106326 Sampling instrument name http://vocab.nerc.ac.uk/collection/Q01/current/Q0100002/ Planktonnet WP2 <http://vocab.nerc.ac.uk/collection/L22/current/TOOL0979/ TripNR3256TripStationNR17157MidasTripActionID106326 Sampling net mesh size http://vocab.nerc.ac.uk/collection/Q01/current/Q0100015/ 200 micrometer http://vocab.nerc.ac.uk/collection/P06/current/UMIC/ TripNR3529TripStationNR19242MidasTripActionID109631UW Conductivity of the water body http://vocab.nerc.ac.uk/collection/P01/current/CNDCZZ01/ 4.05 Siemens per metre http://vocab.nerc.ac.uk/collection/P06/current/UECA/ Flanders Marine Institute Electrical conductivity of the water body by thermosalinograph, based on the UnderWaySystem of the ship TripNR3529TripStationNR19243MidasTripActionID109634 Concentration of chlorophyll-a per unit volume of the water body http://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/ 1.42 Micrograms per litre http://vocab.nerc.ac.uk/collection/P06/current/UGPL/ Flanders Marine Institute Concentration of chlorophyll-a per unit volume of the water body [particulate >GF/F phase] by filtration, acetone extraction and high performance liquid chromatography (HPLC) TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Annelida_sub2_130 Lifestage http://vocab.nerc.ac.uk/collection/P01/current/LSTAGE01/ unspecified http://vocab.nerc.ac.uk/collection/S11/current/S1152/ Flanders Marine Institute identified and counted by image analysis and normalised to a unit volume of water body, validated by human TripNR2547TripStationNR9781MidasTripActionID23024 TripNR2547TripStationNR9781MidasTripActionID23024occurenceIDTA23024_Annelida_sub2_130 Abundance of biological entity specified elsewhere per unit volume of the water body http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL01/ 0.50 specimens/m³ http://vocab.nerc.ac.uk/collection/P06/current/UPMM/ Flanders Marine Insitute identified and counted by image analysis and normalised to a unit volume of water body, validated by human "],["other_data_types.html", "Other data types 8.1 Multimedia data (Acoustic, Imaging) 8.2 Habitat data 8.3 Tracking data", " Other data types Content Multimedia data Habitat data Tracking data 8.1 Multimedia data (Acoustic, Imaging) If you have multimedia data (e.g. images, acoustic, video) that you want to publish alongside your dataset, you can do so by documenting information in the associatedMedia field in your Occurrence table. The usage of this field requires the media in question to be hosted somewhere with a persistent URL of the annotated image(s), e.g., a publication, museum database, etc. Then you simply copy this link to the associatedMedia field for a given occurrence. You may also include a concatenated list if you need to list multiple sources. While there are Core types and extensions (e.g., Audubon Core and Simple Multimedia extension) designed for image, video, and audio files, these data file types are not currently processed by OBIS. Thus for now we recommended to include links in the associatedMedia field. Stay tuned however, as OBIS is looking to incorporate the Simple Multimedia extension. Martin-Cabrera et al., 2022 have produced a best practices for datasets with plankton imaging data that can also apply to acoustic and other imaging data types. Following their guidelines, we strongly recommend including the following terms in your Occurrence table for either of these data types: basisOfRecord - recommended best practice is to always use the term of MachineObservation, especially for imaging datasets derived from imaging instruments identifiedBy - name(s) of persons involved in verifying taxon identification, particularly if automatic identification was made by a software and then validated by a human identificationVerificationStatus - categorical indicator for the extent of taxonomic identification verification. Recommended to use PredictedByMachine or ValidatedByHuman identificationReferences - references used in identification (e.g. citation and version of software or algorithm that identified taxa) The fields identifiedBy and identificationVerificationStatus are crucial to indicate whether an observation has been validated, and by whom. These fields allow users to filter data when basisOfRecord = MachineObservation, so that they can be confident in the taxonomic identification when identificationVerificationStatus = ValidatedByHuman (Martin-Cabrera et al., 2022). The identificationVerificationStatus also has implications for documenting grouped occurrences, particularly for planktonic organisms. For example, if all identifications for a specific taxon in a sample has the same identificationVerificationStatus, you ony need one occurrence record with one associated unique occurrenceID. Then, the summed count or concentration for that taxon can be reported in the eMoF as, e.g. “Abundance of biological entity specified elsewhere per unit volume of the water body”. However, if individuals of a taxon have more than one identificationVerificationStatus (e.g. ValidatedByHuman and PredictedByMachine), you will need two occurrence records with associated unique occurrenceIDs. The two records will document the same taxon with different identificationVericationStatus, and with different summed concentrations of abundance reported in the eMoF. Example Resources: Martin-Cabrera et al. (2022) have created a best practices document for plankton imaging data that you can reference. To see an example imaging dataset implementing these best practices, see the supplementary material of Establishing Plankton Imagery Dataflows Towards International Biodiversity Data Aggregators. Data originating from ROV (Remote Operating Vehicle) observations may require additional processing. Ocean Networks Canada (ONC) is developing a pipeline for publishing ROV data to OBIS. ROV datasets should have: An Event core that documents the hierarchical nature of ROV dives (e.g., ROV dives nested within a cruise) Occurrence and eMoF extensions to record taxonomic and other measurement data e.g., from sensors. ONC’s pipeline outlines the importance of including identifiedBy in order to vet taxon identifications by experts. 8.2 Habitat data Event Core is perfect for enriching OBIS with interpreted information such as biological community, biotope or habitat type (collectively referred to as ‘habitats’). However, the unconstrained nature of the terms measurementTypeID, measurementValueID, and measurementUnitID leads to a risk that habitats measurements are structured inconsistently within the Darwin Core Archive standard and as a result, are not easily discoverable, understood or usable. As a result, members of the European Marine Observation and Data Network (EMODnet) Seabed Habitats and Biology thematic groups have produced a technical report Duncan et al. (2021) that provides guidance on using the Darwin Core eMoF extension to submit habitat data to OBIS, following the ENV-DATA approach and using Seabed Habitats as a use case. Note that the guidelines and structuring approach outlined in this document has not yet been approved or accepted at the global level and is only a recommended approach as agreed upon by EMODnet Seabed Habitats, EMODnet Biology, and OBIS. The implementation at the EurOBIS level may be considered a pilot. The overarching principles are summarised here. Note that because of the numerous classification systems and priority habitat lists in existence, it is not possible to point to a single vocabulary for populating each of measurementTypeID, measurementValueID and measurementUnitID, as for other measurement types, so below are the types of information to include, with an example, as recommended by Duncan et al. (2021): measurementTypeID: A machine-readable URI or DOI reference describing the (version of the) classification system itself. For example: https://dd.eionet.europa.eu/vocabulary/biodiversity/eunishabitats/ measurementValueID: If available, a machine-readable URI describing the habitat class in “measurementValue”. For example: https://dd.eionet.europa.eu/vocabulary/biodiversity/eunishabitats/A5.36 measurementUnitID: null because habitat types are unitless. Please consult the Duncan et al. (2021) technical report (title: A standard approach to structuring classified habitat data using the Darwin Core Extended Measurement or Fact Extension; note you must refine search to Technical Reports from 2021 to identify this report) for more details, including: how to handle a single event with multiple habitat measurements recommended vocabularies and terms for common habitat classification systems example eMoF table For filling measurementType with habitat-related data and/or the dwc:habitat column, you should reference the NERC vocabulary search. While the Coastal and Marine Ecological Classification Standard (CMECS) and the Environment Ontology (ENVO) also contain habitat vocabularies, OBIS recommends the use of NERC vocabulary. If other vocabularies are used, please provide the NERC vocabulary equivalent as additional records in the eMoF table. 8.3 Tracking data Encoding Tracking data into Darwin Core follows the same standards as that of survey/sighting data. Tracking data should additionally indicate the accuracy in latitudinal and longitudinal measurements received from the positioning system, grouped by location accuracy classes, recorded in the coordinateUncertaintyInMeters field. The Ocean Tracking Network (OTN) has developed some guidelines for formatting this type of data in Darwin Core. We summarize the main points below. Using Event core for tracking data is recommended as there can be multiple events involved in tracking an organism. There are capture/tag and release events, receiver deployment events, and detection occurrences. Note that the capture and release of an organism are not considered to be distinct Occurrence records because they are not natural occurrences. Thus, in the Event core table you may record unique events for: The capture of an animal The release of an animal The deployment of a listening (or receiver) station Information pertaining to a specific individual is linked by a unique organismID. You can use eventIDs associated with a receiver to record detection occurrences in the Occurrence table. One organism may then have multiple occurrences (and thus multiple occurrenceIDs), but the same organismID. Any measurements for an organism taken during capture can be recorded in the extendedMeasurementsOrFact extension, linked to the core by the capture event’s eventID as well as the unique organismID. For more details, see the DwC guidelines for biologging. Extracts from the extendedMeasurementOrFact Extension (eMoF) of the actual dataset Ningaloo Outlook turtle tracking of Green turtles (Chelonia mydas), Western Australia (2018-present), are shown as an example tracking dataset, following ARGOS Location class codes. extendedMeasurementOrFact (eMoF) extension: id measurementID occurrenceID measurementType measurementValue measurementValueID 2347540 2347540-argosclass 2347540 ARGOS Location Class A http://vocab.nerc.ac.uk/collection/R05/current/A 2347541 2347541-argosclass 2347541 ARGOS Location Class B http://vocab.nerc.ac.uk/collection/R05/current/B 2347542 2347542-argosclass 2347542 ARGOS Location Class 2 http://vocab.nerc.ac.uk/collection/R05/current/2 2347543 2347543-argosclass 2347543 ARGOS Location Class 3 http://vocab.nerc.ac.uk/collection/R05/current/3 "],["data_qc.html", "9 Data quality control 9.1 Why are records dropped? 9.2 How to conduct Quality Control", " 9 Data quality control OBIS ignores records that do not meet a number of standards. For example, all species names need to be matched against an authoritative taxonomic register, such as the World Register of Marine Species. In addition, quality is checked against the OBIS required fields as well as against any impossible values. OBIS checks, rejects and reports the data quality back to the OBIS nodes, but never change records. The OBIS tier 2 nodes are responsible for the data quality and communicate errors back to the data providers. A number of QC tools are developed to help data providers and OBIS nodes: QC tool for species names QC tool for geography and data format For specific concerns regarding quality control checks or issues, please submit a GitHub ticket to the OBIS QC repository. 9.1 Why are records dropped? Records can be dropped and therefore not published with your dataset for a number of reasons, including: The species is not marine The ‘scientificName’ or scientificNameID did not match with WoRMS Issues with coordinates: No coordinates given decimalLatitude or decimalLongitude out of range The coordinate is zero For each dataset published, a quality report is generated where the number of dropped records and other quality issues will be flagged. Such reports can also be found when searching for data in OBIS. For example, if we searched for ‘Crustacea’ records, the following data quality report is given: Number of Crustacean records dropped We can see that >110,222 Crustacean records have been dropped, mostly due to records missing coordinates or species being flagged as non-marine. Because species are determined as being marine by WoRMS, we acknowledge that sometimes species are marked as not_marine erroneously.For specific advice on this topic, see the common QC issues page. To minimize the number of records dropped, be careful when formatting your data so that you are meeting the requirements. 9.2 How to conduct Quality Control Once you have formatted your data for OBIS, or have received a formatted dataset, it is important to run quality control checks before publishing the dataset on the IPT. The following is a list of various tools you can use to help you perform quality checks on your data: R package obistools EMODnet Biocheck Web UI built on obistools. This tool requires your dataset to be published on an IPT (e.g., a test IPT such as https://ipt.gbif.org/ where your dataset will not be harvested by GBIF or OBIS). Note you are required to have a login to access an IPT R package Lifewatch data services The US Integrated Ocean Observing System Standardizing Marine Bio Data Guide WoRMS taxon match tool Other WoRMS web services, incl. taxon match Excel Conditional Formatting tool Excel > Home > Conditional Formating > Highlight cells Rules > Duplicate values… GBIF data validator Python library for OBIS QC developed by Canadian Integrated Ocean Observing System R package and function Hmisc:: describe Can give important summary statistics and identify numbers that don’t match Conducting QC with obistools Installing obistools requires the devtools package. Use the following code to install both packages: install.packages("devtools") devtools::install_github("iobis/obistools") If you have difficulty installing obistools, please try updating your R packages, in particular the vctrs package. This can be done in RStudio in the Packages tab (“update” button) or by using the update.packages() command (you can choose which packages to update). If you cannot install obistools please reach out to helpdesk@obis.org and we will help you. To use obistools to conduct quality control, you can follow the general order below. Please see the obistools GitHub for examples of how to use the functions. Check that the taxa match with WoRMS obistools::match_taxa Check that all required fields are present in the occurrence table obistools::check_fields Check coordinates Plot them on a map to identify any points that appear outside the scope of the dataset obistools::plot_map. Using obistools:plot_map_leaflet() will additionally allow you to identify the row number for a particular data point. Check that points are not on land obistools::check_onland Ensure depth ranges are valid obistools::check_depth Check that the identifiers are present, unique, and appropriately correspond with each other obistools::check_eventids. You should also check the uniqueness of the occurrenceID field (e.g. using Hmisc::describe or simple code like length(occur$occurrenceID) == length(unique(occur$occurrenceID))) Ensure all eventIDs in extensions have matching eventIDs in the core table obistools::check_extension_eventids Check that eventDate is formatted properly obistools::check_eventdate Check for statistical outliers or other anomalies with Hmisc (below) QC with R package Hmisc The R package Hmisc has the function describe which can help you identify any discrepancies or outliers in your dataset. It will summarize each of your variables for a given data field. This can help you quickly identify any missing data and ensure the number of unique IDs is correct. For example, in an Occurrence table with 1000 records, there should be 1000 unique occurrenceIDs. library(Hmisc) library(Hmisc) data<-read.csv("example_data_occur.csv") describe(data) 12 Variables 407 Observations ------------------------------------------------------------------------------------------------------------------ CollectionCode n missing distinct value 407 0 1 BIOFUN1 Value BIOFUN1 Frequency 407 Proportion 1 ------------------------------------------------------------------------------------------------------------------ eventID n missing distinct 407 0 27 lowest : BIOFUN1_BF1A01 BIOFUN1_BF1A02 BIOFUN1_BF1A03 BIOFUN1_BF1A04 BIOFUN1_BF1A05 highest: BIOFUN1_BF1M3 BIOFUN1_BF1M4 BIOFUN1_BF1M6 BIOFUN1_BF1M8 BIOFUN1_BF1M9 ------------------------------------------------------------------------------------------------------------------ occurrenceID n missing distinct 407 0 407 lowest : CSIC_BIOFUN1_1 CSIC_BIOFUN1_10 CSIC_BIOFUN1_100 CSIC_BIOFUN1_101 CSIC_BIOFUN1_102 highest: CSIC_BIOFUN1_95 CSIC_BIOFUN1_96 CSIC_BIOFUN1_97 CSIC_BIOFUN1_98 CSIC_BIOFUN1_99 This video shows how to use both obistools and Hmisc to conduct QC checks in R. "],["dataquality.html", "9.3 Data quality flags", " 9.3 Data quality flags As you are following the guidelines in this manual to format data, it is important to consider the potential quality flags that could be produced when your dataset is published to OBIS. OBIS performs a number of automatic quality checks on the data it receives. This informs data users of any potential issues with a dataset they may be interested in. A complete list of flags can be found here but broadly speaking potential flags relate to issues with: Location - coordinates Event time - start, end dates Depth values out of range Taxonomic WoRMS name matching Non-marine or terrestrial When you are filling these fields it is important to double check that correct coordinates are entered, eventDates are accurate and in the correct format, and depth values are in meters. Records may be rejected and not published with your dataset if the quality does not meet certain expectations. In other cases quality flags are attached to individual occurrence records. We acknowledge that sometimes you may encounter a QC flag for data that is accurate. For example, you may document a depth value that gets flagged as DEPTH_OUT_OF_RANGE. Sometimes this occurs because your measured depth value is more accurate than the GEBCO bathymetry data which OBIS bases its depth data on. In these cases, you can ignore the flag, but we recommend adding a note in eventRemarks, measurementRemarks,or occurrenceRemarks. The checks we perform as well as the associated flags are documented here. QC Flags in downloaded data There are several ways to inspect the quality flags associated with a specific dataset or any other subset of data. Data downloaded through the mapper and the R package will include a column named flags which contains a comma separated list of flags for each record. In addition, the data quality panel on the dataset and node pages has a flag icon which can be clicked to get an overview of all flags and the number of records affected. This table includes quality flags, but also annotations from the WoRMS annotated names list. When OBIS receives a scientific name which cannot be matched with WoRMS automatically, it is sent to the WoRMS team. The WoRMS team will then annotate the name to indicate if and how the name can be fixed. Documentations about these annotations will be added here soon. Clicking any of these flags will take you to a table showing the affected records. For example, this is a list of records from a single dataset which have the no_match flag, indicating that no LSID or an invalid LSID was provided, and the name could not be matched with WoRMS. The column originalScientificName contains the problematic names, as scientificName is used for the matched name. At the top of the page there’s a button to open the occurrence records in the mapper where they can be downloaded as CSV. The occurrence table also has the flags column, so when inspecting non matching names for example it’s easy to check if the names at hand have any WoRMS annotations: Inspecting QC flags with R Inspecting flags using R is also very easy. The example below fetches the data from a single dataset, and lists the flags and the number of records affected. Notice that the occurrence() call has dropped = TRUE to make sure that any dropped records are included in the results: library(robis) library(tidyr) library(dplyr) # fetch all records for a dataset df <- occurrence(datasetid = "f3d7798e-7bf2-4b85-8ed4-18f2c1849d7d", dropped = TRUE) # unnest flags df_long <- df %>% mutate(flags = strsplit(flags, ",")) %>% unnest(flags) # get frequency per flag data.frame(table(df_long$flags)) Var1 Freq 1 depth_exceeds_bath 78 2 no_accepted_name 17 3 no_depth 5 4 no_match 138 5 not_marine 2 6 on_land 1 7 worms_annotation_await_editor 5 8 worms_annotation_reject_ambiguous 2 9 worms_annotation_reject_habitat 2 10 worms_annotation_todo 9 11 worms_annotation_unresolvable 7 This second example creates a list of annotated names for a dataset: library(robis) library(dplyr) library(stringr) # fetch all records for a dataset df <- occurrence(datasetid = "f3d7798e-7bf2-4b85-8ed4-18f2c1849d7d", dropped = TRUE) # only keep WoRMS annotations and summarize df %>% select(originalScientificName, flags) %>% mutate(flags = strsplit(flags, ",")) %>% unnest(flags) %>% filter(str_detect(flags, "worms")) %>% group_by(originalScientificName, flags) %>% summarize(records = n()) originalScientificName flags records <chr> <chr> <int> 1 Alcyonidium fruticosa worms_annotation_reject_habitat 1 2 Apicularia (Thapsiella) rudis sp. worms_annotation_unresolvable 1 3 Arcoscalpellum vegae worms_annotation_unresolvable 1 4 Balanus evermanni worms_annotation_await_editor 1 5 Chloramidae worms_annotation_reject_ambiguous 2 6 Cleippides quadridentatus worms_annotation_todo 1 7 Enhydrosoma hoplacantha worms_annotation_reject_habitat 1 8 Hippomedon setosa worms_annotation_unresolvable 1 9 Leionucula tenuis worms_annotation_await_editor 1 10 Ophiocten borealis worms_annotation_todo 1 11 Ophiopholis gracilis worms_annotation_todo 1 12 Priapulus australis worms_annotation_await_editor 1 13 Primnoella residaeformis worms_annotation_unresolvable 1 14 Robulus orbigny worms_annotation_unresolvable 1 15 Tetraxonia worms_annotation_unresolvable 2 16 Tmetonyx barentsi worms_annotation_await_editor 2 17 Triaxonida worms_annotation_todo 6 "],["mofreports.html", "9.4 How To Use MoF Report and Tool", " 9.4 How To Use MoF Report and Tool A MEASUREMENT TYPES dataset report has been added regarding currently used measurementType and associated measurementTypeID(s), located near the bottom of the individual dataset pages (if measurementType in use for the dataset). This new dataset report was derived from this MoF statistics report https://r.obis.org/mof/ and this active filtering MoF tool https://mof.obis.org/. Important note: the mof filtering tool should NOT be used as a resource for mapping vocabularies to measurements and parameters! It is a tool designed to assist node managers in finding any incorrect mappings within their datasets. For assistance with mapping vocabularies, see the vocabulary section of this Manual. To more easily locate the datasets within your node that may have possible measurementTypeID issues, use the MoF Statistics page: https://r.obis.org/mof/. This contains the list of Nodes currently using measurementType/measurementValue/measurementUnit with counts and percentage missing for the associated ID(s). If there is a node in that list that you are interested in locating, searching for, and possibly fixing MoF issues, select the Node from the list, then select a dataset (displaying a high percentage of missing ID(s)), and scroll down to the MEASUREMENT TYPE report Example, selected OBIS USA, then selected Florida Keys Reef Visual Census 1994, and scrolled down to MEASUREMENT TYPES section: To locate other datasets using these MEASUREMENT TYPES, use this active filtering MoF tool https://mof.obis.org, sort by measurementType (click column header) and scroll to measurementType(s) of interest For MEASUREMENT TYPE “Number of species observed during time period” has only one entry, which is missing associated ID. To see which datasets are using the listed measurementType, measurementTypeID combination, click on the number of records which is the last column. All are from OBIS USA. For MEASUREMENT TYPE “fish length” … To see which datasets are using this also listed measurementType, measurementTypeID combination, click on the number of records which is the last column. There are two records for fish length, one missing an ID and the other using S06, which may not be the preferred ID for this measurementType: Also, while scrolling through this report, you may notice something you would like to further research, click the record count value to see a list of datasets and associated node(s) using this noted type/ID. NOTE: Current USE does not indicate CORRECT use: To see BODC label for the provided ID, click the Find button, second last column: This is showing a different label from the (variety of) measurementType provided. To see which datasets are using a specific measurementType / ID combination, click the records count, last column: Things you are looking to clean up: If measurementTypeID is empty this should be updated. If the same measurementType (with same meaning/purpose) is using multiple measurementTypeIDs, these should be fixed to a single, preferred BODC vocab value. "],["lifewatch_qc.html", "9.5 Geographic and data format quality control", " 9.5 Geographic and data format quality control These Data validation and QC services are available on the LifeWatch portal at http://www.lifewatch.be/data-services. 9.5.0.1 Geographical service This service allows to upload a file and to plot the listed coordinates on a map. Using this web service does not require knowledge of GIS. This service allows a visual check of the available locations and makes it possible to easily identify points on land or outside the scope or study area. Geographic data are essential for OBIS and the experience is that a lot of these data is incomplete or contains errors. A visual check of the position of the sampling locations is thus a simple way of filtering out obvious errors and improving the data quality. Latitude and longitude need to be in WGS84, decimal degrees. This format is also necessary for the OBIS Schema and for uploading the dataset to IPT (Darwin Core). 9.5.0.2 OBIS data format validation This is the most extensive check currently available and is available for data that are structured according to the OBIS Schema. This validation service checks the following items: Are all mandatory fields completed, what are the missing fields? Are the coordinates in the correct format (decimal degrees, taking into account the minimum and maximum possible values)? Are the sampling points on land or in water? Is the information in the date-fields valid (e.g. month between 1-12)? Can the taxon name be matched with WoRMS? This tool undertakes several actions simultaneously. In a first step, this data service allows you to map your own column headers to the field names used in the OBIS Schema. When you then run the format validation service, the following actions are performed: A check of the mandatory fields of the OBIS Scheme. If mandatory fields would be missing, these will be listed separately, so you can complete them. Without these fields, the dataset cannot be accepted by the OBIS node. A listing of all the optional fields of the OBIS Scheme that are available in your file. Validation of the content of a number of fields: Latitude & longitude: Are the values inside the world limit? (yes/no); Are the values different from zero? (yes/no); Are the values situated in the marine environment (sea/ocean) (=prerequisite of a marine dataset)? (yes/no) Date-related fields: Do the year-month-day fields form a valid date? (yes/no) Do the start- and end-date fields form a valid date? (yes/no) Scientific name: Is the scientific name available in WoRMS? (yes/no) When yes: Indication whether taxon is marine or not Indication whether taxon name is valid or not Indication of the taxonomic rank After matching with WoRMS, the report gives a brief overview containing: the number of exact matches the number of fuzzy (=non-exact) matches the number of non-matches the number of errors that might have occurred during matching For each of the above steps, the result report lists the number of records that passes the check. The tool also makes a ‘grand total’ of these results, indicating if the quality of record is sufficient to be imported into OBIS, taking into account the results of the above mentioned checks. If the file contains fields that do not match the OBIS schema, these are also listed. Fields that cannot be mapped to the OBIS schema will not be uploaded in OBIS. After this data format check, a number of columns are added to the originally uploaded file, where the results of each step are listed. Each check is basically a yes/no question, which is translated to a 1 (yes) or 0 (no) value in the results file and is thus easy to interpret. "],["common_qc.html", "9.6 Common Quality Control issues", " 9.6 Common Quality Control issues Content: Uncertain temporal range Uncertain geolocation How to use OBIS Maptool How to use Gazetteers to obtain geolocation information Low confidence taxonomic information Uncertain measurements 9.6.1 Uncertain temporal range When the eventDate or temporal scope of your dataset is in question or provided in an invalid format (e.g., textual description), there are a number of options to ensure the most accurate date is provided. You may provide a range of dates in the ISO 8601 format if the range of dates is certain. Do not include a date range if you are making assumptions. Notes about any assumptions or interpretations on date ranges can be documented in the eventRemarks field. Be careful when entering date ranges. For example, entering 1870/1875-08-04 is equivalent to any date between 1870 and 1875-08-04. Date ranges can be used in this way to capture some level of uncertainty in when an event occurred. If only parts of the date are known (e.g., year but not month and day), you may provide the date in ISO 8601 format while excluding the unknown elements. Do not use zero to populate incomplete dates, simply end the date with the known information (e.g., 2011-03 instead of 2011-03-00). Additionally, if the year is unknown, you should only populate the month and day fields because eventDate cannot be formatted to exclude year. In these cases, eventDate is not necessary to fill. If date was provided as a textual description that is accurately interpretable, include the text description in the verbatimEventDate field. Then provide the interpreted date in ISO 8601 format in the eventDate field. Be sure to document any other important information in ‘eventRemarks’. For historical dates that do not conform to the ISO 8601 format, guidelines have been developed by the OBIS Historical Data Project team. The dwc:class:GeologicalContext can be used to capture some information for records pertaining to fossilized specimens, and the Chronometric Age Extension can also be used. Note that currently when publishing, the Chronometric Age extension will not be aggregated with OBIS data, but this extension will be available when an individual dataset is downloaded. This applies to any other extensions not currently implemented. 9.6.2 Uncertain geolocation Sometimes locality information can be difficult to interpret, especially if records originate from historical data with vague descriptions, or descriptions/names of areas that no longer exist. If your dataset is missing decimalLongitude and decimalLatitude, but the locality name is given, there are a number of approaches you can take. You can: Use the OBIS Map Tool to obtain a WKT string for point, line, or polygon features to put in the footprintWKT field. The corresponding projection should be placed in the footprintSRS field. Note the accepted spatial reference system for OBIS is EPSG:4326 (WGS84). The Marine Regions Gazetteer is available for use within the Map tool to help find locations. Search for locations with the Marine Regions Gazetteer to obtain coordinates and a locationID. For information on how to use this tool, see below. You can also use the Getty Thesaurus of Geographic Names or Google Maps. See below Always be sure to fill in the georeferenceSources field to indicate the source(s) you used to obtain locality information when appropriate If you have a set of points, a line, or a polygon (perhaps from the Map tool), you can find the centroid of the features using either obistools::calculate_centroid or PostGIS, and then enter this coordinate into the decimalLatitude and decimalLongitude fields. This PostGIS guideline will help you select a centroid that is guaranteed to fall within your designated area. Estimate coordinateUncertaintyinMeters that is wide enough to cover the area If the location is provided as an array or WKT format, you can use R package obistools::calculate_centroid to obtain coordinate uncertainty. Use the OBIS maptool to obtain from the “radius” column. This is only applicable for lines or polygons, not point features. For data that only has textual descriptions: Try GBIF’s GEOLocate Web Application. You can use this tool for one location at a time with the Standard Client, or upload a CSV file for batch processing. This tool lets you enter text descriptions in the “Locality String” field, and other relevant locality information (e.g. country, state, county) to obtain geographic coordinates. Use this Biodiversity Enhanced Location Services tool developed by VertNet. It can translate textual descriptions and provide decimalLatitude, decimalLongitude, geodeticDatum, and coordinateUncertaintyInMeters as a csv sent to an email address. For more information on this service, see the associated GitHub. GBIF also provides some guidelines for difficult localities as well as other georeferencing tips for different geographic features, such as when only a distance or heading is provided (e.g., 10 km off Sao Paulo’s coast, north of Fiji). Important note: If you are making any inferences and/or decisions about locality coordinates, please record this in the georeferenceRemarks field. Additional information about the locality can also be stored in DwC terms such as waterBody, islandGroup, island and country. locationAccordingTo should provide the name of the gazetteer that was used to obtain the coordinates for the locality. 9.6.3 How to use OBIS Map Tool A video tutorial on how to use our Map tool is available below. This video covers the following topics: Estimating coordinates Using the line and polygon tool Obtaining and exporting WKT strings Well-Known Text (WKT) strings are representations of the shape of the location and can be provided in the footprintWKT field. This is particularly useful for tracks, transects, tows, trawls, habitat extent, or when an exact location is not known. WKT strings can be created using the Map tool’s WKT function. The Map tool also calculates a midpoint and a radius for line or polygon features, which can then be added to decimalLongitude, decimalLatitude, and coordinateUncertaintyInMeters, respectively. As mentioned above, the obistools::calculate_centroid function can be used to calculate the centroid and radius for WKT polygons. This wktmap tool can also be used to visualize and share WKT strings. 9.6.4 Using Getty Thesaurus & Google Maps to obtain locality coordinates For both the Getty thesaurus and Google Maps you can simply search the name of a locality, for example the Cook Strait in New Zealand. The search result on the Getty thesaurus will bring you to a page where you can obtain decimalLatitude and decimalLongitdue. Screenshot of Cook Strait page on the Getty Thersaurus For Google Maps, the coordinates can be found in the url after searching. Screenshot of Google Maps showing where coordinates can be found in the URL 9.6.5 How to use Marine Regions Gazetteer tool Marine Regions offers a marine gazetteer search engine to obtain geographic information and unique identifiers for marine regions. Once you have navigated to the gazetteer search engine, you have two options to search by: enter the name of the desired locality, or enter an MRGID code. Most likely you will have a locality name but not an MRGID. You may also select a placetype to search instead for types of regions that may be physical (e.g., seamount, bay, fjord, etc.) or administrative (e.g., exclusive economic zones, countries, etc). You can specify specific sources if known (e.g., published paper, organization, etc.). Finally, you can give a latitude/longitude coordinate with a radius around it to obtain a list of regions near that point. For this example we will search by geographic name for the Bay of Fundy. Our search returned 5 results from different sources (indicated in brackets). So how do we select the correct one? We can notice right away that the second result, from SeaVox SeaArea, has a preferred alternative, which when you click on the link brings you to the IHO Sea Area description for Bay of Fundy. So already we can likely drop SeaVox as a potential candidate. A good next step may be to compare the geographical extent for each to ensure it covers the desired area. If you are uncertain about exactly where your locality is, it may be better to be safe and choose a wider geographic region. Let’s compare the maps for all 5 results: Notice that no region has the exact same geographic extent. Let’s select the IHO Bay of Fundy locality (the first search result) to ensure we are covering the entire area of the Bay of Fundy, but not the Gulf. Inspecting the rest of the page, there is a lot of other useful information we can use. We can populate the following OBIS fields for our dataset, copying the information outlined in the red boxes: locationID from MRGID: http://marineregions.org/mrgid/4289 decimalLatitude and decimalLongitude latitude and longitude coordinates of the location’s midpoint in decimal degrees: 44.97985204, -65.80601556 coordinateUncertaintyInMeters precision: 196726 meters Screenshot of Marine Region placedetails, highlighting important information for OBIS Since we are obtaining all this locality data from Marine Regions, we must also populate the locationAccordingTo field. Here, we will provide the name of the gazetteer we used to obtain the coordinates for the locality - in this case you would write “Marine Regions”. In georeferenceRemarks we must document that the coordinates are the region’s midpoint, that locality information was inferred by geographic name, and, where applicable, place the original locality name in the field verbatimLocality. Finally, the location portion of our dataset would look something like this: locality locationID decimalLatitude decimalLongitude coordinateUncertaintyInMeters locationAccordingTo georeferenceRemarks Bay of Fundy http://marineregions.org/mrgid/4289 44.97985204 -65.80601556 196726 Marine Regions Coordinates are a midpoint inferred from location name The OBIS Mapper has built-in access to the Marine Regions Gazetteer. The video below demonstrates how to use this built-in tool, as well as how to navigate the Marine Regions Gazetter to obtain important georeferencing information to include in your data. 9.6.5.1 DwC Terms obtained from Maptool and Gazetteers Below is a table summarizing the different DwC terms you can obtain from the OBIS Maptool or from the Gazeteers discussed above. DarwinCore Term Maptool Term Marine Regions Term Notes decimalLatitude Latitude Latitude decimalLongitude Longitude Longitude locationID MRGID coordinateUncertaintyInMeters radius precision (not always available) footprintWKT WKT 9.6.6 Low confidence taxonomic identification In case of low confidence taxonomic identifications, and/or the scientific name contains qualifiers such as cf., ?, or aff., then you should: Put the name of the lowest possible taxon rank that can be determined with high-confidence in scientificName (e.g. the genus) Put any text regarding identification with low confidence and/or qualifiers in identificationQualifier (e.g., cf., aff.) Put the species name in specificEpithet Place the rank of the taxon documented in scientificName (e.g., genus) in taxonRank Document any relevant comments in taxonRemarks or identificationRemarks (e.g. reasoning for identification) Take an example specimen named Pterois cf. volitans. The associated occurrence record would have the following taxonomic information: scientificName = Pterois identificationQualifier = cf. volitans specificEpithet = leave blank scientificNameID = the one for Pterois taxonRank = genus If the provided name is unaccepted in WoRMS, it is okay to use the unaccepted name in this field. scientificNameID should contain the WoRMS LSID for the genus. There is a new Darwin Core term verbatimIdentification meant for containing the originally documented name, however this term is not yet implemented in OBIS so if you populate this field it will not be indexed alongside your data. However you can use identificationRemarks to add extra information. The use and definitions for additional Open Nomenclature (ON) signs (identificationQualifier) can be found in Open Nomenclature in the biodiversity era, which provides examples for using the main Open Nomenclature qualifiers associated with physical specimens (Figure 1). Whereas the publication Recommendations for the Standardisation of Open Taxonomic Nomenclature for Image-Based Identifications provides examples and definitions for identificationQualifiers for image-based non-physical specimens (Figure 2). Figure 1. Flow diagram with the main Open Nomenclature qualifiers associated with physical specimens. The degree of confidence in the correct identifier increases from the top down. More info and figure copied from Open Nomenclature in the biodiversity era. Figure 2: Flow diagram with the main Open Nomenclature qualifiers for the identification of specimens from images (non-physical, image-based) . More information and figure copied from Recommendations for the Standardisation of Open Taxonomic Nomenclature for Image-Based Identifications If the occurrence is instead unknown or new to science, it should be documented according to recommendations by Horton et al. 2021. Populate the scientificName field with the genus, and in identificationQualifer provide the ON sign ‘sp.’. Be sure to also indicate the reason why species-level identification is unavailable by supplementing ‘sp.’ with either stet. (stetit) or indet. (indeterminabilis). If neither of these are applicable, (e.g. for undescribed new species), add a unique taxon identifier code after ‘sp.’ to identificationQualifer. For example Eurythenes sp. DISCOLL.PAP.JC165.674. When adding a taxon identifier code, please avoid simple alphanumeric codes (i.e. Eurythenes sp. 1, Eurythenes sp. A). Like creating eventIDs or occurrenceIDs, try to provide more complex and globally unique identifiers. Identifiers could be constructed by combining higher taxonomic information with information related to a collection, institution, museum or collection code, sample number or museum accession number, expedition, dive number, or timestamp. This ensures namestrings will remain unique within OBIS. We also recommend including these temporary names on specimen labels for physical specimens. 9.6.6.1 Changes in taxonomic classification Taxonomic classification can change over time - so what does that mean for your datasets when records change classification? Because OBIS relies on WoRMS as the taxonomic backbone, changes in taxonomic classification will be updated between a day to a few weeks from the date of change, unless triggered manually. This means that the WoRMS LSID associated with a species in question will be used to automatically populate the taxonomic classification with the updated information. However we recognize that there may be issues when larger changes occur (e.g. Family level splits). Then, records that are only identified to the Family level may not get updated properly. For example, there was a shift in coral taxonomy where the family Nephtheidae was split into Capnellidae and Alyconiidae for species occurring in the Northern Atlantic. While species that were identified as belonging to Nephtheidae have now been updated to belong to one of those two families, records that were only identified down to family (i.e., Nephtheidae) are still documented as Nephtheidae. Unfortunately there is currently no solution for dealing with splits like this, aside from contacting data providers and asking them to change the taxonomy. A future solution may be an annotation registry that includes statements like “identifications of taxon x in geographic area y are misidentified and should be linked to taxon z”. 9.6.7 Uncertain measurements 9.6.7.1 individualCount In some cases individualCount may be uncertain due to only fragments of organisms being found, or only a range of individuals is known. The occurrenceRemarks can be used to document information about decisions made during data formatting. In cases where only certain body parts (e.g., head, tail, arm) are recorded, you can incorporate this information in measurementTypeID by finding a P01 code that contains elements from the S12 collection, which documents sub-components of biological entities. If a P01 code for your sub-component does not exist, you can request the creation of a P01 code. If you are uncertain whether fragments are from the same individual, a range can be provided and the uncertainty can be recorded in occurrenceRemarks. When providing a range to estimate the count, there are two suggested approaches: Use MOF/eMoF: place the data in the (extended)MeasurementOrFact extension. This requires an additional two rows per Occurrence. One with the equivalent of a minimumIndividualCount and one with the equivalent of maximumIndividualCount. Place data into dynamicProperties: Include the information in the Occurrence record itself, with no extension, and instead document it in dynamicProperties, with a value such as {“minimumIndividualCount”:0, “maximumIndividualCount”:5} We note that documenting the data in ‘dynamicProperties’ means the information will not be machine readable and may be more difficult for users to extract. Abundance vs Count data: A brief clarification on abundance and count data: abundance is the number of individuals within an area or volume. This type of data is recorded in organismQuantity, where organismQuantityType should be used to specify the type of quantity (e.g., individuals, percent biomass, Braun Blanquet Scale, etc.). However, if the value is just the number of individuals without reference to a space, this information is recorded in individualCount. 9.6.8 Non-marine species If you are given an error that your taxon is not marine, please confirm first whether the species is actually freshwater by cross referencing with WoRMS or IRMNG. If the species is not marine (i.e. belongs to a non-marine genus), check with the data provider as necessary for possible misidentification. Finally you can contact WoRMS at info@marinespecies.org to discuss adding the taxon to the WoRMS register. You will be required to provide documentation in this case to confirm marine status of the taxon. Otherwise, records marked as non-marine will be dropped from the published dataset, and this will be flagged in the data quality associated with your dataset. Let’s consider an example within this dataset on benthic macroalgae. Inspecting the data quality report we can see there are three dropped records due to species not being marine. Dropped records from a benthic macroalgae dataset Clicking on the dropped records we can see which three species were dropped. By scrolling to the right of the table, we can see these records have two quality flags: NO_DEPTH and NOT_MARINE. Flags specifying why certain records were dropped Let’s take a look at the first species, Pseudochantransia venezuelensis. When we search for this species on WoRMS we can see that the species is marked as freshwater. Cross-referencing with IRMNG, if we search for the genus-species, the species is not even found, an indication that it is not in the database (and also why it can be good to check multiple sources). Searching for just the genus, we can see that marine and brackish are stricken out, indicating the species is not marine. If you have species that are marked as non-marine in these registers but are either supposed to be marine, or were found in a marine environment, then you should contact WoRMS to discuss adding it to the register. For additions and/or edits to environmental or distribution records of a species, contact the WoRMS Data Management Team at info@marinespecies.org with your request along with your record or publication substantiating the addition/change. "],["data_publication.html", "10 Data publication and sharing 10.1 Licenses", " 10 Data publication and sharing Once you have finished formatting your data and have conducted some basic quality control procedures on it, you are ready to publish your dataset to OBIS. Note that OBIS nodes can accept any data files from its data sources or data providers, and they publish these data on their respective OBIS node Integrated Publishing Toolkit (IPT). Data from node IPTs are then harvested by central OBIS to become accessible via the global database. The Integrated Publishing Toolkit (IPT) is developed and maintained by the Global Biodiversity Information Facility (GBIF). While GBIF maintains an IPT manual, we outline specific OBIS instructions here. Some nodes require the manager themself to upload data. This means after you have formatted your data for OBIS and have done some basic quality control steps, you simply pass this file on to the appropriate node manager. They will communicate with you any issues during the publication process. Sometimes a node manager will request the data provider to upload their own dataset to the IPT. If you the data provider are required to upload your data then there are a number of steps you will take to upload your data. A reminder that all data formatting and quality control steps should be completed before uploading your dataset. Once uploaded, the node manager will check your upload before publishing the dataset. There are a few steps involved to publish a dataset, whether you are the provider or the node manager. You must: Identify the correct IPT for your OBIS node (you may have to contact your node manager to confirm IPT) Login to the IPT, or have the node manager create an account for you if you do not have one, so you can upload your dataset(s) Map each of your fields to Darwin Core terms. This should be relatively straight forward if you have done this already during data formatting Fill all relevant metadata to help users understand and cite your dataset Publish and make your data public Details for each of these steps are outlined on the subsequent pages. We will start with an overview of which Creative Commons license should be used, and then move on to using the IPT to publish data. 10.1 Licenses OBIS nodes must make the necessary agreements with the original data providers so that data can be made available to OBIS under one of the following Creative Commons licenses (in order of preference): CC-0 - data may be used without restrictions CC-BY - data are available for any use if proper attribution and credit is given CC-BY-NC - data may be used for any non-commercial use as long as proper attribution/credit is given You may need to consult with your organization if there are any copyright concerns. For more information on the different Creative commons license types see About the licenses. "],["ipt.html", "10.2 IPT: Integrated Publishing Toolkit", " 10.2 IPT: Integrated Publishing Toolkit Contents: Introduction How to access the IPT Who populates IPTs? Upload data Map to Darwin Core Add metadata Publish on the IPT Publish your data as a dataset paper Download data from the IPT Introduction to the IPT Before we get into the details for accessing and using the IPT, let’s understand what it is. Biodiversity datasets and their metadata are published in OBIS using the Integrated Publishing Toolkit (IPT), developed by GBIF. The IPT is an open source web application that can be customized by the OBIS node manager (see IPT admin page for details). An IPT-instance is used to publish and register all datasets. To be able to create and manage your own dataset (called a “resource” by GBIF), you will need a user account. In general, the IPT software assists users in mapping data to valid Darwin Core terms, as well as archiving and compressing the Darwin Core content with: A descriptor file: meta.xml that maps the core and extensions files to Darwin Core terms, and describes how the core and extensions files are linked The eml.xml file, which contains the dataset metadata in Ecological Metadata Language (EML) format. For instructions on how to enter the metadata go to EML. All these components (i.e., core file, extension files, descriptor file, and metadata file) become compressed together (as a .zip file) and comprise the Darwin Core Archive. Example showing how Occurrence core, EML, and meta.xml files make up a Darwin Core-Archive file How to access the IPT Once you have determined which OBIS node IPT is suited for your dataset, you can contact your node manager to create an associated account for you. There will be a link on the sign in page that will direct you to the IPT’s administrator to contact them. If your node’s IPT is not listed here, you will have to contact the node manager to get the link to their IPT. Screenshot of IPT login page, highlighting link to IPT admin and the login button If you are an IPT admin and want to know how to set up an IPT yourself, see the IPT admin page. 10.2.0.1 Who populates the IPT with datasets? With regard to populating the IPT with marine data for OBIS, there are two possible approaches: Manager driven: You as node manager take the responsibility of describing, checking and uploading the data and metadata to the IPT. The data provider can send you the data ‘as such’ or you can make agreements with your providers on the accepted OBIS data format and standards. This approach will give you a very good knowledge of what data is available. It can be time-consuming, as (extended) communication with the data provider will be necessary to document the metadata and to re-format the data to the OBIS standards. User driven: You as node manager can guide (some of your) data providers to publish the data and metadata to the IPT themselves. Your main task will be to make sure that all relevant information and data for OBIS is available and that you perform the necessary quality checks before the data are released to OBIS. Once the Darwin Core Archive is created, the data provider should inform the node manager of this action, so he or she can do the necessary quality control checks. In order for the node manager to be able to look at the dataset, the data provider should add him or her as a “resource manager” to this specific dataset. In most cases, there will be a combination of these two approaches. The chosen approach will largely depend on the capacity, availability and willingness of your data provider to invest extra time in formatting and thoroughly describing their data. If you – as node manager – would prefer a partly user driven approach, the following steps to publishing marine data to OBIS briefly explains how you or a data provider can upload, standardize and publish a dataset on the OBIS node IPT, without the hassle of installing and maintaining an IPT instance. The data are published in your organization’s name. This guide is based on the Canadensys 7-step guide to publishing marine data: Desmet, P. & C. Sinou. 2012. 7-step guide to data publication. Canadensys. http://community.canadensys.net/publication/data-publication-guide. Caution: Make sure you have obtained the rights from the data owners to publish their data! Create your resource on the IPT Once you have your account, login at the top of the IPT page. Click on the tab Manage resources: it will display all the datasets you are managing and will be empty at first. You can create a new resource at the bottom of the page. Follow the GBIF IPT manual for more detailed instructions. Screenshot of the manage resources page on the IPT The first thing that needs to be completed is the shortname of your resource. This shortname uniquely identifies your resource (i.e. dataset) and will eventually show up in the URL of this resource on IPT. These shortname identifiers are also used to create folders on the IPT and they cannot be changed. We therefore advise that the shortname: is unique, descriptive and short (max. 100 characters) does not contain a space, comma, accents or special characters Shortname good examples: VLIZ_benthos_NorthSea_2000 UBC_algae_specimens … When you would delete a resource, please inform your node manager of this action! If you create a test-file, please include _test at the end of your shortname. Then select the type of data you are uploading: Occurrence, Checklist, Sampling-Event (i.e. Event Core), Metadata only, or Other. Note that Checklist datasets are accepted by GBIF, but not currently implemented in OBIS. However, you can still have checklist data hosted on OBIS IPTs. You can also create an entirely new resource by uploading an existing archived resource. See the IPT manual section Upload a Darwin Core-Archive for instructions. Please note the IPT has a 100MB file upload limit, however, there is no limit to the size of a Darwin Core Archive that the IPT can export/publish. Refer to this note in the IPT manual to find out how to work around the file upload limit. Once you have created your resource, you will see an empty resource overview page. 10.2.0.2 Upload data Uploading your source file to the IPT is easy: go to > your resource overview page > Source Data and click on Choose File. This is where you will select and add the files containing your Core table and (if applicable) extensions. You might want to compress/zip your source file first to improve the upload speed of large files. The IPT will unzip them automatically once received. Follow the IPT manual for more detailed instructions (including the option to use multiple source files or to upload via a direct database connection). Accepted formats are delimited text files (csv, tab and files using any other delimiter), either directly or compressed as zip or gzip. Screenshot of where on the IPT page you can add source data Let’s follow an example Event core dataset. The first file we will upload will be a .csv file containing the Event core table. After it’s been selected, we click “Add”. A source file detail page will then be shown, displaying how the IPT has interpreted your file (number of columns, rows, header rows, character encoding, delimiters, etc.). Click the preview button to verify everything is correct, click anywhere on the screen to exit the preview, then click save. Screenshot of page shown after you upload a source file for the first time You can also provide information about how the data table is encoded, how many header rows exist, the type of delimiters, and what type of character encoding you used. You may need to double check the character encoding for your file, especially if you used any special characters (e.g., in species or place names). UTF-8 is one of the most common encoding standards, and you can select this encoding when saving files, depending on the software used: Windows: MS Excel: select Save as. From the drop down select the “CSV UTF-8 (Comma delimited)” option Windows: Notepad: Click on File, then Save as In the drop down menu “Save as type” drop-down, select All Files In the “Encoding” drop-down on the bottom right, select UTF-8. Be sure to name your file using the .csv extension (e.g., data.csv). Mac: Numbers: Select File then Export to… -> CSV. Click Advanced options Click the drop-down menu next to Text Encoding and select Unicode (UTF-8) Click Next, then finally name your file, select a location, and click Save Once you have specified all the above information, click Save, This will redirect you back to the Overview page for your dataset. You can add multiple files for each of your tables by clicking the three vertical dots to the right and “+ Add”. We will add a .csv file for an Occurrence extension, as well as one for the eMoF extension. Example screenshot of the different files, core and extensions, you can upload to an IPT Map your data to Darwin Core Because biodiversity data are published in the Darwin Core standard, the next step is to map your data fields to Darwin Core. As we have mentioned earlier in this manual, the DwC standard includes a list of defined terms and allows your data to be understood and used by others. It also allows an aggregator like OBIS or GBIF to integrate your data with other datasets. Darwin Core mapping is the process of linking the fields in your resource file with the appropriate Darwin Core terms. It is the most challenging step in publishing your data for two reasons: The list of Darwin Core terms can be overwhelming, so it might be difficult to select the ones that are appropriate for your dataset The IPT currently only allows one-to-one mapping of fields, so the ease of mapping will depend on your database structure and on the feasibility of exporting as close to Darwin Core as possible. You can contact your node manager or the OBIS secretariat at info@iobis.org to help guide you through the steps, review your mapping, suggest terms etc. You also welcome to post questions in the OBIS Slack. You can find more information regarding Darwin Core mapping in the IPT manual (including core types, extensions, auto-mapping, default values, value translation, etc.). To add the DwC mappings, click the three vertical dots to the right of this section and select “+ Add” Screenshot of the IPT demonstrating where to add new DwC mappings A popup window will allow you to select your Core type to facilitate mapping - Occurrence or Event core. In this example, we will select Darwin Core Event for the Event core table. Screenshot showing the two Core types you can map a datafile to Next we will confirm which file we are mapping to: the .csv file containing our event data. Then click Save. Because we had already named several of our fields with Darwin Core terms, they have been auto-mapped for us. It is important to review each Darwin Core class (Record, Event, Location, etc.) and ensure any unmapped fields are mapped to the correct DwC term. When you select a term, a drop down menu will appear where you can select the appropriate field from your dataset. It is good practice to double check that the auto-mapped fields are mapped correctly. Once you are done mapping, any unmapped fields will appear at the bottom of the page for you to check. If there is no DwC term to map these terms to, that is okay, but the data will not be published alongside the rest of your dataset. Consider moving these unmapped fields to either dynamicProperties or to one of the extensions (e.g., eMoF), whichever is most applicable. Finally, click Save. You may return to the Overview page by clicking Back. To add DwC mappings for the other files (Occurrence and eMoF), click the same “+ Add” button and go through the same process for each extension table you have. The IPT may identify Redundant terms if certain terms appear in the e.g., Event core and Occurrence extension. If your Occurrence extension (or core) contains information about individualCount and organismQuantity, you can map such fields in both the Occurrence and the eMoF as a measurementType. Screenshot showing other DwC extensions you can map to Watch the video below for an overview of all of the above procedures, from uploading data to mapping terms to DwC. The next step is to fill in or upload metadata. Add metadata Metadata enables users to discover, assess, understand and attribute your dataset for their particular needs, so it pays off to invest some time providing them. Go to your resource overview page > Metadata and click Edit to open the metadata editor. Any information you provide here will be visible on the resource homepage and bundled together with your data when you publish. Screenshot showing where to add or upload metadata Follow the guidelines on the OBIS metadata standards and best practices page, or check the IPT manual for detailed instructions about the metadata editor. You can also upload a file with metadata information. The video below also demonstrates how to fill metadata on the IPT. Publish on the IPT With your dataset uploaded, properly mapped to DwC, and all the metadata filled, you can publish your dataset. On your resource overview page, go to the Publication section, click the vertical dots and select Publish. Screenshot showing where to manage the publishing of your dataset The IPT will now generate your data as Darwin Core, and combine the data with the metadata to package it as a standardized zip-file called a “Darwin Core Archive”. See the IPT manual for more details. Note: Hitting the “publish” button does not mean that your dataset is available to everyone, it is still private, with access limited to the resource managers. It will only be publicly available when you have changed Visibility to Public. You can choose to do this immediately or at a set date. Screenshot showing how to change the visibility of your dataset Your dataset will only be harvested by GBIF when you change Registration to Registered. This step is not needed for OBIS to harvest your datasets. Please do not register your dataset with GBIF if your dataset is already published in GBIF by another publisher. Note that the IPT itself must be registered with GBIF in order to publish to GBIF. The node manager can do this. Screenshot showing where to register your dataset with GBIF. This is only available if the IPT itself is registered with GBIF as well. Back on the resource overview page > Published Release, you can see the details of your first published dataset, including the publication date and the version number. Since your dataset is published privately, the only thing left to do is to click Visibility to Public (see the IPT manual) to make it available to everyone. Warning: please do not do this with your test dataset. It is now listed on the IPT homepage and you can share and link to it, e.g.: http://ipt.vliz.be/resource.do?r=kielbay70. This would be a good time to notify any regional or thematic network you are involved in, which can also have an interest in your dataset. Your published dataset is a static snapshot of your data and will not change until you upload an updated source file and click publish again or publish a new version (do not create a new resource). This procedure has the advantage that your dataset is always available, does not require a live internet connection to your database and can be easily shared. It also allows you to control the publication process more precisely: version 1, version 2, etc. and users are informed of how recent the data are (via the last publication date). To view an older version of the metadata about the resource, just add the trailing parameter &v=n to the URL where v stands for “version”, and n gets replaced by the version number, e.g., http://ipt.vliz.be/ilvo/resource.do?r=zoopl_bpns&v=1. In this way, specific versions of a resource’s EML, RTF, and DwC-A files can be retrieved. Please note, the IPT’s Archival Mode must be turned on in order for old versions of DwC-A to be stored (see Configure IPT settings section of the IPT manual). The first minute of the video below provides an overview of how to publish on the IPT. 10.2.0.3 Publish your metadata as a data paper The Metadata expressed in the EML Profile standard can also be downloaded as a Rich Text Format (RTF) file. The latter can serve as a draft manuscript for a data paper (First database-derived ‘data paper’ published in journal), which can be submitted for peer-review to e.g. a Pensoft journal. Downloading datasets from an IPT To download a dataset from an IPT, simply navigate to the IPT home page (not the Manage Resources tab) and search for the dataset in question. You can search for keywords in the Filter box on the right side of the page. Generally you do not need to log in to an IPT to download a dataset. Overview of home page of an IPT Once you navigate to the page of a dataset, at the top of the page you will have options to download the whole Darwin Core Archive file, or just the metadata as an EML or RTF file. Be sure to note the license associated with the dataset. Overview of a dataset page on an IPT, emphasizing where to download the resource "],["ipt_admin.html", "10.3 IPT Admin Responsibilities", " 10.3 IPT Admin Responsibilities If you are an IPT administrator, you are responsible for: Creating the IPT Registering IPT with OBIS and GBIF Keeping the IPT up to date Describing, checking and uploading the data and metadata to the IPT Guide data providers to publish the data and metadata to the IPT themselves Make sure all relevant information and data for OBIS is available Perform necessary quality checks before the data are released to OBIS Creating and Installing IPTs OBIS nodes can decide to install and manage their IPT on their own institutional servers or use (at no charge!) the OBIS servers in Oostende, Belgium, provided as in-kind by the Flanders Marine Institute (VLIZ), which also runs the European OBIS node (EurOBIS). VLIZ also ensures the IPT instances run on the latest version (important for security updates). Here is an overview of the IPT instances hosted in Oostende: http://ipt.iobis.org/. Please contact the secretariat at info@iobis.org if you would like OBIS to host your IPT. To install your own IPT, please follow the instructions in the GBIF IPT manual. Registration When you have installed your IPT, please provide the IPT instance URL to the OBIS secretariat (helpdesk@obis.org), so your IPT is included in the data harvesting process. OBIS recommends to share the data as widely as possible including with other networks such as GBIF. On 13 October 2014, a cooperation agreement was signed between the secretariats of IOC-UNESCO/OBIS and GBIF in which the two parties recognized the two initiatives (OBIS and GBIF) as complementary with common goals (and in particular OBIS’s role in Marine Biodiversity Data). Together they agreed to work towards maximizing the quantity, quality, completeness and fitness for use of marine biodiversity data, accessible through OBIS and GBIF and in particular in the development of data standards (DwC), technology (IPT), maximizing fitness for use, development of biodiversity indicators for assessments, enhance capacity through training and coordinate approaches to the global science/policy interface. At the 4th session of the OBIS Steering Group (SG-OBIS-IV, Feb 2015), it was recommended that GBIF should harvest OBIS tier 2 nodes if OBIS tier 2 nodes could also harvest marine datasets from their GBIF nodes. In this way OBIS could work directly with the entire marine community and promote its standards and best practices. It was not recommended that iOBIS set up a separate IPT for GBIF to harvest, since this would mean a duplication of effort. In order to publish data with GBIF, the OBIS node also needs to become a data publisher in GBIF, and link the IPT installation with this publishing organization. OBIS nodes are encouraged to use the OBIS node name as the publishers’s name, unless the host institution requires its institutional name to be used. In the latter case, reference to the OBIS node can be added in the description, as well as between brackets in the title. The name of the IPT instance can also refer to the OBIS node. OBIS nodes are also encouraged to select OBIS as the endorsing organization. In this way, the OBIS node is also listed on the OBIS page at GBIF. The video tutorial below will help guide you on registering your OBIS node IPT with GBIF. Maintaining the IPT As the IPT administrator, you must make sure the IPT is kept up to date, datasets (resources) are published in a timely manner, add, edit, or delete IPT users as required, register with GBIF as applicable, and configure the types of cores and extensions accepted by the IPT. To add or update extensions, navigate to the Darwin Core Types and Extensions page from the Administration menu. To install an extension (e.g., DNA Derived Data), simply scroll down the page and click the Install button to the right of the desired extension. Screenshot of IPT Admin page For extensions already installed, you may notice yellow flags indicating a core or extension is out of date. You can update these easily by clicking the Update button. Screenshot demonstrating when core or extensions need to be updated For a detailed breakdown of administrator options, see the IPT guide. "],["data_sharing.html", "10.4 Maintaining and sharing published data 10.5 Update your data in OBIS 10.6 Publish OBIS data to GBIF", " 10.4 Maintaining and sharing published data Content Add a DOI User tracking Update your own data Publish to OBIS and GBIF 10.4.1 Adding a DOI to datasets DOIs are important for tracking your dataset. Fortunately you can easily reserve a DOI for your dataset if the IPT administrator has configured the IPT accordingly. As the IPT administrator, you must enable the capacity for users to reserve DOIs. To do this you first need a DataCite account associated with an Organization. Only one DataCite account can be used to register DOIs in this manner (i.e. IPT users do not need an account). The IPT’s archival mode, configurable on the IPT settings page, must also be turned on (note that enabling this mode will use more disk space) to enable this feature. For more information see the IPT administration manual. Once this has been configured, a data provider or admin can easily reserve a DOI for a dataset. First log in to the IPT, navigate to the Manage Resources tab, then select the dataset for which you wish to reserve a DOI. On the overview page for the dataset, scroll to the Publication section, click the three vertical dots and select “Reserve DOI”. Screenshot indicating how to reserve a DOI for your dataset 10.4.2 User tracking OBIS tracks the number of times your dataset is downloaded. This information is available on your dataset’s page under the Statistics box. Example screenshot of how dataset downloads can be tracked 10.5 Update your data in OBIS To update your own data in OBIS, the process is largely the same as publishing your first version. Follow the steps below: Log in to the IPT where your data are hosted Under the Manage Resources tab, locate your dataset Upload new files, complete the DwC mapping, and/or update any metadata that may have changed In the Publication section, click Publish just as you did before The new version will be automatically updated. A new DOI will be generated only if you generate it yourself on the new version. Deciding when to generate a new DOI is up to you, but generally you should generate a new DOI when there have been major changes to your dataset, such as significant changes in your metadata or a move to a 2.0 resource version within the IPT. Example of IPT version control 10.6 Publish OBIS data to GBIF There are a few differences between OBIS and GBIF. Perhaps the most obvious difference is that OBIS focuses on marine data, whereas GBIF includes broader biodiversity data. However, OBIS implements more strict quality control requirements for published datasets. A complete list of GBIF’s quality control checks can be found here, and a guide on GBIF’s publishing process is here. OBIS currently accepts two core data table types: Occurrence core and Event core. GBIF includes both Occurrence and Event core, with one additional core type, Checklists. GBIF is also developing a new data model to expand data publishing capabilities. Some of the other main differences in how OBIS and GBIF structure and publish datasets that you should be aware of include: OBIS uses WoRMS as the exclusive taxonomic backbone (and WoRMS identifiers to populate scientificNameID), whereas GBIF uses Catalog of Life and does not currently require the use of taxonomic identifiers The OBIS-ENV-DATA structure, the eMoF extension, and the DNA Derived data extensions are not included in GBIF downloads (e.g., this dataset description). This data can still be published alongside your dataset, and is available when it is downloaded from the Source archive, but it will not be included in a GBIF Annotated Archive download. OBIS conducts some QC procedures that GBIF does not, including: Checking validity of depth measurements Checking validity of WoRMS LSID Identifying if taxa are exclusively freshwater or terrestrial GBIF includes most of the same data standards as OBIS (Darwin Core, EML), however GBIF also follows the Biological Collection Access Service (BioCASE/ABCD) Watch the video below for details on how to publish OBIS datasets to GBIF (starting at 1:07), or how to publish GBIF datasets to OBIS (starting at 6:42). See the tables below for a quick comparative reference on which terms are required or recommended in OBIS and GBIF for Occurrence and Event tables. Event Table: Term Status in OBIS Status in GBIF eventID required required eventDate required required decimalLatitude & decimalLongitude required strongly recommended samplingProtocol strongly recommended required samplingSizeValue & samplingSizeUnit strongly recommended required countryCode strongly recommended strongly recommended parentEventID strongly recommended strongly recommended samplingEffort strongly recommended strongly recommended locationID strongly recommended strongly recommended coordinateUncertaintyInMeters strongly recommended strongly recommended geodeticDatum recommended strongly recommended footprintWKT recommended strongly recommended occurrenceStatus required in occurrence extension strongly recommended Occurrence Table: Term Status in OBIS Status in GBIF occurrenceID required required eventDate required required scientificName required required basisOfRecord required required kingdom recommended required decimalLatitude & decimalLongitude required strongly recommended scientificNameID required not required, accepted occurrenceStatus required not required, accepted taxonRank strongly recommended strongly recommended coordinateUncertaintyInMeters strongly recommended strongly recommended individualCount, organismQuantity & organismQuantityType strongly recommended strongly recommended geodeticDatum recommended strongly recommended eventTime recommended not required, accepted countryCode not required, accepted strongly recommended informationWithheld not required, accepted not required, accepted dataGeneralizations not required, accepted not required, accepted country not required, accepted not required, accepted "],["access.html", "11 Data access 11.1 OBIS Homepage and dataset pages 11.2 Mapper 11.3 R package 11.4 API 11.5 Full exports 11.6 Finding your own data in OBIS 11.7 How to contact data provider 11.8 Interpreting downloaded files from OBIS", " 11 Data access OBIS has over 100 million records of marine data accessible for downloading. To download data from OBIS, there are several options: OBIS homepage or advanced dataset search OBIS Mapper Accessible through the R package robis OBIS API Full data exports IPT When you download data from the Mapper or full export, the data you will receive is flattened into one table with occurrence plus event data. eMoF data tables are separate upon request. However when you download a dataset from the OBIS homepage or dataset page, all tables (Event, Occurrence, eMoF) are separate files. 11.1 OBIS Homepage and dataset pages From the OBIS homepage, you can search for data in the search bar in the middle of the page. You can search by particular taxonomic groups, common names, dataset names, OBIS nodes, institute name, areas (e.g., Exclusive Economic Zone (EEZ)), or by the data provider’s country. When you search by dataset you will notice an additional option appears for advanced search options. This will allow you to identify specific datasets, and apply filters for OBIS nodes and whether datasets include extensions. OBIS homepage search, showing where to find the advanced search link Regardless if you found a dataset through the homepage or the advanced Dataset search, you will be able to navigate to individual dataset pages. For individual dataset pages (instead of aggregate pages for e.g., a Family) there are three buttons available: Report issue - allows you to report any issues with the dataset in question Source DwC-A - download the dataset as a Darwin Core-Archive file. This will provide all data tables as separate files within a zipped folder To mapper - this will open another browser with the data shown in the Mapper Dataset download If you searched for aggregate datasets (e.g., all Crustacea records, all records from OBIS-Canada, etc.), the source DwC-A button will not be available to you. To download these data subsets, you must click to mapper and then download the data from the Mapper as a CSV. 11.2 Mapper https://mapper.obis.org Watch this video demonstration of how to use the Mapper as well as the OBIS homepage search. The mapper allows users to visualize and inspect subsets of OBIS data. A variety of filters are available (taxonomic, geographic, time, data quality) and multiple layers can be combined in a single view. Layers can be downloaded as CSV files. Screenshot demonstrating where how to download a particular layer When you download data from the mapper, you will be given the option to include eMoF and/or DNA Derived Data extensions alongside the Event and Occurrence data. You must check the boxes of extensions you want to include in your download. Screenshot showing the popup confirmation for which extensions you want to include in your download from the OBIS Mapper After downloading, you will notice that the Event and Occurrence data is flattened into one table, called “Occurrence.csv”. Upon inspecting this file in your viewer of choice, you will see it contains all 225 possible DwC fields, although not every field will contain data for each observation. Any extensions you checked will be downloaded as separate tables. 11.3 R package https://github.com/iobis/robis The robis R package has been developed to facilitate connecting to the OBIS API from R. The package can be installed from CRAN or from GitHub (latest development version). The package documentation includes a function reference as well as a getting started vignette. As a quick example of what the package can do, you can obtain raw occurrence data by feeding a taxon name or AphiaID to the occurrence function. If you’d like to then download this data, you can simply export R objects with the write.csv function. For example, if we wanted to obtain Mollusc data from OBIS: library(robis) moll<-occurrence(“Mollusca”) write.csv(moll, “mollusca-obis.csv”) This file will be saved to your working directory (if you are not familiar with working directories, read here). After opening the file, you will notice that the fields in the download do not include every possible field, but instead only those where information has been recorded by data providers, plus the fields added by OBIS’s quality control pipeline. To use robis for visualizing and mapping occurrences, see the Visualization section of the manual. Watch the video below for a walkthrough of how to use the robis package to obtain OBIS data. 11.4 API https://api.obis.org/ Both the mapper and the R package are based on the OBIS API, which can also be used to find and download data. When using the API directly, you can filter by the following options: Occurrence Taxon Checklist Node Dataset Institute Area Country Facet Statistics When you have entered all the information you are interested in filtering by, scroll down and click the “Execute” button. This will produce a response detailing how many records match your criteria, as well as information for some of the headers from the data (e.g., basisOfRecord, Order, genus, etc.). A download button will be available for you to download the data as well. When searching with the API, you may need to know certain identifiers, including: AphiaID - obtainable from the WoRMS page of a taxa of interest (e.g. the AphiaID for Mollusca would be 51) Dataset UUID - can be obtained from the URL on individual dataset pages E.g., this dataset’s UUID would be 5061d21c-6161-4ea2-a8d4-38f8285dfc47 Area ID Institute ID - this should be the Ocean Expert ID (e.g., the ID for NOAA Fisheries Service, Southeast Regional Office St. Petersburg is 7532) OBIS node UUID A short video demonstrating use of the API is shown below. 11.5 Full exports https://obis.org/data/access/ To obtain a full export of OBIS data, navigate to the OBIS homepage, click on Data from the top navigation bar, then select Data Access from the dropdown menu. OBIS homepage showing where to navigate to access full database exports Here you will be able to download all occurrence records as a CSV or Parquet file. Note the disclaimer that such exports will not include measurement data, dropped records, or absence records. As with downloads from the Mapper, the exported file is a single Occurrence table. This table includes all provided Event and Occurrence data, as well as 68 fields added by the OBIS Quality Control Pipeline, including taxonomic information obtained from WoRMS. OBIS Data Access page 11.6 Finding your own data in OBIS To find your own dataset in OBIS, you can use the same tools as finding any dataset in OBIS. You have the following options: From the OBIS homepage or the Mapper, you can search by dataset name, species of interest, the OBIS node that you uploaded to, or by institute Note: When using the Mapper you can combine multiple search criteria to help narrow down your search E.g., if we wanted to find this dataset in the Mapper, we could search for OBIS USA under Nodes, National Oceanic and Atmospheric Administration, Washington under Institutes, and/or Radiozoa under Scientific Name. Then when we view the data and scroll down to datasets, the only one listed is the one we were interested in If you have used the (extended)measurementOrFact extension and have measurementType data, you can search by the name of your measurementType, and click on the hyperlink for records. This will populate a list of datasets that you can scroll through which have used a particular measurementType. Note this tool is only for consultatation purposes and should never be used to help you select vocabulary for your data 11.7 How to contact data provider To contact the data provider, navigate to the page for the individual dataset in question (e.g., https://obis.org/dataset/80479e14-2730-436d-acaa-b63bdc7dd06f). Under the “Contacts” section, there will be a list of individuals you can contact. Clicking any name will direct you to your system’s default email program. For example: Example of contact section on a dataset homepage access via the OBIS search If you are the node manager and need to contact the data provider about a particular dataset, contact information should be provided in the metadata and you can contact them from information provided. 11.8 Interpreting downloaded files from OBIS In general, the field names you will see when you download data from OBIS are the same as those seen during the data formatting and publishing process. When you download data from the Mapper you will see all 225 possible Darwin Core fields. Downloading data from an IPT or full export will include only the fields provided by the data provider, formatted as one Occurrence file (or separate files for individual datasets). Some fields are added through the OBIS quality control pipeline, including taxonomic information from WoRMS and the fields flags, bathymetry, and dropped. As mentioned in the Quality Control section, the fields flags and dropped will list quality control issues or if the record was dropped, respectively. Details and definitions for all fields added by the OBIS QC pipeline can be found here. For a full list of the other Darwin Core terms and their definitions included in downloads, please reference the Darwin Core reference guide. "],["citing.html", "11.9 Citing Data from OBIS", " 11.9 Citing Data from OBIS Depending on how you access data from OBIS, there are different ways you should cite downloaded datasets. General OBIS citation: OBIS (YEAR) Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. For individual datasets retrieved from OBIS (dataset citations are available in the zip downloads as html file): [Dataset citation available from metadata] [Data provider details] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. Accessed: YYYY-MM-DD). For example: Sousa Pinto, I., Viera, R. (Year: if not provided use year from dataset publication date) Monitoring of the intertidal biodiversity of rocky beaches with schools in Portugal 2005-2010. CIIMAR - Interdisciplinary Centre of Marine and Environmental Research, Porto. (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. Accessed: 2024-01-01) When data represents a subset of many datasets taken from the integrated OBIS database (e.g. downloaded from the Mapper), you can, in addition to citing the individual datasets (and taking into account the restrictions set at each dataset level), also cite the OBIS database as follows: OBIS (YEAR) [Data e.g. Distribution records of Eledone cirrhosa (Lamarck, 1798)] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. Accessed: YYYY-MM-DD) The derived information products from OBIS are published under the CC0 license and can be cited as follows: OBIS (YEAR) [Information product e.g. Global map showing the Hulbert index in a gridded view of hexagonal cells] (Available: Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. https://obis.org. Accessed: YYYY-MM-DD) "],["dataviz.html", "12 Data Visualization 12.1 Example notebooks using data from OBIS 12.2 obisindicators: calculating & visualizing spatial biodiversity using data from OBIS", " 12 Data Visualization 12.1 Example notebooks using data from OBIS Here are a few R notebooks showcasing the robis package: Data exploration of wind farm monitoring datasets in OBIS Diversity of fish and vulnerable species in Marine World Heritage Sites based on OBIS data Data exploration - Stratified random surveys (StRS) of reef fish in the U.S. Pacific Islands DNADerivedData extension data access Canary Current LME Here are others that may be of interest: Diversity indicators using OBIS data OBIS species richness for OSPAR Quality control of ISA data Accessing gridded data 12.2 obisindicators: calculating & visualizing spatial biodiversity using data from OBIS obisindicators is an R library developed during the 2022 IOOS Code Sprint. The purpose was to create an ES50 diversity index within hexagonal grids following the diversity indicators notebook by Pieter Provoost linked above. The package includes several examples, limited to 1M occurrences, that demonstrate uses of the package. Screenshot of diversity map derived from OBIS data "],["other_resources.html", "13 Other Resources 13.1 MBON Pole to Pole Tutorial 13.2 IOOS Darwin Core Guide 13.3 EMODnet Biology 13.4 Template Generators 13.5 Bionomia", " 13 Other Resources In this section we highlight useful resources created by collaborators and other community members. 13.1 MBON Pole to Pole Tutorial https://www.youtube.com/watch?v=teJhfsSWonE This tutorial was created by the MBON Pole to Pole project to help guide people through the process of transforming datasets to Darwin Core using tools MBON Pole to Pole has developed. 13.2 IOOS Darwin Core Guide https://ioos.github.io/bio_data_guide/ This book contains a collection of examples and resources related to mobilizing marine biological data to the Darwin Core standard for sharing though OBIS. This book has been developed by the Standardizing Marine Biological Data Working Group (SMBD). The working group is an open community of practitioners, experts, and scientists looking to learn and educate the community on standardizing and sharing marine biological data. 13.3 EMODnet Biology https://classroom.oceanteacher.org/course/view.php?id=958 Contributing Datasets to EMODnet Biology is a course hosted on Ocean Teacher Global Academy (OTGA), developed by members of the European Marine Observation and Data Network. The course prepares users to format, publish, and perform quality control checks on datasets according to Darwin Core standards. While targeted at EMODnet Biology users, this course has significant overlap in how to prepare datasets for OBIS and is useful for those unfamiliar with OBIS standards. Note, an account with OTGA is required to access the course. 13.4 Template Generators There is an Excel template generator developed by Luke Marsden & Olaf Schneider as part of the Nansen Legacy project. It allows the creation of Event or Occurrence core templates, with an optional eMoF extension. Note this template generator is aimed at GBIF users, so make sure to account for and include required OBIS terms. There is also an Excel to Darwin Core macro tool developed by GBIF Norway that you can download for use in Microsoft Excel. This macro can help you set up Event, Occurrence, and eMoF tables by selecting all relevant DwC fields from a list, or by importing data from another spreadsheet. It allows for auto-generation of identifiers (e.g. eventID, occurrenceID) if macros are enabled, and can also auto-populate the eMoF when measurement fields in the Occurrence table are populated. 13.5 Bionomia https://bionomia.net/ Bionomia is a platform that links individuals to the biological specimens they collect and identify, recognizing their contributions to biodiversity research. To ensure your data is integrated, publish your dataset compliant with Darwin Core standards and include relevant information in fields such as recordedBy, recordedByID, identifiedBy, identifiedByID. Bionomia automatically harvests specimen data when published in this format (learn more at Bionomia). Please note, Bionomia respects privacy; attributions are not openly shared if the individual has not made their profile publicly accessible (see Bionomia’s privacy policy). Users can also utilize Bionomia’s integration with Zenodo by enabling settings to push specimen data into Zenodo (see Bionomia’s integration with Zenodo). "],["404.html", "Page not found", " Page not found The page you requested cannot be found (perhaps it was moved or renamed). You may want to try searching to find the page's new location, or use the table of contents to find the page you are looking for. "]] diff --git a/style.css b/style.css index da64e43..e4f77ff 100644 --- a/style.css +++ b/style.css @@ -30,7 +30,7 @@ table { .caution{ background-image: url("images/icons/triangle-exclamation-solid-yellow.png"); background-size: 50px 50px; - background-position-y: 45px; + background-position-y: center; background-position-x: 5px; background-repeat: no-repeat; } \ No newline at end of file diff --git a/vocabulary.html b/vocabulary.html index 0108e79..ad32732 100644 --- a/vocabulary.html +++ b/vocabulary.html @@ -335,139 +335,141 @@

    6 Standardizing terms & vocab

    6.1 Map data fields to Darwin Core

    There are many possible ways of setting up your datasheets, and if you are new to OBIS you likely did not use controlled Darwin Core (DwC) or BODC vocabulary before samples were collected. In mapping your data fields to DwC we recommend documenting your choices so you have a reference to go back to should the need arise. In such a document you should take notes on the choices you made, as well as any actions you had to take (e.g. separate one column into many, convert dates or coordinates, etc.).

    For example, a DwC mapping reference table could look like the following:

    - +
    ----++++ - - - - + + + + - - - - + + + + - - - - + + + +
    Verbatim field nameMapped DwC termActions takenNotesVerbatim field nameMapped DwC termActions takenNotes
    dateeventDateconvert dates to ISOdateeventDateconvert dates to ISO
    coordinatesdecimalLongitude, decimalLatitudeconvert ddmmss to decimal degrees, separated one column into 2 for longitude and latitudeput original coordinates into verbatimCoordinatescoordinatesdecimalLongitude, decimalLatitudeconvert ddmmss to decimal degrees, separated one column into 2 for longitude and latitudeput original coordinates into verbatimCoordinates

    In order to help you map your data to DwC terms, we have provided the table below which outlines some common data fields, their associated Darwin Core vocabulary, and which data table the field is likely to go in:

    ---+++ - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + +
    Common Raw TermsDwC FieldData tableCommon Raw TermsDwC FieldData table
    Date, TimeeventDateEvent, OccurrenceDate, TimeeventDateEvent, Occurrence
    Species, g_s, taxascientificNameOccurrenceSpecies, g_s, taxascientificNameOccurrence
    Any biotic/abiotic measurements*measurementType, measurementValue, measurementUnit*eMoFAny biotic/abiotic measurements*measurementType, measurementValue, measurementUnit*eMoF
    DepthmaximumDepthInMeters or minimumDepthInMetersEvent, OccurrenceDepthmaximumDepthInMeters or minimumDepthInMetersEvent, Occurrence
    Lat/Latitude, Lon/Long/Longitude, dddecimalLatitude, decimalLongitudeEvent, OccurrenceLat/Latitude, Lon/Long/Longitude, dddecimalLatitude, decimalLongitudeEvent, Occurrence
    Sampling methodsamplingProtocoleMoFSampling methodsamplingProtocolEvent, eMoF
    Sample size, N, #, No.sampleSizeValueeMoFSample size, N, #, No.sampleSizeValueEvent, eMoF
    LocationlocalityEventLocationlocalityEvent
    Presence, absenceoccurrenceStatusOccurrencePresence, absenceoccurrenceStatusOccurrence
    Type of record/ specimenbasisofRecordOccurrenceType of record/ specimenbasisofRecordOccurrence
    Person/ people that recorded the original OccurrencerecordedByOccurrencePerson/ people that recorded the original OccurrencerecordedByOccurrence
    OrcID of person/ people that recorded the original OccurrencerecordedByIDOccurrenceOrcID of person/ people that recorded the original OccurrencerecordedByIDOccurrence
    Person/ people that identified the organismidentifiedByOccurrencePerson/ people that identified the organismidentifiedByOccurrence
    OrcID of person/ people that identified the organismidentifiedByIDOccurrenceOrcID of person/ people that identified the organismidentifiedByIDOccurrence
    Data collector, data creatorrecordedByEvent, OccurrenceData collector, data creatorrecordedByEvent, Occurrence
    Taxonomist, identifieridentifiedByOccurrenceTaxonomist, identifieridentifiedByOccurrence
    Record number, sample number, observation numberoccurrenceID (either ID or incorporated into ID)OccurrenceRecord number, sample number, observation numberoccurrenceID (either ID or incorporated into ID)Occurrence
    -

    Note that mapping abiotic/biotic measurement fields (sex, temperature, abundance, lengths, etc.) will occur within the extendedMeasurementOrFact extension. Here this data will go from being a separate column to being condensed into the measurementType and measurementValue fields.

    +
    +

    Note that mapping abiotic/biotic measurement fields (sex, temperature, abundance, lengths, etc.) will occur within the extendedMeasurementOrFact extension. Here this data will go from being a separate column to being condensed into the measurementType and measurementValue fields.

    +

    The obistools R package also has the map_fields function that you can use to map your dataset fields to a DwC term.

    @@ -506,8 +508,10 @@

    6.2.1 Populate measurementUnitID<

    6.2.2 Populate measurementValueID

    The measurementValueID field is used to provide an identifying code for measurementValues that are non-numerical (e.g. sampling related, sex or life stage designation, etc.).

    -

    Note: it is NOT used for standardizing numeric measurements!

    -

    Unlike measurementUnitID, there is more than one collection which may be used to search for and use concepts from. The collection is dependent on which type of measurementValue you have. See the table below for some common, non-exhaustive examples. Note that behaviour is an exception in that we recommend to use codes from the ICES vocabulary server, instead of NERC. A video is available on the OBIS YouTube Vocabulary series that demonstrates how to find and select vocabularies for measurementValueID from both NREC and ICES.

    +
    +

    measurementValueID is NOT used for standardizing numeric measurements!

    +
    +

    Unlike measurementUnitID, there is more than one collection which may be used to search for and use concepts from. The collection is dependent on which type of measurementValue you have. See the table below for some common, non-exhaustive examples. Note that behaviour is an exception in that we recommend to use codes from the ICES vocabulary server, instead of NERC. The video below, from the OBIS YouTube Vocabulary series , demonstrates how to find and select vocabularies for measurementValueID from both NERC and ICES.

    @@ -573,6 +577,8 @@

    6.2.2 Populate measurementValueID
  • The DOI of the paper/manual
  • Handle for publications on IOC’s Ocean Best Practices repository, e.g. http://hdl.handle.net/11329/304
    • +

    6.2.3 Populate measurementTypeID

    @@ -613,10 +619,10 @@
    6.2.3.1.1 Selecting P01 codes for
  • Name of sampling instrument https://vocab.nerc.ac.uk/collection/P01/current/NMSPINST/
  • Sample duration https://vocab.nerc.ac.uk/collection/P01/current/AZDRZZ01/
    • -

    Use the following decision tree to help you select P01 codes for biological, chemical, physical, and/or sampling measurements. See the OBIS YouTube Vocabulary series for guidance on how to use this tree and examples for different types of measurementTypeIDs. Note that the measurementTypeID:sampling measurements branch is newly developed, and we are happy to receive feedback if the suggestions are not working for you.

    +

    Use the following decision tree to help you select P01 codes for biological, chemical, physical, and/or sampling measurements. See the OBIS YouTube Vocabulary series for guidance on how to use this tree and examples for different types of measurementTypeIDs. Note that the measurementTypeID:sampling measurements branch is newly developed, and we are happy to receive feedback if the suggestions are not working for you.

    -Decision tree to assist with finding and selecting appropriate controlled vocabulary to be used in the eMoF. For those unfamiliar with the tree see the OBIS YouTube Vocabulary series. -
    Decision tree to assist with finding and selecting appropriate controlled vocabulary to be used in the eMoF. For those unfamiliar with the tree see the OBIS YouTube Vocabulary series.
    +_Decision tree to assist with finding and selecting appropriate controlled vocabulary to be used in the eMoF. For those unfamiliar with the tree see the OBIS YouTube Vocabulary series. +
    _Decision tree to assist with finding and selecting appropriate controlled vocabulary to be used in the eMoF. For those unfamiliar with the tree see the OBIS YouTube Vocabulary series.