re-arranged schedule

environment.html

@@ -39,11 +39,13 @@ <h2> Environment </h2>
 <ul>
    <li> <strong>Climate is What We Expect, Weather (Data) is What We Get Via APIs  </strong> <br> <em> Adam Sparks, Curtin University </em> 
 <p> No matter where we are, weather shapes our lives. We are all familiar with ordinary everyday weather concerns, do I need an umbrella when I step out the door today or maybe how cold will it be, will I need to wear a jacket later? Businesses use it to track long-term patterns and understand historical trends. Major media organisations use historical weather data to tell stories by visualising the data to show the effects of climate change to the public. Agricultural researchers use weather data in their analyses to help explain experimental results or build complex models that simulate farming systems. And governments use the data to prepare and plan for future disasters or understand seasonal trends to ensure adequate infrastructure is in place. While the uses are often critical, and the data may be freely or openly available, getting the data quickly and easily into R can be frustrating. There are 193 members of the World Meteorological Organisation (WMO), many of which offer some sort of programmatic access to historical weather data or forecasted weather data via APIs, but some do not, while there are other non-member organisations that do. I'll present the good, the bad and the ugly of different weather data sources and getting the data wrangled and tamed ready to go in your R session with what you need to think about for end users of the data when you make a weather data API client R package to help make our world more understandable. </p> </li>
-   <li> <strong> Read, manipulate and plot gridded data with metR </strong> <br><em> Elio Campitelli, Monash University </em> 
+   <li> <strong> Read, Manipulate and Plot Gridded Data with metR </strong> <br><em> Elio Campitelli, Monash University </em> 
 <p> The metR package provides an assortment of tools for wrangling, plotting and analysing meteorological field data. It has been developed from my own research needs, originally in response to a lack of available tools. For example, a large number of functions are provided for plotting variations of filled contours, which preceded the ggplot2 filled contour functions. Because meteorological field data is delivered in NetCDF there is a function to read this type of file. Utility functions allow conversion between different different longitude conventions.  Principal components is a primary analysis tool, so there are functions for this, along with various model fitting procedures. There are tools for imputation, finding anomalies and for model diagnostics. Writing a package tailored to what you, individually need, can be useful for others: philosophically, if I need it, probably others do too! </p> 
 </li>
-   <li> <strong> Opening Pacific Data: opportunities and challenges for domain experts and data scientists </strong> <br> <em>  Giulio Valentino Dalla Riva, Pacific Community </em> 
-<p> The Pacific Community (SPC) offers open access to high quality, domain-expert curated, regularly updated Pacific data through a variety of access points. In particular, the SPC Pacific Data Hub .stat portal is accessible both through a point-and-click interface and a developer-friendly API (with SDK in R, Python, JS). In this talk I will present the Pacific Data Hub ecosystem, and highlight the opportunities offered by the SPC data portal for both the data user and the developer. </p>
+   <li> <strong> Open Air Quality directly in R with airpurifyr </strong> <br> <em>  Michael Lydeamore, David Wu, Jayani Lakshika </em> 
+<p> Air quality and pollution have emerged as critical research areas with implications across various policy domains, extending beyond traditional climate-focused studies. Despite this growing interest, many projects still rely on limited, ad-hoc datasets. I will introduce airpurifyr, a new R package designed to facilitate access to the OpenAQ Web API, a freely available and semi-curated global database of air quality measurements.
+<p> In addition to providing an overview of the airpurifyr package, I will showcase research conducted by our master's students, who have leveraged the OpenAQ API to enhance their data exploration skills and contribute to the field of air quality analysis.
+<p> By streamlining access to comprehensive air quality data, airpurifyr aims to empower researchers, policymakers, and students alike, fostering more robust analysis and evidence-based decision-making in the ongoing pursuit of cleaner, healthier air worldwide. </p>
 </li>
 </ul>
 <br><br>

fun.html

@@ -43,9 +43,12 @@ <h2> Fun stuff </h2>
 96,000 ticket holders per night. The biggest shows of Taylor Swift's career. “Taylor-Gaters” hanging out outside. 3 nights in a row, like 3 consecutive AFL Grand Finals.
 How do you get such a large crowd to the venue and home again from all over Melbourne, Victoria, Australia (and the world), all with no underground car park? By public transport of course!
 Belinda will take you through her strategic model of how she estimated how many Swifties would be using each Metro train line from postcode-level ticketholder data.  </li>
-   <li> <strong> Why build silly things R? </strong> <br><em> Fonti Kar, University of NSW </em> 
+   <li> <strong> Why Build Silly Things R? </strong> <br><em> Fonti Kar, University of NSW </em> 
 <p> Data science is an ever-evolving industry that requires constant upskilling. The pressures to learn the latest tools for project deliverables or to enhance one’s CV can be a hindrance to effective learning. Here, I argue for the need for silliness when developing new R skills. Learning is far more enjoyable and conducive to retention and application when we take away the seriousness of upskilling. I will share my experience in creating <a href="https://fontikar.github.io/ohwhaley/"> ohwhaley</a> - a <em> toy </em> R package which serves as a tool for learning package development and upskilling new learners. I hope attendees will walk away feeling more light-hearted and empowered to build silly things in R to reinvigorate their curiosity for R knowledge.
  </p> </li>
+   <li> <strong> Opening Pacific Data: opportunities and challenges for domain experts and data scientists </strong> <br> <em>  Giulio Valentino Dalla Riva, Pacific Community </em> 
+<p> The Pacific Community (SPC) offers open access to high quality, domain-expert curated, regularly updated Pacific data through a variety of access points. In particular, the SPC Pacific Data Hub .stat portal is accessible both through a point-and-click interface and a developer-friendly API (with SDK in R, Python, JS). In this talk I will present the Pacific Data Hub ecosystem, and highlight the opportunities offered by the SPC data portal for both the data user and the developer. </p>
+</li>
 </ul>
 <br><br>
 

index.html

@@ -129,8 +129,8 @@ <h2> Programme </h2>
 <li> 12:00-1:00 Lunch
 <li> 1:00-2:30 Invited talks - <a href="education.html" target="_blank"> Education (3) </a>
 <li> 2:30-3:00 Afternoon tea
-<li> 3:00-4:00 Invited talks - <a href="fun.html" target="_blank"> Fun stuff (2) </a>
-<li> 4:00-4:30 Lightning talks - <a href="tips.html" target="_blank"> An assortment of tips and tricks</a>  <li> 4:30-5:00 Closing discussion
+<li> 3:00-4:30 Invited talks - <a href="fun.html" target="_blank"> Fun stuff (3) </a>  
+<li> 4:30-5:00 Closing discussion
 </ul>
 
 <h2> Location information </h2>