Update bibliography

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-@misc{eranti_open_2023,
- abstract = {Read this work by Batut B, at F1000Research.},
- author = {Eranti, Pradeep and Yehudi, Yo and El-Gebali, Sara and Batut, Bérénice},
- month = {July},
- shorttitle = {{\textless}p{\textgreater}{Open} {Seeds} by {OLS}},
- title = {Open {Seeds} by {OLS}: {A} virtual mentoring \& training program for {Open} {Science} ambassadors},
- url = {https://f1000research.com/slides/12-1485},
- urldate = {2023-11-29},
- year = {2023}
-}
-
-@misc{freeberg_open_2023,
- abstract = {Read this work by Batut B, at F1000Research.},
- author = {Freeberg, M. and Psomopoulos, F. and Pilvar, D. and Batut, B. and Community, O. L. S.},
- month = {June},
- shorttitle = {Open seeds by {OLS}},
- title = {Open seeds by {OLS}: {A} mentoring \& training program for open science ambassadors},
- url = {https://f1000research.com/posters/12-710},
- urldate = {2023-11-20},
- year = {2023}
-}
-
-@article{knowles_we_2021,
- abstract = {Research software infrastructure is critical for accelerating science, and yet, these digital public goods are often unsustainably funded. Solving this problem requires an appreciation of the intrinsic value of research software outputs, and greater investment of time and effort into effectively funding maintenance of software at scale.},
- author = {Knowles, Rebecca and Mateen, Bilal A. and Yehudi, Yo},
- copyright = {2021 The Author(s), under exclusive licence to Springer Nature America, Inc.},
- doi = {10.1038/s43588-021-00048-5},
- issn = {2662-8457},
- journal = {Nature Computational Science},
- keywords = {Computational science, Culture, Funding, Software},
- language = {en},
- month = {March},
- note = {Number: 3
-Publisher: Nature Publishing Group},
- number = {3},
- pages = {169--171},
- title = {We need to talk about the lack of investment in digital research infrastructure},
- url = {https://www.nature.com/articles/s43588-021-00048-5},
- urldate = {2023-11-20},
- volume = {1},
- year = {2021}
-}
-
-@article{treasure_ten_2022,
- abstract = {In recent years, a wide variety of mentorship programmes targeting issues that cannot be addressed through traditional teaching and learning methods alone have been developed. Mentoring plays significant roles in the growth and development of both mentors and mentees, and the positive impacts of mentoring have been well documented. Mentorship programmes are therefore increasingly being implemented in a wide variety of fields by organisations, academic institutes, businesses, and governments. While there is a growing body of literature on mentoring and mentorship programmes, gaining a clear overview of the field is often challenging. In this article, we therefore provide a concise summary of recommendations to consider when designing and establishing mentorship programmes. These recommendations are based on the collective knowledge and experiences of 4 different emerging and established mentorship programmes and can be adapted across various mentorship settings or contexts.},
- author = {Treasure, Anne M. and Hall, Siobhan Mackenzie and Lesko, Igor and Moore, Derek and Sharan, Malvika and Zaanen, Menno van and Yehudi, Yo and Walt, Anelda van der},
- doi = {10.1371/journal.pcbi.1010015},
- issn = {1553-7358},
- journal = {PLOS Computational Biology},
- keywords = {Artificial intelligence, Communications, Human learning, Internet, Machine learning, Social communication, Sustainability science, Telecommunications},
- language = {en},
- month = {May},
- note = {Publisher: Public Library of Science},
- number = {5},
- pages = {e1010015},
- title = {Ten simple rules for establishing a mentorship programme},
- url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010015},
- urldate = {2023-11-20},
- volume = {18},
- year = {2022}
-}
-
-@article{williams_international_2023,
- abstract = {Science, technology, engineering, mathematics, and medicine (STEMM) fields change rapidly and are increasingly interdisciplinary. Commonly, STEMM practitioners use short-format training (SFT) such as workshops and short courses for upskilling and reskilling, but unaddressed challenges limit SFT’s effectiveness and inclusiveness. Education researchers, students in SFT courses, and organizations have called for research and strategies that can strengthen SFT in terms of effectiveness, inclusiveness, and accessibility across multiple dimensions. This paper describes the project that resulted in a consensus set of 14 actionable recommendations to systematically strengthen SFT. A diverse international group of 30 experts in education, accessibility, and life sciences came together from 10 countries to develop recommendations that can help strengthen SFT globally. Participants, including representation from some of the largest life science training programs globally, assembled findings in the educational sciences and encompassed the experiences of several of the largest life science SFT programs. The 14 recommendations were derived through a Delphi method, where consensus was achieved in real time as the group completed a series of meetings and tasks designed to elicit specific recommendations. Recommendations cover the breadth of SFT contexts and stakeholder groups and include actions for instructors (e.g., make equity and inclusion an ethical obligation), programs (e.g., centralize infrastructure for assessment and evaluation), as well as organizations and funders (e.g., professionalize training SFT instructors; deploy SFT to counter inequity). Recommendations are aligned with a purpose-built framework—“The Bicycle Principles”—that prioritizes evidenced-based teaching, inclusiveness, and equity, as well as the ability to scale, share, and sustain SFT. We also describe how the Bicycle Principles and recommendations are consistent with educational change theories and can overcome systemic barriers to delivering consistently effective, inclusive, and career-spanning SFT.},
- author = {Williams, Jason J. and Tractenberg, Rochelle E. and Batut, Bérénice and Becker, Erin A. and Brown, Anne M. and Burke, Melissa L. and Busby, Ben and Cooch, Nisha K. and Dillman, Allissa A. and Donovan, Samuel S. and Doyle, Maria A. and Gelder, Celia W. G. van and Hall, Christina R. and Hertweck, Kate L. and Jordan, Kari L. and Jungck, John R. and Latour, Ainsley R. and Lindvall, Jessica M. and Lloret-Llinares, Marta and McDowell, Gary S. and Morris, Rana and Mourad, Teresa and Nisselle, Amy and Ordóñez, Patricia and Paladin, Lisanna and Palagi, Patricia M. and Sukhai, Mahadeo A. and Teal, Tracy K. and Woodley, Louise},
- doi = {10.1371/journal.pone.0293879},
- issn = {1932-6203},
- journal = {PLOS ONE},
- keywords = {COVID 19, Careers, Human learning, Instructors, Medicine and health sciences, Science education, Scientists, Workshops},
- language = {en},
- month = {November},
- note = {Publisher: Public Library of Science},
- number = {11},
- pages = {e0293879},
- title = {An international consensus on effective, inclusive, and career-spanning short-format training in the life sciences and beyond},
- url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293879},
- urldate = {2023-11-20},
- volume = {18},
- year = {2023}
-}
-
-@misc{yehudi_enhancing_nodate,
- author = {Yehudi, Yo and Stack-Whitney, Kaitlin and Sharan, Malvika},
- doi = {10.31219/osf.io/k3bfn},
- title = {Enhancing the inclusivity and accessibility of your online calls},
- url = {https://osf.io/k3bfn/},
- urldate = {2023-11-20}
-}
-
-@article{yehudi_subjective_2023,
- abstract = {Biological science produces “big data” in varied formats, which necessitates using computational tools to process, integrate, and analyse data. Researchers using computational biology tools range from those using computers for communication, to those writing analysis code. We examine differences in how researchers conceptualise the same data, which we call “subjective data models”. We interviewed 22 people with biological experience and varied levels of computational experience, and found that many had fluid subjective data models that changed depending on circumstance. Surprisingly, results did not cluster around participants’ computational experience levels. People did not consistently map entities from abstract data models to the real-world entities in files, and certain data identifier formats were easier to infer meaning from than others. Real-world implications: 1) software engineers should design interfaces for task performance, emulating popular user interfaces, rather than targeting professional backgrounds; 2) when insufficient context is provided, people may guess what data means, whether or not they are correct, emphasising the importance of contextual metadata to remove the need for erroneous guesswork.},
- author = {Yehudi, Yo and Hughes-Noehrer, Lukas and Goble, Carole and Jay, Caroline},
- copyright = {2023 The Author(s)},
- doi = {10.1038/s41597-023-02627-9},
- issn = {2052-4463},
- journal = {Scientific Data},
- keywords = {Computational science, Data processing},
- language = {en},
- month = {November},
- note = {Number: 1
-Publisher: Nature Publishing Group},
- number = {1},
- pages = {756},
- title = {Subjective data models in bioinformatics and how wet lab and computational biologists conceptualise data},
- url = {https://www.nature.com/articles/s41597-023-02627-9},
- urldate = {2023-11-20},
- volume = {10},
- year = {2023}
-}