This training focuses on the implementation of the OS principles in AI development and highlights open-source AI tools and notable projects at the intersection of OS and AI.

By the end of this training, learners should be able to:

• explain the importance of FAIR and open data for training AI models and for AI-assisted research
• understand the role of AI in data analysis
• understand how open data and software licences enable the development of AI tools
• be familiar with examples of AI tools for research

Training outline

• FAIR principles and the concept of AI-ready data
• Using open-source AI  to create customized tools for research (e.g. customized chatbots)
• Simulations and Virtual Research Environments
• Open-source AI tools for research (Tip: make the selection relevant to the target group’s discipline)
• Notable OS projects involving AI (Tip: make the selection relevant to the target group’s discipline)

Resources for facilitators and learners

Publications

• OECD. 2023. Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research . OECD. https://doi.org/10.1787/a8d820bd-en.
• Open Data for AI: What Now? Paris: United Nations Educational, Scientific and Cultural Organization.
• Huerta, E. A., Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, et al. 2023. ‘FAIR for AI: An Interdisciplinary and International Community Building Perspective’. Scientific Data 10 (1): 487. https://doi.org/10.1038/s41597-023-02298-6.
• Scheffler, Matthias, Martin Aeschlimann, Martin Albrecht, Tristan Bereau, Hans-Joachim Bungartz, Claudia Felser, Mark Greiner, et al. 2022. ‘FAIR Data Enabling New Horizons for Materials Research’. Nature 604 (7907): 635–42. https://doi.org/10.1038/s41586-022-04501-x.
• Sbailò, Luigi, Ádám Fekete, Luca M. Ghiringhelli, and Matthias Scheffler. 2022. ‘The NOMAD Artificial-Intelligence Toolkit: Turning Materials-Science Data into Knowledge and Understanding’. Npj Computational Materials 8 (1): 1–7. https://doi.org/10.1038/s41524-022-00935-z.
• Sansone, Susanna-Assunta, Philippe Rocca-Serra, Mark Wilkinson, and Lee Harland. 2022. ‘FAIR: Making Data AI-Ready’. In Artificial Intelligence for Science, 627–40. WORLD SCIENTIFIC. https://doi.org/10.1142/9789811265679_0033.

Videos

• RDA Research Data Alliance, 2022. P19 BO6 BoF - Is FAIR FAIR? A Discussion of the Overlaps in the FAIR Principles of Data Management.

Elucidata Corporation, 2023.

• DataFAIR 2023- FAIR Data Governance and Generative AI.

• Center for Open Science, 2023. How Is AI Impacting Science?

• Open Science Fair, 2023. OS Fair 2023 - Panel - AI with and for Open Science.

• BrainModes, 2020. What Is The Virtual Research Environment?

• Euro-BioImaging Communication, 2024. Tools from AI4Life That Anyone Can Use.

• Blue-Cloud, 2022. Blue-Cloud Hackathon - Using the Blue-Cloud Virtual Research Environment.

Software

• Stumpp, Jürgen. (2016) 2024. ‘Awesome Chatbots’.

• Blumenfeld, Josh. 2023. ‘NASA and IBM Openly Release Geospatial AI Foundation Model for NASA Earth Observation Data’. NASA Eathdata. Earth Science Data Systems, NASA. 3 August 2023.