FAIR: the future of data sharing In-Person
Researchers and students entering the academic world are landing in an environment unlike any we have seen before. The generalized publish or perish policy has generated a data reproducibility crisis that threatens our credibility. On the other hand, the increase of our data storage and computational capacities, as well as the development of artificial intelligence opens huge opportunities. Instead of limited case studies, we can now compare simultaneously thousands of systems and understand the general laws governing the performances of our applications faster than we ever did. And this progress is much needed in regards in the challenges – global warming, major pandemic risks, etc. – that we are currently facing.
However, such large-scale studies require data to be structured, standardized, shared and perfectly documented, so that it can be used across projects and across institutes and contain all the information needed to ensure reproducibility. In the era of terabyte storage capacities, producing such data is no technical issue, but we need to adapt our best-practices to unlock data potential. The concept of FAIR data, for Findable, Accessible, Inter-operable and Reusable (or also Findable and AI-Ready) summarizes what we need our experimental, theoretical and simulation results to be. I will first present this concept, our need for it, and briefly the changes we need to implement to really enter the 21st century data production era. We will then explore the work of the FAIRmat consortium, one of the groups actively working on this transition, as well as NOMAD, one of the informatics tools to format, store and handle those data.
- Date:
- Tuesday, March 4, 2025
- Time:
- 2:00pm - 3:00pm
- Time Zone:
- Baghdad, East Africa Time (change)
- Location:
- Building 9 (Room 2220)
- Campus:
- KAUST
- Audience:
- KAUST Faculty KAUST Researchers KAUST Students M.Sc. Students PhD Students Post Docs Visiting Students
FAIR data implementer.