FAIR for Machine Learning; Building on the Lessons from FAIR Software
FAIR for Machine Learning; Building on the Lessons from FAIR Software
Ensuring that data are FAIR is nowadays a clear expectation across all science domains, as a result of many years of global efforts. Research software, has only just started to receive the same level of attention in recent years, with targeted actions towards the definition of the FAIR principles as applied to research software, as well as concerted efforts around reproducibility, quality, and sustainability. Given the rapid rise of ML as a key technology across all science domains, it is important to build on our collective experience, and already start addressing the challenges ahead of us, towards making ML FAIR. These are slides that were presented at the 4th Workshop on Metadata and Research (objects) Management for Linked Open Science - DaMaLOS 2024, co-located with ESWC on 26th May 2024, Hersonissos, Crete, Greece.
- University of California, San Diego United States
- ΙΝΣΤΙΤΟΥΤΟ ΕΦΑΡΜΟΣΜΕΝΩΝ ΒΙΟΕΠΙΣΤΗΜΩΝ/ΕΚΕΤΑ Greece
- Centre for Research and Technology Hellas Greece
Machine Learning, Standards, Best Practices, Software, FAIR
Machine Learning, Standards, Best Practices, Software, FAIR
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