24 Oct 2021
Working Towards Understanding the Role of FAIR for Machine Learning presented at 2nd Workshop on Data and research objects management for Linked Open Science (DaMaLOS 2021). (paper) 
20 Sep 2021
Steps towards defining FAIR principles for Machine Learning (ML) BoF accepted at RDA VP18 
20 Sep 2021
Towards FAIR for Machine Learning (ML) models Birds of a Feather (BoF) session accepted at SC21 Conference
06 Aug 2021
First FAIR4HEP paper submitted to arXiv 
21 Apr 2021
IRIS-HEP Topical Presentation 
23 Feb 2021
FAIR for ML Models BoF accepted at RDA VP17 
9 Nov 2020
FAIR for ML Models poster at RDA VP16 
11 August 2020
FAIR4HEP Project launches! 
FAIR4HEP is a collaboration between scientists at the University of Illinois at Urbana-Champaign, Massachusetts Institute of Technology, University of California at San Diego and the University of Minnesota
This research is supported by DE-SC0021258, DE-SC0021395, DE-SC0021225, and DE-SC0021396 from the Office of Advanced Scientific Computing Research within U.S. Department of Energy Office of Science.
