FAIR in ML, AI Readiness, & Reproducibility Workshop

The FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network (FARR RCN) is hosting computer scientists, geoscientists, data practitioners, and tool builders and providers for a two-day workshop to advance AI readiness, reproducibility, and FAIR principles in machine learning.

Eric Sokol, NEON quantitative ecologist, is presenting on outcomes from the AI-ready ecology/biodiversity data infrastructure workshop, FAIR4AI-Biodiversity. Also, the winners of the NSF Harnessing the Data Revolution (HDR) Scientific Modeling Out Of Distribution (Scientific-MOOD) FAIR Challenge will present their solutions at the workshop. Read more about the process of developing a submission to the HDR Scientific-MOOD FAIR Challenge in our blog.  

Learn more about the FARR RCN workshop at farr-rcn.org/workshop26.