Hi all, Here is a notes doc<https://docs.google.com/document/d/1Wrnl5vSJ9DPNyMxUFXg_peFZyjHJJKPQ4mrHU3pwGj0/edit?tab=t.0> <https://docs.google.com/document/d/1Wrnl5vSJ9DPNyMxUFXg_peFZyjHJJKPQ4mrHU3pwGj0/edit?tab=t.0> from the FARR workshop Nrupen, Isma, and I attended last week. A few discussion points for our next meetings: * Should we develop an AI-agent to assess FAIRness of models? * Strong theme of moving past FAIR for humans and move towards AI readiness * ❗MISM should require SKILL.md<http://skill.md/> file (Isma and Nrupen agree) so agent can run workflow * https://workflowhub.eu/ * Internship program: coordinate with CDEC to implement FAIR requirements * Training to get interns familiar with FAIR and expectations * Implement FAIR checklist for output products (good way to test run our standards) * 🥐Croissant is an open, community-built, standardized metadata vocabulary for ML datasets, including key attributes and properties of datasets, as well as information required to load them into ML tools. Croissant enables data interoperability across ML frameworks and beyond, making ML easier to reproduce and replicate. By building the vocabulary as an extension to schema.org, a machine-readable standard to describe structured data, Croissant also makes ML datasets discoverable beyond the scope of the repository where they have been published. https://mlcommons.org/working-groups/data/croissant/ Thanks, Aubrie Aubrie Weyhmiller Research Scientist Renaissance Computing Institute (RENCI) Pronouns: she/her