rseng-fair-ml

Covers applying FAIR principles to machine learning artifacts: making models findable and reusable with model cards and rich repository metadata, documenting datasets with Croissant and datasheet-style records, licensing models and weights, linking the model-data-code-paper cluster with persistent identifiers, and the RDA FAIR4ML metadata direction. Use when a project trains, fine-tunes, publishes or reuses ML…