FAIRsharing record for: Croissant Format Specification

This FAIRsharing record describes: The Croissant metadata format simplifies how data is used by ML models. It provides a vocabulary for dataset attributes, streamlining how data is loaded across ML frameworks such as PyTorch, TensorFlow or JAX. In doing so, Croissant enables the interchange of datasets between ML frameworks and beyond, tackling a variety of discoverability, portability, reproducibility, and responsible AI (RAI) challenges. Croissant was developed collaboratively by a community from industry and academia, as part of the MLCommons effort. The Croissant format doesn't change how the actual data is represented (e.g., image or text file formats) — it provides a standard way to describe and organize it. Croissant builds upon schema.org, the de facto standard for publishing structured data on the Web, which is already used by over 40M datasets. Croissant augments it with comprehensive layers for ML relevant metadata, data resources, data organization, and default ML semantics.