Introducing the FAIR² Specification
FAIR
AI-Ready
Responsible AI Aligned
Context-Rich
FAIR² (FAIR Squared™) extends the FAIR principles with a formal specification that makes research data AI-ready, responsibly governed, and optimized for deep scientific reuse. It provides a context-rich representation of each dataset, ensuring rigor, reproducibility, and interoperability. Compatible with MLCommons Croissant, FAIR² integrates with TensorFlow, JAX, and PyTorch, enabling AI-driven analysis and broad data sharing on platforms like Kaggle and Hugging Face.
An initial release of the FAIR² Specification will be available soon.
An initial release of the FAIR² Specification will be available soon.