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In recent years, digital object management practices to support findability,accessibility, interoperability, and reusability (FAIR) have begun to beadopted across a number of data-intensive scientific disciplines. These digitalobjects include datasets, AI models, software, notebooks, workflows,documentation, etc. With the collective dataset at the Large Hadron Colliderscheduled to reach the zettabyte scale by the end of 2032, the experimentalparticle physics community is looking at unprecedented data managementchallenges. It is expected that these grand challenges may be addressed bycreating end-to-end AI frameworks that combine FAIR and AI-ready datasets,advances in AI, modern computing environments, and scientific datainfrastructure. In this work, the FAIR4HEP collaboration explores theinterpretation of FAIR principles in the context of data and AI models forexperimental high energy physics research. We investigate metrics to quantifythe FAIRness of experimental datasets and AI models, and provide open sourcenotebooks to guide new users on the use of FAIR principles in practice.

FAIR Principles for data and AI models in high energy physics research and education