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PreprintarXiv licencearXiv

Toward FAIR and AI-Ready Data: An Assessment of the GlueX Experiment's Data Ecosystem at Jefferson Lab

  • Anil Panta
  • Brad Sawatzky
  • Casey Morean
  • Dmitry Romanov
  • Douglas Higinbotham
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Abstract

Department of Energy programs increasingly require that experimental data be not only preserved but usable by artificial intelligence (AI) systems, yet no accepted standard defines when a dataset is AI-ready. We argue that data must be FAIR (Findable, Accessible, Interoperable, Reusable) before they can be AI-ready, and that AI readiness is a distinct property requiring its own assessment. Using the GlueX experiment in Hall D at Jefferson Lab as a pilot, we map the data ecosystem, assess it against FAIR, and define an AI-readiness framework whose criteria are backed by deterministic metrics. We also evaluate existing validation software to separate what can be assessed with off-the-shelf tools from what requires domain-specific development. The assessment yields a set of findings, each paired with a recommendation, some of which are already in production or prototyped and others planned as next steps.

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Cite

BibTeXRIS
BibTeX
@misc{panta2026toward,
  title = {{Toward FAIR and AI-Ready Data: An Assessment of the GlueX Experiment's Data Ecosystem at Jefferson Lab}},
  author = {Anil Panta and Brad Sawatzky and Casey Morean and Dmitry Romanov and Douglas Higinbotham},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.34987},
  eprint = {2609.34987},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.34987},
}
RIS
TY  - UNPB
TI  - Toward FAIR and AI-Ready Data: An Assessment of the GlueX Experiment's Data Ecosystem at Jefferson Lab
AU  - Anil Panta
AU  - Brad Sawatzky
AU  - Casey Morean
AU  - Dmitry Romanov
AU  - Douglas Higinbotham
PY  - 2026
DA  - 2026/09/28
PB  - arXiv
DO  - 10.48550/arxiv.2609.34987
UR  - https://arxiv.org/abs/2609.34987
AB  - Department of Energy programs increasingly require that experimental data be not only preserved but usable by artificial intelligence (AI) systems, yet no accepted standard defines when a dataset is AI-ready. We argue that data must be FAIR (Findable, Accessible, Interoperable, Reusable) before they can be AI-ready, and that AI readiness is a distinct property requiring its own assessment. Using the GlueX experiment in Hall D at Jefferson Lab as a pilot, we map the data ecosystem, assess it against FAIR, and define an AI-readiness framework whose criteria are backed by deterministic metrics. We also evaluate existing validation software to separate what can be assessed with off-the-shelf tools from what requires domain-specific development. The assessment yields a set of findings, each paired with a recommendation, some of which are already in production or prototyped and others planned as next steps.
ER  -