Work

FOXDEN: FAIR Services for AI-Ready Scientific Datasets

Year: 2026

Type: preprint

Abstract: Scientific datasets are most compatible with AI workflows when they are described by rich, machine-readable metadata and provenance information, following the FAIR guiding principles. The FAIR Open-Science Extensible Data Exchange Network (FOXDEN) is a set of cyberinfrastructure building blocks developed at the Cornell High Energy Synchrotron Source (CHESS) for annotating raw, reduced, and analyzed datasets with both structured and unstructured metadata and provenance records. It also allows researchers to publish these records with Digital Object Identifiers (DOIs) to create AI-ready datasets. We describe FOXDEN's architecture and its deployment at CHESS and at the National High Magnetic Field Laboratory, where it serves as a foundation for future agentic scientific workflows.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.03105

Authors: Kuznetsov, Valentin, Sun, Werner M., Soloway, Keara, Verberg, Rolf, Shanks, Katherine S., Sarker, Suchismita, Butcher, David S., Nygren, Kelly E.

Institutions: Cornell University, Cornell High Energy Synchrotron Source, National High Magnetic Field Laboratory

Topic: Research Data Management Practices

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National High Magnetic Field Laboratory, National Institutes of Health, National Institute of General Medical Sciences, Division of Materials Research, Division of Chemistry, High Magnetic Field Laboratory, Chinese Academy of Sciences, Air Force Research Laboratory

Awards: G1019936685, G11703650, G3316604223, G3937223104, G4965881104, Mid-scale: Operations of the Center for High Energy X-ray Science (CHEXS), G6827444803, National High Magnetic Field Laboratory Renewal 2023-2027, G742574937, G7568170897, G8152425740, G8395108435

OpenAlex ID: W7208734612

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