ICR FAIR Data - FOXDEN: Advancing AI-Ready Data Exchange Through CHESS–MagLab Collaboration

Published November 17, 2025

Figure created with the assistance of AI-based image generation (OpenAI DALL·E)

Scientists need data that’s easy to find, understand, and reuse—both by people and computers. This kind of data is called FAIR, and it’s especially important now that artificial intelligence (AI) is helping with research. FAIR data helps AI work better and faster, even powering things like labs that run experiments automatically. To make this happen, researchers at MagLab and Cornell are building tools that organize and describe data clearly, making it easier for scientists to share, explore, and discover new things.

What is the development?

The MagLab is partnering with researchers at the Cornell High Energy Synchrotron Source (CHESS) to implement FOXDEN (1) — their FAIR Open-Science Extensible Data Exchange Network. This software helps manage, discover, and track data and metadata that are Findable, Accessible, Interoperable, and Reusable (FAIR). By using emerging metadata standards like Science-on-schema.org (2) and Croissant (3), the system improves data discoverability and makes it easier to apply artificial intelligence (AI) techniques.

Why is this important?

Making data FAIR through detailed metadata — information that describes the data, such as experimental parameters — is key to ensuring scientific results are reproducible. It also makes data AI-ready, meaning it can be understood and used by computer algorithms and AI agents. This enables automation of tasks like experimental design and data analysis, helping scientists work faster and more efficiently.

Who did the research?

David S. Butcher, Lissa C. Anderson

Ion Cyclotron Resonance Facility, National MagLab, Tallahassee FL

How does the MagLab benefit from working with CHESS?

CHESS has already developed a robust suite of services that support advanced data and metadata management, visualization, analysis, and publishing to external repositories. By building on these tools, the MagLab can offer similar capabilities to its users without starting from scratch. This collaboration between facilities with different specialties — high-energy radiation at CHESS and high magnetic fields at the MagLab — highlights the power of cross-disciplinary and inter-institutional partnerships in advancing scientific research.

Citations

CHESS Computing: FOXDEN. https://github.com/CHESSComputing/FOXDEN
Canty, R. B. et al. Science Acceleration and Accessibility with Self-Driving Labs. Nat Commun 2025, 16 (1), 3856. https://doi.org/10.1038/s41467-025-59231-1.
Science-on-Schema.Org v1.3.2, 2024. https://doi.org/10.5281/ZENODO.7884538.
Akhtar, M. et al. Croissant: A Metadata Format for ML-Ready Datasets. https://doi.org/10.1145/3650203.3663326.