NEXUS-DM:
Networked Exchange for Dark Matter
“Connect data, code, and community"

Funded by the National Science Foundation (NSF) under grant numbers 2531754, 2138811, 2331152,

About NEXUS-DM

NEXUS-DM is a FAIR-aligned Dark Matter Data Commons designed to close critical gaps in transparency, reproducibility, and accessibility of dark matter experimental data. Its modular components include:
Curated Data Repository: End-to-end infrastructure for storing and accessing raw and processed dark matter datasets, starting with CDMS and DELight data.
Reusable Interfaces: User-friendly command-line and Python APIs that integrate with NSDF and Pegasus for scalable AI/ML workflows.
AI/ML-Enhanced Workflows: Demonstrations of GAN-based bias mitigation, denoising, and calibration pipelines for reproducible and explainable analysis.
Education and Training: Open tutorials, Jupyter notebooks, and an ACCESS Affinity Group to foster reproducibility and workforce development.

NEXUS-DM and FAIR

NEXUS-DM operationalizes the FAIR principles (Findable, Accessible, Interoperable, Reusable) to maximize discovery and ensure long-term stewardship:
FAIR Digital Objects (FDOs): Self-describing containers encapsulating data, metadata, and workflows, ensuring transparency, traceability, and machine-actionable reuse.
Open Science Integration: Alignment with NSF’s National Science Data Fabric (NSDF) and ACCESS cyberinfrastructure to provide sustainable, interoperable, and AI-ready data services.
Ready to dive in? Join us and explore the full power of our software, data, and documentation! Discover more here:

Recent Publications

Amy Roberts, Jack Marquez, Kin Hong NG, Kitty Mickelson, Aashish Panta, Giorgio Scorzelli, Amy Gooch, Prisca Cushman, Matthew Fritts, Himangshu Neog, Valerio Pascucci, and Michela Taufer. The Making of a Community Dark Matter Dataset with the National Science Data Fabric. arXiv preprint arXiv:2507.13297, 2025. [link]
Michela Taufer, Heberth Martinez*, Jakob Luettgau*, Lauren Whitnah, Giorgio Scorzelli, Pania Newel, Aashish Panta, Timo Bremer, Doug Fils, Christine R. Kirkpatrick, and Valerio Pascucci. Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric. IEEE Computing in Science and Engineering (CiSE), 25(5):39–47, 2023. 10.1109/MCSE.2024.3363828.