The Making of a Community Dark Matter Dataset with the National Science Data Fabric

TL;DR

This paper details the creation of an accessible, open, and reproducible dark matter dataset from proprietary formats, enhancing interdisciplinary research and machine learning applications in dark matter physics.

Contribution

It introduces a novel workflow using NSDF services to convert proprietary dark matter data into open formats and provides tools for broader scientific use.

Findings

Converted proprietary data into open, multi-resolution IDX format

Developed a web dashboard for data visualization

Released a Python CLI for scalable analysis and machine learning

Abstract

Dark matter is believed to constitute approximately 85 percent of the universes matter, yet its fundamental nature remains elusive. Direct detection experiments, though globally deployed, generate data that is often locked within custom formats and non-reproducible software stacks, limiting interdisciplinary analysis and innovation. This paper presents a collaboration between the National Science Data Fabric (NSDF) and dark matter researchers to improve accessibility, usability, and scientific value of a calibration dataset collected with Cryogenic Dark Matter Search (CDMS) detectors at the University of Minnesota. We describe how NSDF services were used to convert data from a proprietary format into an open, multi-resolution IDX structure; develop a web-based dashboard for easily viewing signals; and release a Python-compatible CLI to support scalable workflows and machine learning applications. These contributions enable broader use of high-value dark matter datasets, lower the barrier to entry for new collaborators, and support reproducible, cross-disciplinary research.