Nexus-DM delivers state-of-the-art tools for accessing and analyzing dark matter data, including a web-based event visualization dashboard, a command-line interface (CLI) for data access, and a library for data manipulation. By combining ease of access with scientific depth, the initiative empowers the community to accelerate discovery in dark matter research.
To get up and running quickly, follow the step-by-step guides found in the Nexus-DM documentation
- Web-based visualization: Explore midas files, isolate detector channels, and examine detailed metadata such as trigger types and timestamps, all within the browser with the NSDF Dark Matter Dashboard.
- Easy data access via CLI: Effortlessly search, and download dark matter data files using the NSDF Dark Matter CLI.
- Workflow integration: Leverage NSDF Dark Matter Library to load and manipulate dark matter data, enabling seamless integration into workflow pipelines, including machine learning.
- Search events: Search for specific events using the autocomplete input.
- Visualization: Visualize channel waveforms from multiple detectors.
- Channel isolation: Select or deselect channels from one or more detectors.
- Event metadata: View information about the trigger type, readout type, and timestamp of the events.
The NSDF Dark Matter Dashboard provides a web-based interface to visualize and explore dark matter data all within the browser. Learn about the controls that make the dashboard from dashboard guide.
The NSDF Dark Matter CLI offers a pool of operations to access the R76 dark matter dataset. The CLI can be used as part of a workflow to download data which can then be analyzed with the NSDF Dark Matter Library. Learn about the CLI from CLI guide.
The NSDF Dark Matter Library offers a pool of operations to manipulate dark matter data from getting event metadata to detector channel data acquisition. Learn about the library usage in library guide.
Contributions are always welcome, no matter how large or small. Read our contributing guide to learn on how you can start to contribute to the NSDF Dark Matter Initiative.
- Michela Taufer, Heberth Martinez, Aashish Panta, Paula Olaya, Jack Marquez, Amy Gooch, Giorgio Scorzelli and Valerio Pascucci. ‘Leveraging National Science Data Fabric Services to Train Data Scientists’. In: Proceedings of the 2024 Workshop on Education for High-Performance Computing (EduHPC)-Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SC24). Atlanta, GA, USA: IEEE Computer Society, 2024, https://doi.ieeecomputersociety.org/10.1109/SCW63240.2024.00053.
- Paula Olaya, Jakob Luettgau, Camila Roa, Ricardo Llamas, Rodrigo Vargas, Sophia Wen, I-Hsin Chung, Seetharami Seelam, Yoonho Park, Jay Lofstead, and Michela Taufer. Enabling Scalability in the Cloud for Scientific Workflows: An Earth Science Use Case. In Proceedings of IEEE CLOUD, pages 1–10, Chicago, IL, USA, June 2023. IEEE Computer Society, https://doi.org/10.1109/CLOUD60044.2023.00052.
- Laboy, Gabriel; Ashworth, Jay; Olaya, Paula; Martinez, Heberth; Marquez, Jack; Panta, Aashish; Scorzelli, Giorgio; Taufer, Michela; Pascucci, Valerio, 2024, "NSDF OpenVisus Tutorial - GEOtiled Terrain Parameters", https://doi.org/10.7910/DVN/B33F4X, Harvard Dataverse, V1.
- Llamas, R., L. Valera, P. Olaya, M. Taufer, R. Vargas (2022). 1-km soil moisture predictions in the United States with SOMOSPIE, HydroShare, https://doi.org/10.4211/hs.79162a679f8a4273be582561a5504f68.
If you are referencing Nexus-DM in a publication, please cite the following paper:
- Amy Roberts, Jack Marquez, Kin Hong NG, Kitty Mickelson, Aashish Panta, Giorgio Scorzelli, Amy Gooch, Prisca Cushman, Matthew Fritts, Himangshu Neog, Valerio Pascucci, Michela Taufer. The Making of a Community Dark Matter Dataset with the National Science Data Fabric. arXiv preprint arXiv:2507.13297 (2025).
On GitHub, you can copy this citation in APA or BibTeX format via the "Cite this repository" button. Or, see the comments in CITATION.cff for the raw BibTeX.
Copyright (c) 2025, NSDF
The NSDF SLAC dashboard is distributed under terms of the Apache License, Version 2.0 with LLVM Exceptions.
See LICENSE for more details.
This research is supported by the National Science Foundation (NSF) awards #2138811, #2103845, #2334945, #2138296, and #2331152. The work presented here is partly obtained using resources from ACCESS TG-CIS210128. We thank the Scientist Cloud Storage.