| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01074 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701074 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701074
Facilitating Scientific Reproducibility and Interoperability through CWL Integration in the Dirac Grid Middleware
1 EP Department, CERN, Geneva, Switzerland
2 Laboratoire Univers et Particules de Montpellier, CNRS/IN2P3, University of Montpellier, France
3 Département de Physique Nucléaire et Corpusculaire, University of Geneva, Switzerland
* e-mail: alexandre.boyer@cern.ch
Published online: 7 October 2025
In the wake of the reproducibility crisis that has underscored the critical need for verifiable scientific research, the integration of Common Workflow Language (CWL) into the Dirac grid middleware represents a significant leap forward. CWL facilitates the precise definition of computational workflows, ensuring that they are easily shareable and executable across diverse computational environments. This standardization allows scientific processes to be replicated without ambiguity. Its widespread adoption across major workload and workflow management systems emphasizes its effectiveness. Dirac, a comprehensive framework for managing computational tasks and workflows at different scales, serving a broad range of communities from diverse scientific fields, has traditionally utilized specialized descriptive languages, introducing complexity and barriers to interoperability and seamless workflow reproduction. By adopting CWL, this study aims to eliminate these barriers, standardizing the description of computational tasks and thereby enhancing their reproducibility and interoperability. By streamlining the interface for defining computational tasks within Dirac, we enable researchers to effortlessly transition workflows from local to grid-scale environments and foster compatibility with a broader ecosystem of scientific tools. This integration promises not only to mitigate the challenges posed by the reproducibility crisis but also to significantly lower the threshold for engaging with complex computational infrastructures, thus accelerating scientific discovery and innovation across multiple disciplines.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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