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Recording provenance of workflow runs with RO-Crate.
- Source :
-
PloS one [PLoS One] 2024 Sep 10; Vol. 19 (9), pp. e0309210. Date of Electronic Publication: 2024 Sep 10 (Print Publication: 2024). - Publication Year :
- 2024
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Abstract
- Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and Schema.org to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain.<br />Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: M.R.C. is a Member of the BioCompute Technical Steering Committee. S.S.R. was a Member of the BioCompute Technical Steering Committee until May 2023. S.S.R. in 2020 did a consultancy from George Washington University on BCO and RO-Crate. This does not alter our adherence to PLOS ONE policies on sharing data and materials.<br /> (Copyright: © 2024 Leo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Subjects :
- Software
Machine Learning
Reproducibility of Results
Workflow
Subjects
Details
- Language :
- English
- ISSN :
- 1932-6203
- Volume :
- 19
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- PloS one
- Publication Type :
- Academic Journal
- Accession number :
- 39255315
- Full Text :
- https://doi.org/10.1371/journal.pone.0309210