Recording provenance of workflow runs with RO-Crate

Recording provenance of workflow runs with RO-Crate. Leo, S. A. C., Michael R. AND Rodríguez-Navas, L. A. S., Raül AND Kanitz, A. A. D. G., Paul AND Wittner, R. A. P., Luca AND Garijo, D. A. F., José M. AND Colonnelli, I. A. G., Matej AND Ohta, T. A. S., Hirotaka AND Capella-Gutierrez, S. A. d. W., & Renske AND Kinoshita, B. P. A. S. PLOS ONE, 19(9):1-35, Public Library of Science, 09, 2024.

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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.

@article{10.1371/journal.pone.0309210,
    doi = {10.1371/journal.pone.0309210},
    author = {Leo, Simone AND Crusoe, Michael R. AND Rodríguez-Navas, Laura AND Sirvent, Raül AND Kanitz, Alexander AND De Geest, Paul AND Wittner, Rudolf AND Pireddu, Luca AND Garijo, Daniel AND Fernández, José M. AND Colonnelli, Iacopo AND Gallo, Matej AND Ohta, Tazro AND Suetake, Hirotaka AND Capella-Gutierrez, Salvador AND de Wit, Renske AND Kinoshita, Bruno P. AND Soiland-Reyes, Stian},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Recording provenance of workflow runs with RO-Crate},
    year = {2024},
    month = {09},
    volume = {19},
    url = {https://doi.org/10.1371/journal.pone.0309210},
    pages = {1-35},
    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.},
    number = {9},

}

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