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Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv
2018
Zenodo
The automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Computationally driven data-intensive experiments using workflows enable Automation, Scaling, Adaption and Provenance support (ASAP). However, there are still several challenges associated with the effective sharing, publication and reproducibility of such workflows due to the incomplete capture of provenance and lack of interoperability between different
doi:10.5281/zenodo.1966881
fatcat:gnpjnqu6pzdtfe7d4nzvi2r44a