Preprint Open Access
The automation of data analysis in the form of scientific workflows is a widely adopted practice in many fields of research nowadays. 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, understandability and reproducibility of such workflows due to the incomplete capture of provenance and the dependence on particular technical (software) platforms. This paper presents CWLProv, an approach for retrospective provenance capture utilizing open source community-driven standards involving application and customization of workflow-centric Research Objects (ROs).
The ROs are produced as an output of a workflow enactment defined in the Common Workflow Language (CWL) using the CWL reference implementation and its data structures. The approach aggregates and annotates all the resources involved in the scientific investigation including inputs, outputs, workflow specification, command line tool specifications and input parameter settings. The resources are linked within the RO to enable re-enactment of an analysis without depending on external resources.
The workflow provenance profile is represented in W3C recommended standard PROV-N and PROV-JSON format to capture retrospective provenance of the workflow enactment. The workflow-centric RO produced as an output of a CWL workflow enactment is expected to be interoperable, reusable, shareable and portable across different plat-
This paper describes the need and motivation for CWLProv and the lessons learned in applying it for ROs using CWL in the bioinformatics domain.