Applying the FAIR Principles to computational workflows

Abstract

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

Fig. 1

A workflow specification formally specifies the data flow and/or execution control between components such as reference datasets, executable scripts, and AI/ML models. These components can be reused as parts of other workflow specifications. Using a workflow specification means instantiating it as a workflow run – typically by executing it within a workflow management system – by feeding it inputs like parameters and experimental/observational data as required. During execution, components will be used in some order, finally resulting in output data as well as logs and provenance metadata.