Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem. Persistent, globally unique identifiers, resolvable on the Web, and associated with a set of additional descriptive metadata, are foundational to FAIR data. Here we describe some basic principles and exemplars for their design, use and orchestration with other system elements to achieve FAIRness for digital research objects.