In order to investigate RQ3, and considering important parts of the FAIR principles are Reuse and provenance, this chapter examines in closer details how FAIR Digital Objects and RO-Crate can be used with Computational Workflows.
Section 5.1 proposes that tools in computational workflows, when wrapped as interoperable building blocks, can be considered as FAIR Digital Objects, with a use case from biomolecular simulation.
Sections 5.2 and 5.3 explore how FDOs and Research Objects can be constructed incrementally using computational workflows, with a use case from specimen digitization in natural history collections.
Section 5.4 presents a profile of RO-Crate to capture workflow execution provenance, with incremental granularity levels and six workflow engine implementations. Use cases include machine learning-aided tumour detection and compatibility with PROV approaches.
Supplementary materials that may assist readers of this chapter provide further details on FAIR Computational Workflows [Goble 2020], WorkflowHub [Goble 2021], Common Workflow Language [Crusoe 2022] and making a software tool workflow ready [Brack 2022a].
On the aspects of workflow provenance, recommended reading in supplementary materials covers CWLProv [Khan 2019], RO-Crate in Galaxy [De Geest 2022] and Common Provenance Model [Wittner 2020, Wittner 2023a].