WorkflowHub: a registry for computational workflows

A new paper in Nature Scientific Data describes a public and inclusive registry dedicated specifically to the sharing of computational workflows: WorkflowHub.

Australian BioCommons’ Research Community Engagement Lead (Proteins / Metabolites / Workflows), Dr Johan Gustafsson, is the lead author of a stellar group of international experts. They have worked hard on their shared passion for a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR).

The WorkflowHub registry is designed to allow any scientist, regardless of expertise level, to contribute and share computational workflows. It indexes workflows from any scientific domain, in any format, in any workflow language, regardless of whether it uses a workflow management system and supports users to increase the FAIRness of their workflows.

By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 800 workflows registered.

The paper describes how WorkflowHub’s structure, design, standards, community engagement, and continued evolution support:

1) collaboration, sharing and credit for workflow developers, projects, and consortia;

2) integration with added-value services, platforms, and capabilities that support the workflow life cycle (i.e. creation, version control, execution, maintenance, reuse and citation); and

3) wizards and inbuilt features that ease the process of sharing workflows alongside the constellation of associated digital artefacts that give a workflow its scientific context.

Gustafsson, O.J.R., Wilkinson, S.R., Bacall, F. et al. WorkflowHub: a registry for computational workflows. Sci Data 12, 837 (2025). https://doi.org/10.1038/s41597-025-04786-3