WorkflowHub: a registry for computational workflows
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Abstract (arXiv)
The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides 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). 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 700 workflows registered.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Oct 2024.
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Citing papers per year: 2026: 6.
6 papers.
- 2026R-LAM: Reproducibility-Constrained Large Action Models for Scientific Workflow Automationcited by 2
- 2026Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflowscited by 1
- 2026Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Codecited by 0
- 2026SetGo: Metadata Readiness for Scientific AI Datasetscited by 0
- 2026CURATE: Leveraging LLM Agents to Compose, Catalog, and Deploy Reproducible Workflowscited by 0
- 2026Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Datacited by 0
- 2026Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data11 Aug 2026
- 2026CURATE: Leveraging LLM Agents to Compose, Catalog, and Deploy Reproducible Workflows4 Aug 2026
- 2026SetGo: Metadata Readiness for Scientific AI Datasets10 Jul 2026
- 2026Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Code9 Mar 2026
- 2026Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows6 Mar 2026
- 2026R-LAM: Reproducibility-Constrained Large Action Models for Scientific Workflow Automation12 Jan 2026