# Applying the FAIR Principles to computational workflows

arXiv: https://arxiv.org/abs/2410.03490 · HTML: https://mlsanity.com/paper/2410.03490/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2024.

## Built on by (most cited first, 10 of 10)

- [The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science](https://mlsanity.com/paper/2509.09915/) (2025)
- [Fantastic Scientific Agents and How to Build Them: AgentBuild for Rietveld Refinement](https://mlsanity.com/paper/2606.12834/) (2026)
- [Automated Data Readiness for Scientific AI](https://mlsanity.com/paper/2607.02771/) (2026)
- [Criteria-first, semantics-later: reproducible structure discovery in image-based sciences](https://mlsanity.com/paper/2602.15712/) (2026)
- [MetaConfigurator: AI-Assisted RDF Authoring from JSON Data](https://mlsanity.com/paper/2606.07094/) (2026)
- [Twelve quick tips for designing AI-driven HPC workflows](https://mlsanity.com/paper/2606.07491/) (2026)
- [Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration](https://mlsanity.com/paper/2606.28856/) (2026)
- [SetGo: Metadata Readiness for Scientific AI Datasets](https://mlsanity.com/paper/2607.22677/) (2026)
- [CURATE: Leveraging LLM Agents to Compose, Catalog, and Deploy Reproducible Workflows](https://mlsanity.com/paper/2608.04270/) (2026)
- [Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data](https://mlsanity.com/paper/2608.11022/) (2026)

## Abstract (arXiv)

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.