FAIR research software

Use this skill when someone wants their software to be Findable, Accessible, Interoperable, and Reusable, or when reviewing how well a project meets those principles. FAIR is a set of principles for increasing the visibility and usefulness of research to others; the data principles from 2016 now extend to software, workflows, and machine-learning projects. Treat FAIR as one subset of overall software quality: it ensures software can be discovered, understood, and rerun by others (or by the author months later), but it says nothing about whether the software is correct - pair it with testing and the other quality dimensions.

Work through the four principles below. Each maps to concrete actions; recommend the ones a project is missing, and explain why each matters as you go. Many actions serve more than one principle, so a single change (good metadata, a DOI, a license) often lifts several at once.

Make software Findable

Software and its metadata must be easy to discover by humans and machines. Recommend:

• Write a machine-readable description (metadata) of the software so search engines and tools can index it. Use a standard such as CodeMeta rather than an ad-hoc format, following the Research Software Metadata Guidelines.
• Put the code in a public repository (for example GitHub or GitLab) and, ideally, register it in a general-purpose or domain-specific registry (for example bio.tools for the biosciences). The Awesome Research Software Registries list helps pick one by domain, country, or language.
• Mint a persistent identifier so the software can be cited and reliably located: a DOI from Zenodo or FigShare, or a SoftWare Heritage persistent identifier (SWHID) from Software Heritage. Persistent identifiers also earn the authors credit through citable references.
• Publish to language-specific repositories where relevant - PyPI for Python packages, CRAN for R - so the software surfaces in the tools users already search.

Make software Accessible

Once found, the software and its metadata must be retrievable by standard protocols, free, and legally usable. Recommend:

• Ensure people can obtain a copy over standard communication protocols (HTTP, FTP, and the like) - a plain, documented download or clone path, not a personal request.
• Keep the code and its metadata available even after active development stops, including earlier versions. Archive releases (for example to Zenodo) so a deposited, immutable snapshot outlives the live repository.
• Keep metadata retrievable even where the software itself is gated, so the record of what the software is and where it lives never disappears.

Make software Interoperable

When it interacts with other software, it should do so through standardised formats, protocols, and APIs. Recommend:

• Use community-agreed standard formats for inputs and outputs, and for metadata (for example CodeMeta), instead of bespoke formats that lock data in.
• Communicate with other tools via standard protocols and documented APIs, so the software slots into larger pipelines rather than becoming a dead end.
• Document the functionality and the interaction surface - for example the command-line interface - so another tool's author knows how to drive it.

Make software Reusable

Software should be usable (it can be executed) and reusable (it can be understood, modified, built upon, or incorporated into other software). Recommend:

• Document the software: what it does, how to install it, and how to run it, so others can understand and extend it. Nothing else does more for reuse.
• Give it a license that clearly states how it may be reused. Point to an open-source license guide or Choose an open source license to pick one; without a license, others legally cannot reuse the code even if it is public.
• State how to cite the software (for example a CITATION.cff file) so reusers can give credit.
• Follow software-development best practices more broadly: use a conventional project structure and coding conventions so the code is readable and understandable by people, not only runnable by machines.

FAIR within software quality

Position FAIR correctly when advising: quality software is defined by many aspects - correctness, performance, maintainability, usability, robustness, reproducibility, and more. Reproducibility hinges on FAIR: if code and metadata are not findable or accessible, no one can rerun the work; if they are not interoperable or reusable, no one can adapt or verify it. A genuinely high-quality package satisfies both the classic engineering criteria (tests, style, documentation, performance) and the FAIR principles. Be explicit that FAIR does not guarantee the software works or is useful - only that others can discover, understand, and exercise it - so always pair FAIR advice with testing.

Assess FAIRness

When asked to evaluate a repository, use an assessment tool rather than judging by hand, and frame the result as diagnostic, not a verdict. These assessments make quality aspects visible and guide improvement; they are not meant to score, rank, or discredit authors or their software. Tools to reach for:

• FAIR software checklist - a self-assessment tool from the Australian Research Data Commons and partners.
• howfairis - a command-line tool that checks a repository against the five FAIR recommendations.
• Research Software FAIRness Checks - a CLI that evaluates a GitHub or GitLab repository automatically.
• FAIRsoft Evaluator - assesses a tool's FAIRness from its metadata.
• CODECHECK - independent re-execution of the computations behind a paper.
• Common metrics for research software - shared metrics for scoring each FAIR4RS principle.

Present findings as strengths plus areas to improve, and turn each gap into one of the concrete actions above.

Working with this skill

The generated references.md beside this file lists the source material and pointers:

• references.md - verified Learn more pointers

Learn more (verified):

• https://www.gofair.foundation/fair-principles - the FAIR principles explained
• https://doi.org/10.1038/sdata.2016.18 - original FAIR Guiding Principles paper
• https://doi.org/10.1038/s41597-022-01710-x - FAIR principles for research software (FAIR4RS)
• https://fair-software.eu - five recommendations for FAIR software
• https://fairsoftwarechecklist.net - FAIR software self-assessment checklist

Related skills

Check whether any of these applies before moving on:

• rseng-archiving - accessibility beyond active development
• rseng-citation-metadata - metadata and identifiers implement findability
• rseng-fair-ml - FAIR extended to ML artifacts
• rseng-fairguard - automated FAIR4RS scoring
• rseng-licensing - license implements reusability
• rseng-software-reuse - registering software for findability