FAIRy

Open Science

FAIRy-core is built to support open science and FAIR data sharing. Here's what we share, how we engage with the community, and how FAIRy supports open science principles.

⚠ Early alpha: Interfaces may change before v1.0

What we share

FAIRy-core is open source and freely available. We share:

  • Public repositories: The FAIRy-core validator codebase is available on GitHub for researchers and developers to use, modify, and contribute to. We welcome community contributions through pull requests, issue reports, and discussions. We maintain shared rulepacks for common repositories (GEO, Zenodo, ENA) and data types, and we're building processes to review and merge community-submitted rulepacks so the library grows with real-world use cases.
  • Example rulepacks: Starter rulepacks for common repositories (GEO, Zenodo, ENA) and data types, so you can see how validation rules are structured.
  • Demo datasets: Sample datasets that demonstrate FAIRy's validation capabilities, including GEO-bulk sequencing examples (coming soon).
  • Talk slides and notes: Presentations from community events, workshops, and conferences, including our BIOFAIR Open Mic contributions.

Licensing model

We use a dual-license and permissive content model to balance open science goals with sustainable development:

  • FAIRy-core (CLI + validators): Licensed under AGPL-3.0-only. This ensures the core validator remains open while allowing commercial licensing options for organizations that require it.
  • Rulepack schema & example rulepacks: Licensed under CC0-1.0 (public domain). This encourages community rulepack sharing and avoids license contamination concerns.
  • Sample datasets: Licensed under CC BY-4.0, allowing reuse with attribution.
  • Hosted UI / orchestration: Proprietary or source-available (when available).

Commercial licensing available

Available for organizations that cannot adopt AGPL. Contact hello@datadabra.com for details.

Community & Initiatives

We actively participate in open science communities and initiatives:

  • BIOFAIR Open Mic: Regular participation in BIOFAIR community discussions, sharing FAIRy's approach to local-first dataset pre-checking and how it supports the BIOFAIR Data Network roadmap.
  • Pilots and collaborations: Working with research institutions, core facilities, and data stewards to develop domain-specific rulepacks and validate FAIRy's approach in real-world settings. Request a pilot scope →
  • Talks and events: Presenting at conferences, workshops, and community gatherings to share learnings and gather feedback. View our talks →
  • Working groups: Engaging with standards bodies and working groups focused on FAIR data, metadata quality, and data submission workflows.

If you're running a pilot, organizing an event, or part of a working group that could benefit from FAIRy, get in touch.

Pilot program

Note: The pilot program is not permanent. We're currently accepting a limited number of pilot engagements.

During a pilot, we work with you to encode 5-7 of your intake rules into a custom rulepack. You receive the rulepack and can use it freely. If you want ongoing support, additional rulepack development, dashboards, or other features, we offer various paid engagement options.

Contact us:

How FAIRy supports open science

Local-first privacy

Analysis stays on your machine. FAIRy runs entirely on your computer or within your institution's network. Nothing is uploaded to external servers. This means researchers can validate sensitive or pre-publication data without privacy concerns, and institutions can maintain full control over their data workflows.

Reproducibility

Attestations with hashes and rulepack versions. Every validation run produces an attestation file that documents exactly what was checked, when, and under which rulepack version. File hashes ensure you can verify data integrity, and rulepack versioning means you can reproduce validation results even as rules evolve. This supports reproducible research and transparent data quality assessment.

Example attestation snippet:

{
  "attestation": {
    "fairy_version": "0.1.0",
    "rulepack": "GEO-SEQ-BULK/v0_1_0.json",
    "timestamp": "2025-11-15T10:30:00Z",
    "submission_ready": false
  },
  "file_hashes": {
    "samples.tsv": "sha256:abc123...",
    "files.tsv": "sha256:def456..."
  },
  "findings": [...]
}

Transparency

Pass/warn/fail reports with fix logs. FAIRy generates clear, human-readable readiness reports that show exactly what needs to be fixed and why. No black boxes — you can see the validation logic, understand the rules, and trace every issue back to a specific requirement. This transparency helps researchers learn FAIR data principles and makes the validation process educational, not just a gate.

Sample readiness report:

✓ PASS: All required metadata fields present
⚠ WARN: Date format should be ISO 8601 (found: "11/15/2025")
✗ FAIL: Missing required field: "sample_id"

Resources

Quick links to FAIRy-core resources and materials:

ResourceLink
GitHub (FAIRy-core)View repository →
GitHub (FAIRy-lab)Streamlit UI with project workspaces →
Examples / case studiesGEO-bulk and other examples →
Slides / notesBIOFAIR deck →
Institution Kit (PDF)SBOM, security summary, deployment options →

Last updated: November 2025

Contributing

Contributions welcome! See our contributing guidelines for details on how to submit pull requests, report issues, and contribute rulepacks.

Security

Report security issues to hello@datadabra.com (per SECURITY.md). Please do not open public GitHub issues for security vulnerabilities.

How to cite FAIRy

If you use FAIRy in your research, please cite:

APA Style

Slotnick, J. (2025). FAIRy Core (Version 0.1) [Computer software]. Datadabra.
https://github.com/yuummmer/fairy-core

BibTeX

@software{fairy2025,
  author = {Slotnick, Jennifer},
  title = {FAIRy Core},
  year = {2025},
  version = {0.1},
  publisher = {Datadabra},
  url = {https://github.com/yuummmer/fairy-core}
}

Get started with FAIRy

Download FAIRy-core →Request a Pilot Scope →