Make research data submission-ready—before handoff.
FAIRy validates and packages datasets for labs, cores, and institutions. Local-first, open, and fast.
Piloting with university cores & collections teams.
| Check | Status | Next Step |
|---|---|---|
| Sample IDs present | PASS | No action needed |
| Dates in ISO format | PASS | No action needed |
| Contact email valid | WARN | Verify email format |
| File naming convention | FAIL | Rename files per standard |
| Required metadata fields | PASS | No action needed |
One-click pre-flight: human report + machine JSON + attestation.
🔒Local-first privacy: No data leaves your environment. All processing happens on your machines. No accounts, no cloud, no external transfer.
Who it's for
Stop spending hours chasing missing fields and renaming files
People hand you garbage and you spend hours chasing missing fields and renaming files. You need proof you did QC before you archive, publish, send to a journal, or report to a grant panel.
FAIRy runs locally and generates a one-page readiness sheet (PASS / WARN / FAIL + how to fix) that labs can attach when they hand off a dataset.
FAIRy gives institutions a repeatable pre-intake check — with both a human-readable fix list and a machine-readable attestation — so their data can confidently join larger integrated networks without weeks of one-off curator triage.
Consistent intake rules
Turn your "we can't accept this without X" policies (sample IDs, contact email, ISO dates, file naming, permit/embargo flags) into a reusable preflight check. Everyone gets held to the same standard.
One-page readiness sheet
FAIRy produces a PASS / WARN / FAIL summary with plain-language "how to fix" instructions. You can send it back to the lab / PI instead of rewriting the same email.
Local-only by design
FAIRy runs inside your environment. No raw data, filenames, locations, or metadata are uploaded. No accounts, no credentials, no cloud.
How we work with institutions
We offer FAIRy Core (no-cost tooling for researchers) and Institutional Pilots (scoped engagements for data stewards and core facilities). During a pilot, we encode your intake rules into a rulepack, generate readiness reports, and produce attestation files. You keep everything — the rulepack is yours.
Some groups maintain their own rulepacks; others prefer ongoing support. Both paths work.
Learn more about institutional offerings →Working solo? Get started free with FAIRy-core →
Don't get your dataset bounced by GEO, Zenodo, or your core facility
Tired of being told "fix your metadata" with no specifics? FAIRy runs locally and tells you exactly what's missing so you don't get yelled at.
Get a checklist before you submit — PASS / WARN / FAIL, why it matters, and how to fix it. This is what your curator is going to ask for.
For labs, cores, and institutions
This is what your curator is going to ask for
FAIRy runs locally and tells you exactly what's missing and how to fix it — no vague "fix your metadata" messages. Get a checklist before you submit so you don't get bounced.
View the full sample report →
How FAIRy runs in your environment
Here's exactly how this would work on your machine if you said yes.
1. Run it locally on your machine
During the pilot, you run FAIRy on a folder or metadata sheet.
FAIRy checks the dataset against your required fields and naming rules, and generates:
- a one-page Submission Readiness Report (PASS / WARN / FAIL + how to fix), and
- a machine-readable Attestation file (timestamp, rulepack version, file hashes).
fairy validate /path/to/dataset --out out/All processing happens on your machine / inside your network.
2. (Optional) Open it in a browser instead of reading the terminal
If you don't want to look at terminal output, FAIRy can render the same report in a local browser window so you can click through issues instead of scrolling logs.
Same checks. Same PASS / WARN / FAIL guidance. Still runs locally.
How FAIRy fits your intake process
Run FAIRy on your dataset
Point FAIRy at a folder or metadata sheet in your environment. It scans filenames and required fields locally.
Learn more →Fix issues with clear tips
FAIRy tells you exactly what’s wrong (bad filename, missing contact email, non-ISO date) and how to fix it. No guessing — we point to the exact column / file.
Learn more →Generate a readiness sheet
FAIRy produces a one-page PASS / WARN / FAIL report (why it matters + how to fix) that you can hand back to the lab / PI — or attach to a ticket before submission.
See sample report →For labs & cores → For researchers (community)
Key features
FAIRy provides comprehensive validation and reporting capabilities for research data.
Local-first validation
Data stays on your machine. All validation runs locally—nothing is uploaded to external servers. Perfect for sensitive or pre-publication data.
Repository-specific rulepacks
Pre-configured rulepacks for GEO, SRA, Zenodo, and other repositories. Each rulepack encodes the specific requirements and expectations of that repository.
Dual-format reports
Machine-readable JSON reports for automation and human-readable Markdown reports for easy review. Both formats included in every validation run.
CLI and Python API
Use FAIRy from the command line or integrate it into your Python workflows. Flexible interfaces for different use cases and automation needs.
Multi-table validation
Validate relationships across multiple tables with foreign key checks. Ensures data integrity and referential consistency across your dataset.
Attestation files
Generate documented proof of validation with timestamps, rulepack versions, and file hashes. Perfect for compliance and due diligence.
Get started with FAIRy
FAIRy is open source and available for researchers, labs, and institutions. Choose the path that fits your needs.
⚠ Early alpha: Interfaces may change before v1.0
Open source repositories
- FAIRy-core →Core validation engine and CLI (AGPL-3.0)
- FAIRy-lab →Streamlit UI with project workspaces and guided fixes
Documentation & resources
Commercial & pilots
Commercial licensing available for organizations that cannot adopt AGPL. For institutions and labs interested in pilots or dashboards.