FAIRy

Example FAIRy Submission Readiness Report

Don't wait for the repository to reject your submission. Run FAIRy before you upload, and you'll see exactly what needs to be fixed — missing fields, formatting issues, inconsistent IDs — all with clear instructions on how to resolve them.

The report below shows what FAIRy generates. Fix the issues it flags, and you can submit with confidence — once, not four times.

Submission Readiness Summary
Dataset: GSM123456_sample_dataset
Generated locally:
2 FAIL / 1 WARN
Issues found that need attention before submission

Provenance: FAIRy pilot build (local run; pre-release)

Command used (local run on 2025-10-14):
fairy validate /path/to/dataset --out out/ --format html

File hash digest (so you can prove which exact files were checked):

SHA256 manifest:
dataset_metadata.json: a1b2c3d4e5f6...
sample_1.fastq: f6e5d4c3b2a1...
sample_2.fastq: 1a2b3c4d5e6f...

What needs to be fixed

SeverityCodeWhere it failedWhy it mattersHow to fix
FAILCORE.ID.UNMATCHED_SAMPLEsamples.tsv → sample_id (row 2 mismatch)Every file must map to a described sample and vice versa.Align sample_id across tables.
FAILGEO.REQUIRED.MISSING_FIELDmetadata.tsv → platform_type (missing)The repository requires platform_type to process your submission; missing this will delay acceptance.Add platform_type column with values like 'Illumina HiSeq 2000' or 'Affymetrix Human Genome U133 Plus 2.0 Array'.
WARNCORE.DATE.INVALID_ISO8601row 0, column 'collection_date'Ambiguous dates hurt reuse; a curator will probably ask you to fix this before accepting.Use ISO8601 (YYYY-MM-DD).
FAIRy runs locally. You can forward this report as-is to a collaborator, student, or PI and say, "Please fix these before I can accept this dataset." No raw data is included — just the problems and how to resolve them.

See how FAIRy's rulepacks map to real repository requirements in our documentation.