FAIR Metadata Checklist

Use this file to audit whether a dataset deposit is findable, accessible, interoperable, and reusable enough for a Nature-style submission.

Quick FAIR test

Principle	Practical check
Findable	Dataset has a persistent identifier, rich title/abstract/keywords, searchable repository record, and metadata that names the data identifier.
Accessible	Identifier resolves through a standard protocol; access conditions are explicit; metadata stay public even if data are restricted.
Interoperable	Files use community formats where possible; metadata use shared vocabulary, units, identifiers, and qualified links to related data/code/publication.
Reusable	Licence, provenance, methods, variables, quality-control notes, version, and community-standard metadata are clear enough for reuse.

DataCite core fields

Mandatory fields commonly expected for DOI-style dataset records:

Identifier
Creator
Title
Publisher / repository
Publication year
Resource type

Strongly recommended when available:

contributor and role
description / abstract
subject keywords
funding reference
related identifiers: manuscript preprint/article, code repository, protocol, previous dataset
version
licence / rights
geolocation or temporal coverage for spatial/temporal data
language

Dataset README template

# [Dataset title]

## Summary
[One-paragraph description of what the dataset contains and which manuscript results it supports.]

## Files
- [filename]: [contents, format, size, related figure/table]

## Variables and units
[Column/field name] | [definition] | [unit] | [allowed values/missing-value code]

## Methods and provenance
[How data were generated, collected, transformed, filtered, normalised, or aggregated.]

## Software and environment
[Software, package versions, scripts, notebooks, operating system or instrument software when relevant.]

## Access and licence
[Licence, access restrictions, data-use agreement, embargo, or controlled-access process.]

## Citation
[Preferred dataset citation.]

File organization

Use stable, descriptive filenames instead of local shorthand.
Keep raw and processed data separate.
Include a manifest for archives or large multi-file deposits.
Map source data to exact figure panels and table numbers.
Preserve units in column names or data dictionaries, not only in manuscript captions.
Record missing-value codes and filtering decisions.
Include checksums for large or critical files when the repository does not generate them.

Provenance prompts

Ask the author:

What instrument, survey, simulation, database, or processing pipeline produced each file?
Which script or notebook converts raw data into each figure or statistical table?
Which samples, time points, conditions, or participants were excluded, and why?
What version of each third-party dataset was used?
Are there licences, consent forms, data-use agreements, or ethics approvals that limit reuse?
Has any data been transformed in a way that prevents reconstruction of the raw values?

Licence guidance

Prefer a standard open licence when data can be public.
Use the repository's licence field rather than only writing licence text in the manuscript.
Use CC0 or CC-BY-style terms only when appropriate for the data and institution.
Do not apply an open licence to third-party or participant data unless the authors hold the right to do so.
For code, use a software licence and archive a release when possible.

Final audit

Block submission until these are resolved:

no Data Availability statement for original research
no identifier or stable access route for data supporting central conclusions
sensitive data restriction without access procedure
third-party data with no source or permission route
public dataset with no licence or README
claim that data are in the paper when figure source data are absent
mismatch between manuscript statement, repository record, and supplementary files

4.0 KiB Raw Permalink Blame History