backup materials and knowledge-base docs
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# FAIR Metadata Checklist
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Use this file to audit whether a dataset deposit is findable, accessible, interoperable, and
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reusable enough for a Nature-style submission.
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## Quick FAIR test
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| Principle | Practical check |
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| Findable | Dataset has a persistent identifier, rich title/abstract/keywords, searchable repository record, and metadata that names the data identifier. |
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| Accessible | Identifier resolves through a standard protocol; access conditions are explicit; metadata stay public even if data are restricted. |
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| Interoperable | Files use community formats where possible; metadata use shared vocabulary, units, identifiers, and qualified links to related data/code/publication. |
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| Reusable | Licence, provenance, methods, variables, quality-control notes, version, and community-standard metadata are clear enough for reuse. |
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## DataCite core fields
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Mandatory fields commonly expected for DOI-style dataset records:
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- Identifier
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- Creator
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- Title
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- Publisher / repository
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- Publication year
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- Resource type
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Strongly recommended when available:
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- contributor and role
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- description / abstract
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- subject keywords
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- funding reference
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- related identifiers: manuscript preprint/article, code repository, protocol, previous dataset
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- version
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- licence / rights
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- geolocation or temporal coverage for spatial/temporal data
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- language
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## Dataset README template
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```text
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# [Dataset title]
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## Summary
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[One-paragraph description of what the dataset contains and which manuscript results it supports.]
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## Files
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- [filename]: [contents, format, size, related figure/table]
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## Variables and units
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[Column/field name] | [definition] | [unit] | [allowed values/missing-value code]
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## Methods and provenance
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[How data were generated, collected, transformed, filtered, normalised, or aggregated.]
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## Software and environment
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[Software, package versions, scripts, notebooks, operating system or instrument software when relevant.]
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## Access and licence
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[Licence, access restrictions, data-use agreement, embargo, or controlled-access process.]
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## Citation
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[Preferred dataset citation.]
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```
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## File organization
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- Use stable, descriptive filenames instead of local shorthand.
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- Keep raw and processed data separate.
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- Include a manifest for archives or large multi-file deposits.
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- Map source data to exact figure panels and table numbers.
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- Preserve units in column names or data dictionaries, not only in manuscript captions.
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- Record missing-value codes and filtering decisions.
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- Include checksums for large or critical files when the repository does not generate them.
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## Provenance prompts
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Ask the author:
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- What instrument, survey, simulation, database, or processing pipeline produced each file?
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- Which script or notebook converts raw data into each figure or statistical table?
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- Which samples, time points, conditions, or participants were excluded, and why?
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- What version of each third-party dataset was used?
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- Are there licences, consent forms, data-use agreements, or ethics approvals that limit reuse?
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- Has any data been transformed in a way that prevents reconstruction of the raw values?
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## Licence guidance
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- Prefer a standard open licence when data can be public.
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- Use the repository's licence field rather than only writing licence text in the manuscript.
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- Use CC0 or CC-BY-style terms only when appropriate for the data and institution.
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- Do not apply an open licence to third-party or participant data unless the authors hold the right
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to do so.
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- For code, use a software licence and archive a release when possible.
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## Final audit
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Block submission until these are resolved:
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- no Data Availability statement for original research
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- no identifier or stable access route for data supporting central conclusions
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- sensitive data restriction without access procedure
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- third-party data with no source or permission route
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- public dataset with no licence or README
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- claim that data are in the paper when figure source data are absent
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- mismatch between manuscript statement, repository record, and supplementary files
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