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Fair data

Skill BioTender-max/awesome-bio-agent-skills/skills/labclaw/fair-data

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.From the repository description

Install
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill fair-data

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SKILL.md

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FAIR Data Principles — Findable, Accessible, Interoperable, Reusable

Overview

Guidelines for making scientific data FAIR: Findable, Accessible, Interoperable, and Reusable.

Findable

  • Assign globally unique persistent identifiers (DOIs) to datasets
  • Rich metadata describing the dataset (title, authors, description, keywords, dates)
  • Metadata registered in searchable resources (DataCite, re3data, FAIRsharing)
  • Data indexed in domain-specific repositories

Accessible

  • Data retrievable by identifier using standardized protocol (HTTP, FTP)
  • Metadata accessible even if data is restricted
  • Authentication/authorization where necessary, clearly documented
  • Long-term preservation plan (minimum 10 years for funded research)

Interoperable

  • Use formal, shared vocabularies (ontologies: GO, ChEBI, EFO, MeSH)
  • Standard file formats (CSV, JSON, HDF5, NetCDF — not proprietary)
  • Include references to related datasets and publications
  • Machine-readable metadata (JSON-LD, Dublin Core, schema.org)

Reusable

  • Clear data usage license (CC-BY, CC0 recommended for scientific data)
  • Detailed provenance (how data was collected, processed, quality controlled)
  • Meet community standards (MIAME for microarrays, MINSEQE for sequencing)
  • Version control for datasets that evolve

Recommended Repositories

DomainRepository
GeneralZenodo, Figshare, Dryad
GenomicsGEO, SRA, ENA
ProteomicsPRIDE, MassIVE
StructuresPDB, EMDB
ClinicalClinicalTrials.gov, YODA
ChemistryChEMBL, PubChem
MaterialsNOMAD, Materials Cloud

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