Best use case
FAIR Data Principles — Findable, Accessible, Interoperable, Reusable is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Overview
Teams using FAIR Data Principles — Findable, Accessible, Interoperable, Reusable should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/fair-data/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How FAIR Data Principles — Findable, Accessible, Interoperable, Reusable Compares
| Feature / Agent | FAIR Data Principles — Findable, Accessible, Interoperable, Reusable | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Overview
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 | Domain | Repository | |--------|-----------| | General | Zenodo, Figshare, Dryad | | Genomics | GEO, SRA, ENA | | Proteomics | PRIDE, MassIVE | | Structures | PDB, EMDB | | Clinical | ClinicalTrials.gov, YODA | | Chemistry | ChEMBL, PubChem | | Materials | NOMAD, Materials Cloud |
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