A simple format for FAIR data
Built on widely-adopted open standards. Easy to adopt, easy to extend.
Lightweight
A few simple JSON-based metadata formats for describing catalogs, datasets, tables, and files. No bespoke vocabularies to learn.
Standards-based
Compatible with DataCite Metadata Schema 4.6 for citation and JSON Schema Draft 2020-12 for structural validation.
Format-agnostic
Describes any kind of data — CSV, TSV, JSON, JSONL, Parquet, Arrow, XLSX, ODS, SQLite — through a unified file dialect layer.
FAIR by design
Findable, Accessible, Interoperable, and Reusable. Datasets carry the metadata needed to be properly cited and discovered.
Extensible
Domain-specific profiles add custom properties and validation rules while staying compatible with the base specification.
Software-first
Python and TypeScript implementations out-of-the-box, plus an MCP server so AI assistants can validate and query Fairspec data.
One JSON file. A FAIR dataset.
A Fairspec descriptor carries everything a consumer needs: citation metadata, file dialects, and validated schemas.
{
"$schema": "https://fairspec.org/profiles/latest/dataset.json",
"title": "Climate Survey 2025",
"creators": [
{ "name": "Ada Lovelace" }
],
"resources": [
{
"data": "measurements.csv",
"fileDialect": { "format": "csv" },
"tableSchema": {
"primaryKey": ["id"],
"properties": {
"id": { "type": "integer" },
"temperature": { "type": "number" }
}
}
}
]
}Implementations
Adopt the standard from your language of choice, your AI assistant, or a desktop interface.
Describe your data. Make it FAIR.
Read the specifications, browse the examples, and start describing your datasets in minutes.