The Two-Way Bridge for FAIR Metadata

Bridging Information Technology & Data Stewardship with JSON Schema

FAIR Data JSON Schema bridges the gap between Information Technologists and Data Practitioners by enriching the established IT practice of describing datasets as schemas. Document your data with machine-actionable FAIR metadata—with zero new tools to learn and 100% ecosystem compatibility.

Explore Documentation See Examples & Cookbook View Specifications

Native Machine Actionability & AI Readiness

JSON Schema is the native format for APIs, tools, LLMs, function calling, and autonomous AI agents. By adding fair: metadata annotations, AI agents gain deep semantic understanding of the data—without needing to inject unstructured documentation into context windows or write custom glue code.

Designed for Two Universes

Connecting Information Technologists with Data Practitioners through a shared, lightweight standard.

Developers Data Engineers AI & ML Experts IT Architects

For Information Technologists

You don't need to learn RDF, SPARQL, complex domain models, or niche tools to document data properly. Use the JSON Schema ecosystem you already know and love.

Zero Learning Curve: Works directly in JSON/YAML with standard tooling.
100% Compatible: Existing JSON Schema validators treat fair: keywords as transparent metadata annotations.
Best Practices: Effortlessly align on FAIR data principles and global standards without the steep learning curve.

Data Stewards Scientists Researchers Domain Custodians

For Data Practitioners

Established semantic standards (DDI, CDIF, Schema.org, RO-Crates) are essential, but their steep learning curve hinders adoption outside specialized archives.

Accessible Stepping Stone: A lightweight ingest layer that lowers the barrier to FAIR data documentation.
Does Not Replace Standards: Bridges raw data into active software workflows while supporting export to CDIF v1.1.
Active Metadata: Turns static portal documentation into live, machine-enforceable data rules.

See the Difference in Action

Adding simple fair: annotations transforms a low-level technical schema into a self-documenting, machine-actionable asset—while remaining 100% valid and compatible with standard JSON Schema tools across Python, Node.js, Go, and Rust.

Syntax:

Standard JSON Schema Technical Only

title: Weather Station Payload
description: Raw surface observation data
type: object
properties:
  station_id:
    type: string
  temp:
    type: number
    description: Temperature measurement
  quality_flag:
    type: integer
    description: Data quality flag
    enum: [0, 2, 3, 6, 7]

{
  "title": "Weather Station Payload",
  "description": "Raw surface observation data",
  "type": "object",
  "properties": {
    "station_id": { "type": "string" },
    "temp": {
      "type": "number",
      "description": "Temperature measurement"
    },
    "quality_flag": {
      "type": "integer",
      "description": "Data quality flag",
      "enum": [0, 2, 3, 6, 7]
    }
  }
}

FAIR Data JSON Schema Machine-Actionable & Self-Documenting

$schema: https://highvaluedata.net/fair-data-schema/dev
$id: https://doi.org/10.5281/zenodo.1234567
title: Arctic Weather Station Surface Observations
description: Raw surface observation data
fair:identifiers:
  - identifier: NOAA-NCEI-2024-ARCTIC-01
    type: AccessionNumber
    uniquenessScope: institutional
fair:licenseRef: https://spdx.org/licenses/CC-BY-4.0
fair:contributors:
  - name: NOAA National Centers for Environmental Information
    contributorRef: https://ror.org/007qwym43
    role: Provider
type: object
properties:
  temp:
    type: number
    description: Temperature measurement
    fair:label: Ambient Surface Temperature
    fair:quantity: Temperature
    fair:quantityRef: https://qudt.org/vocab/quantitykind/Temperature
    fair:measurementUnit: "Degree Celsius (°C)"
    fair:measurementUnitRef: http://qudt.org/vocab/unit/DEG_C
  quality_flag:
    type: integer
    description: Data quality flag
    fair:classification: WMO Quality Flags
    oneOf:
      - const: 0
        title: Good / OK
      - const: 2
        title: Suspect / Doubtful
      - const: 3
        title: Incorrect value
      - const: 6
        title: Estimated / Synthetic
      - const: 7
        title: Missing value marker
        fair:sentinel: true

{
  "$schema": "https://highvaluedata.net/fair-data-schema/dev",
  "$id": "https://doi.org/10.5281/zenodo.1234567",
  "title": "Arctic Weather Station Surface Observations",
  "description": "Raw surface observation data",
  "fair:identifiers": [
    {
      "identifier": "NOAA-NCEI-2024-ARCTIC-01",
      "type": "AccessionNumber",
      "uniquenessScope": "institutional"
    }
  ],
  "fair:licenseRef": "https://spdx.org/licenses/CC-BY-4.0",
  "fair:contributors": [
    {
      "name": "NOAA National Centers for Environmental Information",
      "contributorRef": "https://ror.org/007qwym43",
      "role": "Provider"
    }
  ],
  "type": "object",
  "properties": {
    "temp": {
      "type": "number",
      "description": "Temperature measurement",
      "fair:label": "Ambient Surface Temperature",
      "fair:quantity": "Temperature",
      "fair:quantityRef": "https://qudt.org/vocab/quantitykind/Temperature",
      "fair:measurementUnit": "Degree Celsius (°C)",
      "fair:measurementUnitRef": "http://qudt.org/vocab/unit/DEG_C"
    },
    "quality_flag": {
      "type": "integer",
      "description": "Data quality flag",
      "fair:classification": "WMO Quality Flags",
      "oneOf": [
        { "const": 0, "title": "Good / OK" },
        { "const": 2, "title": "Suspect / Doubtful" },
        { "const": 3, "title": "Incorrect value" },
        { "const": 6, "title": "Estimated / Synthetic" },
        { "const": 7, "title": "Missing value marker", "fair:sentinel": true }
      ]
    }
  }
}

Bridging Information Technology & Data Stewardship with JSON Schema

Native Machine Actionability & AI Readiness

Designed for Two Universes

For Information Technologists

For Data Practitioners

See the Difference in Action

Documentation & Guides

Cookbook & Examples

Executive Overviews

Specifications & Keywords