FAIR² and Schema.org Integration
🎯 Overview
FAIR² (FAIR Squared) builds upon Schema.org, ensuring that datasets are Findable, Accessible, Interoperable, and Reusable (FAIR) while also being AI-ready and machine-actionable.
Schema.org is a widely used structured data vocabulary that enhances dataset discoverability. FAIR² extends Schema.org by:
✅ Adding SHACL validation for metadata consistency.
✅ Enabling AI/ML-specific metadata to describe datasets.
✅ Improving machine-actionability with linked data principles.
This document explains how FAIR² leverages Schema.org metadata to improve dataset interoperability.
📌 How FAIR² Uses Schema.org
FAIR² extends Schema.org by adding structured AI-specific metadata.
| Schema.org Term | Usage in FAIR² | Example |
|---|---|---|
schema:Dataset |
Defines datasets in FAIR². | "@type": "Dataset" |
schema:author |
Specifies dataset authorship. | "author": { "@type": "Person", "name": "Dr. Jane Doe" } |
schema:citation |
Links to related publications. | "citation": "Doe et al. (2025)" |
schema:datePublished |
Specifies dataset publication date. | "datePublished": "2025-03-01" |
schema:distribution |
Describes dataset access links. | "distribution": { "contentUrl": "https://example.com/data.csv" } |
schema:license |
Specifies dataset licensing. | "license": "https://creativecommons.org/licenses/by/4.0/" |
FAIR² remains fully compatible with Schema.org while adding AI-specific metadata and validation rules.
📌 Why Schema.org Matters for FAIR²
✅ Enhances dataset discoverability – Improves indexing by search engines & AI platforms. ✅ Ensures machine-actionability – Provides structured metadata for ML workflows. ✅ Supports FAIR principles – Aligns with linked data & persistent identifiers. ✅ Interoperable with ML Croissant – Works within AI-ready metadata frameworks.
🚀 Next Steps
1️⃣ Explore the FAIR² Schema 2️⃣ Learn about SHACL Validation 3️⃣ Contribute to FAIR²