The FAIR principles for research data (Findability, Accessibility, Interoperability, Reusability) are a set of guidelines to facilitate sustainable availability of (research) data.

Inspired by Fluree's "Making Data FAIR", I decided to look at ArcadeDB in terms of FAIRness.

So, to check whether a tool X helps making (research or enterprise) data (more) FAIR, I ask for each principle Y: How does X make data Y?

How does ArcadeDB make data findable:

• A unique identifier is assigned to each record and never reassigned.
• Custom metadata for types and properties beyond SQL COMMENT ON.
• The identifier is a mandatory property of every record.

How does ArcadeDB make data accessible?

• Query Languages: SQL dialect, Cypher, Gremlin, GraphQL, MQL (MongoDB), SPARQL (via Gremlin plugin)
• Interfaces: HTTP, Java, JDBC, MongoDB, Redis, Postgres
• Drivers: CHICKEN Scheme, .NET, Ruby, Rust

How does ArcadeDB make data interoperable?

• The native data format is JSON.
• Data types map to standard Java data types.
• Links between records are based on unique identifiers.

How does ArcadeDB make data reusable?

• Property fields (i.e. for licenses) can be made mandatory.
• Provenance can be enforced by immutable records.
• Export formats: JSONL, GRAPHSON, GRAPHML

So overall, ArcadeDB makes data fair by its feature set. While this feature configuration is likely not unique, a polyglot multi-model NoSQL/NewSQL database is rare.