Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge

TL;DR

This paper introduces Autonomous FAIR Digital Objects (aFDOs), an operational model for active, standards-based, and resilient scientific data management that surpasses passive publication.

Contribution

It extends FAIR Digital Objects with policy, announcement, and agreement layers, enabling autonomous, accountable, and conflict-resolving scientific data handling.

Findings

Resolves 56.3% of ClinVar conflicts with multiple submitters.

Degrades gracefully under Sybil, collusion, and poisoning attacks.

Formalizes a multi-layered, standards-based aFDO architecture.

Abstract

Scientific knowledge on the Web is published as passive assertions and cannot decide when to validate evidence, reconcile contradictions, or update confidence as findings accumulate. Curation depends on centralised middleware and institutional continuity, but when registries close, active stewardship stops even when data remain online. We advance the concept of Autonomous FAIR Digital Objects (aFDOs) from an abstract idea to an operational model, to offer a route from passive scientific publication toward accountable, standards-aligned automation that can outlive its publishing institutions. aFDO augments FDOs with three capabilities anchored in Semantic Web standards, namely 1) a policy layer over RDF-star aligned with PROV-O, SHACL, and ODRL for portable condition-action rules, 2) an announcement layer over ActivityStreams 2.0 that bounds per-announcement evaluation cost, and 3) an agreement layer that resolves multi-source contradictions through reputation and confidence weighted agreement under a bounded adversarial model. We provide a formal definition that distinguishes policy specifications, event handlers, and communication interfaces. We evaluate an open reference implementation on 4,305 FDOs grounded in rare-disease ontologies, namely ClinVar, HPO, and Orphanet, combined with controlled synthetic observations. The consensus mechanism resolves 56.3% of 3,914 naturally occurring ClinVar conflicts where multiple submitters disagree and an expert panel has subsequently adjudicated. Under Sybil, collusion, and poisoning attacks, the mechanism degrades gracefully within its design Byzantine-tolerance bound (f < n/5), and fails as predicted beyond that bound.