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talk · Tuesday 22 September · 11:45–⁠12:00 · SAL B

Closing the Loop: The DiSSCo Annotation Validation Framework for Trustworthy Data Round-Tripping

Wouter Addink · Responsible AI, Open Digital Curation, and Round-Tripping for Biodiversity Data

Recording time 3:14:23–3:29:15Open on Vimeo ↗

Summaries and key moments are machine-produced. Check the recording for the speaker’s exact words.

The short versionDiSSCo proposes impact-based, layered validation so trusted annotations are promoted automatically and only high-impact ones need expert review.

Overview

What this was about

Wouter Addink, DiSSCo technical director, framed the problem of a curator swamped by thousands of AI annotations that could override records and lose provenance. DiSSCo's planned annotation validation framework runs machine annotation services centrally for all collections and places annotations in layers: an untouched institutional base layer, a community enrichment layer for unvetted annotations, and a curated consensus layer. Annotations pass automated technical validation, then are routed by type and impact: low-impact corrections are auto-promoted, medium-impact ones need a two-agent condition or a trusted agent, and high-impact ones (new identifications, type status, catalogue numbers) need managed expert review. A public consultation was extended to 16 October with an online meeting on 22 October.

Why it matters. Addresses how to absorb large volumes of human and AI annotations without losing institutional control or provenance, a key prerequisite for AI-assisted curation at scale.

Key ideas

In the room

  • Scenario: a museum builds an AI annotation service that produces thousands or millions of annotations; directly writing to the CMS loses sovereignty and provenance.
  • Machine annotation services run in the DiSSCo infrastructure so they can be applied to all participating collections; anyone with ORCID can annotate.
  • Layers of trust: institutional base layer (untouched), community enrichment layer (unvetted), managed review, and curated consensus layer holding the latest digital specimen version.
  • Validation flow: submission with persistent identifier and agent ID; automated technical validation (schema, content policies, e.g. no malicious content or exact locations of protected species); automated routing; promotion creating a new version.
  • All versions are stored, making the system resilient to errors in automated promotion.
  • Comments and quality flags go to the enrichment layer; delete proposals need administrator review.
  • Edits are assessed for impact: low (error corrections, measurements) auto-promoted; medium needs two agents agreeing or a trusted agent with a good track record; high (new identification, type status, catalogue number) needs expert review, where experts may also be trusted machines.
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Programme abstract

Closing the Loop: The DiSSCo Annotation Validation Framework for Trustworthy Data Round-Tripping

Wouter Addink

The Distributed System of Scientific Collections (DiSSCo) represents a paradigm shift in specimen data infrastructure, moving from a "custodian-only" model to a distributed, community-driven data lifecycle. Central to this is the ability for human experts and artificial intelligence (AI) agents to enrich Digital Specimens through annotations. However, as AI-driven workflows scale up, there is a risk of specimens becoming annotated with hundreds of low-quality annotations, which threatens synchronization with the source data. Without robust governance, collection managers will face "annotation fatigue," stalling "round-tripping"-the flow of data enrichments back to Collection Management Systems (CMS).

This presentation introduces the DiSSCo Annotation Validation Framework, which went through an international public consultation process in mid-2026. For responsible open digital curation, it treats annotations as first-class digital objects with full provenance, persistent identifiers (Handles) and attribution.

At its technical core, the framework differentiates by the scientific impact of the proposed data change. It aims to find a balance between low-impact corrections, which can be processed efficiently through automated services and high-impact modifications such as taxonomic reidentifications or stratigraphic updates, which should trigger a curated peer-review workflow.

By filtering and prioritizing incoming enrichments, the framework ensures that data round-tripping to source CMS remains high-quality, manageable, and highly trusted. This mechanism effectively closes the loop between external research communities, AI annotators, and physical collection institutions, ensuring Digital Specimens remain dynamic, accurate, and reflective of the latest scientific consensus.

Submitted to the conference programme before the talk, so the talk itself may differ.