Robots Rapidly Writing Records: Machine Annotations at Scale with DiSSCo

What this was about

Soulaine Theocharides explained that DiSSCo harmonises collections data from many institutions so machine annotation services, adapted once, can serve all partners, avoiding duplicated development. Pilot services include plant organ detection (LeafMachine), links to GBIF/ENA/GeoCASe, georeferencing with GeoPick, and label transcription to Darwin Core using a vision model; DiSSCo can now run services over entire datasets at once. The envisaged pipeline ingests only images, runs services, lets remote experts annotate, and returns verified data to institutions. In Q&A she explained that data come from member countries under CC0 or CC BY.

Why it matters. Shows how shared infrastructure lets institutions reuse AI services at scale instead of building their own.

In the room

• Digitisation is getting harder to scale; institutions work independently and duplicate effort.
• DiSSCo brings institutions' data into one structure so services can be reused by all partners.
• Pilot machine annotation services on the sandbox: LeafMachine plant organ detection, linking to GBIF, ENA and GeoCASe, GeoPick georeferencing, and label transcription into Darwin Core; none developed by DiSSCo, all adapted.
• New: running services over entire datasets at once rather than per specimen.
• Pipeline goal: ingest images, run services, remote human experts annotate, verified data returned to institutions (export exists; return in development).
• Virtual hackathon next month for connecting services; DiSSCo is becoming an ERIC to enable service level agreements.

Notable moments