Evidence map›Paper›PMID 40786906›Full record

ArticleBiodiversity data journal2025

SpeciMate: Improving metadata extraction from digitised biological specimens.

Alan Stenhouse, Peter H Thrall

Abstract read
In one paragraph

Article in Biodiversity data journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Alan StenhouseCSIRO, Canberra, Australia CSIRO Canberra Australia.ORCID https://orcid.org/0000-0001-9727-4232
Peter H ThrallCSIRO, Canberra, Australia CSIRO Canberra Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The digitisation of natural history collections represents a critical step towards preserving and increasing accessibility to valuable scientific data. Despite their fundamental importance to taxonomy, ecology and conservation, the world's natural history collections remain underutilised due to the labour-intensive process of extracting metadata from specimen labels. New information: This paper describes SpeciMate, a software application that uses a human-AI collaborative approach to accelerate the extraction of metadata from digitised specimen images. The system leverages artificial intelligence web services including optical character recognition (OCR), automated translation and large language and multimodal models (LLMs) to extract structured metadata, while requiring human expertise for prompt engineering and data curation. We describe the application's architecture, functionality and workflows, which enable effective processing of various specimen types including herbarium sheets and insect slides. Our trials indicate that this tool significantly improves the efficiency of metadata extraction while maintaining high data quality. The combination of automated AI processing with human supervision and refinement represents a promising approach to accelerating the digitisation and databasing of natural history collections, thereby enabling broader access to these invaluable resources for research, education and conservation efforts.

Indexed as

AIdata curationdigitisationmetadatasoftware applicationspecimen

Identifiers

PMID40786906
PMCPMC12332497

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.