ArticleScience advances2026
Unlocking biodiversity data with robotic imaging and AI-driven transcription of natural history collections.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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20 authors.
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Abstract
Digitizing metadata on natural history specimen labels remains a critical bottleneck for biodiversity research. We present a transformative workflow integrating robotic imaging with artificial intelligence (AI)-driven transcription for rapid, comprehensive data extraction from specimen labels. Single high-resolution images of specimens and associated labels were submitted to Gemini 2.5 Flash and GPT-4 Turbo to extract verbatim textual information. This approach yielded ~600 verbatim transcriptions per hour, a 30-fold increase in efficiency compared to traditional manual methods, which yielded ~20 transcriptions per hour. Releasing historical specimen metadata facilitates information accessibility and provides temporal and spatial context for a variety of analyses. Our method fosters the reconnection of disparate biological datasets previously segregated among departments or institutions to unite ecologically interdependent components (e.g., host/parasite and pollinator/plant) for a more complete understanding of biodiversity dynamics.
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