Unlocking biodiversity data with robotic imaging and AI-driven transcription of natural history collections

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.