Learning from Digital Collections: Digitisation and Machine Learning for Large-Scale Museum Studies

The world's museums collectively preserve billions of natural history specimens: identified by experts, curated, and verified. These specimens provide unique and irreplaceable historical data that can help to answer our most pressing questions relating to climate change, changes in phenology, habitat and species range, and biodiversity loss. Large-scale studies incorporating museum collections are vital to understanding these topics, but unlocking the information in collections typically requires physical access to the specimens and careful examination by experts. For the large-scale studies needed to understand global changes, involving millions of specimens across many nations, this is impractical. It is therefore increasingly important for museums to provide digital representations of specimens, comprising images and associated metadata, which can be accessed by researchers anywhere on the planet through searchable online databases. Digital specimens and modern computational analysis allow studies to be conducted at geographic and temporal scales that were previously impossible. We present two case studies demonstrating the value and limitations of such an approach. Firstly, we investigated global flowering phenology using millions of digital specimens from museums and herbaria, applying machine learning (ML) to rapidly estimate phenology from images and reveal long-term, worldwide trends (Williamson et al. 2025). This global study aggregated data from museum collections spanning three centuries, leveraging collection and digitisation efforts by hundreds of herbaria, and using modern computational methods to examine phenological trends at an unprecedented scale. We identified local changes in flowering seasonality, opening the door to fine-grained analysis of the effects of climate change on the world's flora (Fig. 1). However, this study represents only ~2% of the estimated 400 million specimens held in the world's herbaria (Davis 2023). Lack of funding for digitisation, inadequate standardisation of digitisation methods, and inconsistent metadata and data standards remain a challenge for automated studies of this type, with more than 40% of records matching our search criteria failing data quality checks (see Williamson et al. (2025) for details). In our second case study we digitised a portion of the R.H.L. Disney collection of scuttle flies (Diptera: Phoridae) at the Cambridge University Museum of Zoology. This fly family represents a "dark taxon" that may contain more than 100,000 species, of which