The digitisation of natural history collections at scale raises a range of logistical and curatorial challenges. One concern is the physical expansion of storage infrastructure. As part of the Distributed System of Scientific Collections UK (Smith et al. 2022), the Natural History Museum London (NHM) will rehouse a significant proportion of its 36 million pinned entomological specimens (~130,000 drawers). Digitisation involves imaging every specimen and attaching a physical, unique, human- and machine-readable identifier (a small card label with a Data Matrix barcode). These barcodes must be readable from above, and their addition during digitisation can increase drawer occupancy if they exceed the existing footprint. Manual estimation of the footprint increase is impractical at this scale, given the range of circumstances associated with different specimens and drawers. To address this, the NHM has developed an AI-driven approach to estimate specimen drawer expansion and support resource planning. The deep learning pipeline can automatically detect drawer objects, referred to here as classes, including specimens, labels, barcodes, notes, unit trays, and drawers, from high-resolution images. Our dataset includes 11,090 digitised pinned Coleoptera drawers from the Index Lot collection (Natural History Museum 2014), representative of typical entomological collections. The AI pipeline supports both "pre-" and "post-" digitisation analysis by calculating the specimen bounding areas, handling overlaps, and estimating the net footprint change. Our object detection models achieved high performance under real-world conditions, with a mean average precision (mAP*1) of 85.45% across all classes. Barcode detection reached 86.63% mAP, while the standard unit tray detection and unit tray type classification model achieved 99.5% accuracy. (An example of the detection outputs is shown in Fig. 1) Three different calculation methods were tested and evaluated to estimate the drawer expansion area, corresponding to three specimen expansion rates for specimens with Data Matrix barcodes attached. Rate 1 (Per-Drawer Area via Polygon Masks*2): calculates expansion rates per drawer by measuring the total area occupied by specimens and their labels, ensuring overlaps are counted only once using polygon masks, as shown in Fig. 2. Rate 2 (Total-Based Expansion Rate): calculates expansion rates by averaging the occupancy areas of barcode-attached specimens across the entire dataset. Rate 3 (Per-Specimen Expansion Rate): averages the expansion rate per specimen using only bounding box*3 areas. Every specimen contributes equally, regardless of its size, to the final value. Rate 1 (Per-Drawer Area via Polygon Masks*2): calculates expansion rates per drawer by measuring the total area occupied by specimens and their labels, ensuring overlaps are counted only once using polygon masks, as shown in Fig. 2. Rate 2 (Total-Based Expansion Rate): calculates expansion rates by averaging the occupancy areas of barcode-attached specimens across the entire dataset. Rate 3 (Per-Specimen Expansion Rate): averages the expansion rate per specimen using only bounding box*3 areas. Every specimen contributes equally, regardless of its size, to the final value. Table 1 compares different expansion rates. Both per-drawer Rate 1 and per-specimen Rate 3 have confidence intervals. Fig. 3 and Fig. 4 show their distributions: Rate 1 is right-skewed, indicating modest area increases from overlap, while Rate 3 is bimodal*4, with a small peak near 0 (large specimens unchanged) and another around 0.3 (barcode additions on smaller specimens). The global total-based Rate 2 is the mean of the ratios without confidence intervals, providing a macro-level view. In addition to estimating drawer expansion and supporting budget planning, this tool has been designed in a modular fashion, allowing individual components to be used independently at different stages of the digitisation and curation workflow. For example, specific models can be used to flag missing labels or barcodes during digitisation, assist in tracking specimen relocation, and support downstream re-curation decisions. Importantly, the pipeline is designed to be reusable across other entomological collections, with all code to be made openly available alongside a forthcoming publication, promoting scalability within the community. Our findings demonstrate that automated spatial analysis not only improves accuracy and speed in collection management but also lays the groundwork for predictive infrastructure modelling across large-scale digitisation efforts.

There have been few, if any, open data and information management policies openly published from natural science collections. This paper contextualises the rationale for publishing the Open Information and Exceptions Policy of the Natural History Museum, London and provides the policy itself. The policy outlines how the Natural History Museum puts the principle of 'open by default' into practice; and includes sections on purpose and scope, relationship to relevant legislation (which always takes precedence over the policy), the categories of possible exceptions to open information release, what happens when exceptions are declared, relations to UK government information security classifications and definition of terms.

Today, at the international level, powerful data portals are available to biodiversity researchers and policymakers, offering increasingly robust computing and network capacities and capable data services for internationally agreed-on standards. These accelerate individual and complex workflows to map data-driven research processes or even to make them possible for the first time. At the national level, however, and alongside these international developments, national infrastructures are needed to take on tasks that cannot be easily funded or addressed internationally. To avoid gaps, as well as redundancies in the research landscape, national tasks and responsibilities must be clearly defined to align efforts with core priorities. In the present article, we outline 10 essential functions of national biodiversity data infrastructures. They serve as key providers, facilitators, mediators, and platforms for effective biodiversity data management, integration, and analysis that require national efforts to foster biodiversity science, policy, and practice.

In the last decade, the Natural History Museum, UK (NHM), has been at the forefront of the digitisation of natural history collections, with almost six million of its 80 million specimens digitised. This momentous undertaking has led to numerous innovations on how to optimise digitisation workflows. One avenue that is currently being explored is the use of collaborative robots—cobots. Since acquiring a Techman TM5 900 robotic arm in 2023 (Scott et al. 2023), we have been experimenting with its capabilities. Experiments began with simple pick-and-place tasks, using artificial specimens. Next, we focused on two use-cases, based on the digitisation of shark teeth and pinned-insects. Both shark teeth and pinned insects are in abundance at the NHM, making their manual digitisation a tedious task. Currently, we have trained the cobot to pick up a specimen, move it elsewhere to photograph and scan the specimen, then move it back to its original position or to a new place. Thus far, this has all been coordinate-based. Focusing on pinned insect specimens, we have now begun training deep learning models to perform segmentation, classification, and tracking tasks on images and manually taken videos. Segmentation and classification tasks range from distinguishing specimens from one another within drawers, to classifying different pins, labels, and insects. Meanwhile, object tracking methods are utilised to track labels from videos taken around the specimen. By tracking different labels simultaneously from multiple frames, we can combine the views of the labels in order to obtain a full picture for each label (for example, using tools described in Salili-James et al. 2022). Thus far, our machine learning pipelines have proved successful, for example, with F1 scores of 96–98% to classify and segment insects and to locate pin heads from dorsal views. Soon, we will be establishing workflows that integrate computer vision (CV) and machine learning (ML) techniques directly with the robotic arm, with pipelines that could be applied to different datasets, and that can significantly enhance efficiency. Broadly, these pipelines can be split into four sections: Specimen Identification: CV/ML to locate individual specimens or certain parts of specimens e.g., pinheads within pinned insects. Handling: With custom grippers, the cobot can delicately pick up, move, and place specimens, to and from photography stations for high quality scanning. Imaging & Scanning: The cobot will scan and photograph specimens. As the camera moves around the specimen, ML is used to segment and track the specimen labels to image them from the optimal views. A built-in Optical Character Recognition process can also be integrated to perform automatic transcription from here. Identifiers: This step requires the cobot to attach identifier labels to specimens or drawers, after locating the optimal position to do this, using CV/ML. Specimen Identification: CV/ML to locate individual specimens or certain parts of specimens e.g., pinheads within pinned insects. Handling: With custom grippers, the cobot can delicately pick up, move, and place specimens, to and from photography stations for high quality scanning. Imaging & Scanning: The cobot will scan and photograph specimens. As the camera moves around the specimen, ML is used to segment and track the specimen labels to image them from the optimal views. A built-in Optical Character Recognition process can also be integrated to perform automatic transcription from here. Identifiers: This step requires the cobot to attach identifier labels to specimens or drawers, after locating the optimal position to do this, using CV/ML. In this talk, we will discuss the progress of the NHM’s cobot research and explore the future of robotics for the digitisation of natural history collections.

The Natural History Museum in the UK (NHM) is home to more than 80 million objects spanning 4.5 billion years of history. Each of these contain a wealth of data, whether on specimen labels, index cards, registers and/or diaries. Transcribing and categorising this information can help unlock crucial research potential. To do this at scale, we turn to computer vision (CV) and Machine Learning (ML) techniques to automate this work. Over a million of the museum’s specimens are ornithological, including one of the largest and most comprehensive egg collections in the world. Representing 52% of known bird species, with over 300,000 clutches (where a clutch defines the total group of eggs laid in a nest), collected over the last 200 years, arguably make this the most important archive of avian environmental change data in existence(Norris et al. 2023). The eggs were historically catalogued using index cards, containing key information such as identification, collection date, locality and clutch size. A proportion of these egg cards have now been imaged and this led to the start of this project, focusing on a sample of 15,000 photographed egg cards (example seen in Fig. 1). Our initial approach used Google Vision to perform Optical Character Recognition (OCR) to transcribe all text with the egg cards. By focusing on textboxes around key terms (e.g., “Collector”), and using CV tools, we approximated boxes around every key category. Finally, each text segment was associated to a category box, followed by minor post-processing in order to extract (i.e., transcribe and categorise) the data. Here we successfully extracted the data within the sample, with a 98.6% average accuracy. Although our methods worked well for our sample, they did rely on consistency within the structures of cards. To expand the project further, and to mitigate the reliance on consistent structures within cards, we turned to Large Language Models (LLMs). This allowed us to explore automatic data extraction from different types of cards and labels, despite variation in the card structure, and even handle unknown categories of text. Consequently, the scope of the data collected was widened, such as adding ornithological specimen data (e.g., skins), as well as external datasets through collaboration with the British Trust for Ornithology, who manage the Nest Record Scheme (Crick et al. 2003), which holds decades of vital information on the progress of monitored nests in the UK. This index-card data-extraction project is just the beginning. As we expand our data extraction capabilities, our aim is to develop a novel pipeline that can be applied not just to avifauna-related cards, but any structured textual data, with the potential to unlock invaluable insights.

The United Kingdom's Natural History Museum (NHM) AI Lab Programme represents a pioneering initiative aimed at harnessing the power of artificial intelligence (AI) to bridge the gap between the museum's extensive collection and cutting-edge AI technologies. Despite its immense potential, the application of AI in museum research remains nascent (e.g., He et al. 2024), with some individual research groups pursuing independent projects without cohesive collaboration with AI experts who know or have experience in similar endeavours. Moreover, differing standards in utilising AI among researchers add complexity to the field. The NHM AI Lab Programme addresses these challenges by co-creating AI pilot projects that bring together the NHM's collection, academic researchers, and AI experts. The NHM AI Lab Programme serves as a nexus for interdisciplinary collaboration, offering expertise in AI, machine learning, data science, and software engineering to support NHM researchers. Through one-to-one consultations and collaborative research projects, the NHM AI Lab Programme facilitates the integration of innovative AI-driven technologies into streamlining digitisation workflows and enhancing Earth and Life Science research at the NHM. In less than a year since its inception, our Programme has achieved several milestones, hosting around 20 diverse projects. These include research projects such as the application of AI for the automatic detection and identification of nannofossils in chalk, the classification of ancient shark and dinosaur teeth, the prediction of mammal disease outbreaks, and the extraction of data from historical bird egg records. Additional projects focus on the automation of mineral analysis and the detection of secondary impact craters on planetary surfaces using AI. Some led to journal publications (e.g., He et al. 2024), while others streamlined NHM researchers' workflows, enhancing their processes of research and digitisation. Moreover, several initiatives have paved the way for new funding streams and collaborative ventures, as well as promising commercial prospects. Certain projects have pioneered the creation or transformation of datasets to meet AI-ready standards, such as data quality, consistency, accessibility, usability, and data governance protocols, helping to embed AI practices into NHM research. This AI Lab Programme can act as a model for other institutions addressing a similar challenge of bridging the gap between AI and their research and collections. This presentation provides insights into the establishment and operation of the NHM AI Lab Programme, shares experiences, highlights successful collaborations, discusses challenges encountered, and outlines future directions.

BiCIKL ( Bi odiversity C ommunity I ntegrated K nowledge L ibrary) is a European Union (EU) Horizon 2020 project (2021–2024) building a new community of research infrastructures (RIs), researchers and other stakeholders, through improved access to interlinked, open and FAIR ( F indable, A ccessible, I nteroperable, R eusable) biodiversity data along the biodiversity research cycle (specimens, sequences, taxon names, publications) (Penev et al. 2022). The project’s 14 partners developed or substantially improved 16 tools and services currently in process of onboarding to the European Open Science Cloud (EOSC), presented in the FAIR Data Place (FDP) of BiCIKL’s flagship product, the Biodiversity Knowledge Hub (BKH). The tools and data were used in Open Call projects, performed by research groups worldwide. A key achievement of BiCIKL is the establishment of several new bi-directional links between the participating RIs through shared and interoperable data standards and web services. The sustainability of the BiCIKL services and especially of the strong collaborative spirit developed through the project will be ensured by a membership agreement for the BKH maintenance and further development. The results of BiCIKL are diverse and tackle various aspects of the implementation of open science practices in biodiversity research. The project partners and external collaborators from the Open Call projects published more than 80 papers and conference abstracts (see the article collections in Penev et al. 2022a and Thessen et al. 2023), two policy briefs (Penev et al. 2024, Agosti et al. 2024), three Biodiversity Information Science (TDWG) symposia (2021, 2023, 2024), several videos and factsheets and other training materials, guidelines and best practice recommendations, and so on. In the special focus of BiCIKL was the extraction and liberation of data from the PDFs of several thousands of published biodiversity articles making it accessible and re-usable. The new BiCIKL community proved to be successful in both technological innovation and long-lasting spirit of collaboration between biodiversity and genomics researchers, data repositories, RIs, publishers and other stakeholders. Beyond BiCIKL, we envisage our work towards further integration and interoperability between data domains by embracing human-in-the-loop collaborations, enhanced by Artificial Intelligence (AI). The implementation of AI and Large Language Models (LLM) should be possible when considering an important condition: to understand the complexity of past, recent and future changes in biodiversity and natural environments the use of AI tools should be based on аdequately curated, semantically structured and interlinked biodiversity data. We see this radical new step as a concerted community effort towards building a “Biodiversity Supergraph” (Fig. 1), understood here as a two-component ecosystem consisting of: centrally orchestrated system of tools and services, and distributed sources of transformed, semantically enhanced FAIR Linked Open Data, supplied by the partnering RIs. centrally orchestrated system of tools and services, and distributed sources of transformed, semantically enhanced FAIR Linked Open Data, supplied by the partnering RIs. The “Biodiversity Supergraph'' will provide integration of the biodiversity data on a scale and operational level that has never been attempted before. It is key for the next decade, to enable a baseline of global, biodiversity-related information serving organisations, academia, industry and society.

Calcareous nannofossils serve as crucial indicators for establishing the biostratigraphic age of chalk macrofossil specimens in natural science collections. Better age control of specimens collected several hundred years ago enables us to uncover the dark data hidden within these collections and incorporate these data into current research projects, examining ecosystem response to past climate change. However, the manual identification of these microscopic organisms is laborious and subjective, and so we are harnessing deep learning techniques for automatic nannofossil detection and identification. This approach required the construction of a robust dataset, currently comprising over 100,000 labelled images, complemented by the development of multiple specialised deep learning models. While some models focus on detecting target species, others are dedicated to species classification. Evaluation on an independent test set showcases the efficacy of our methodology, with the current detection model achieving a balanced accuracy of 93%. Similarly, the classification model demonstrates robust performance, attaining an average balanced accuracy of 96%. Furthermore, as well as assisting with our biostratigraphic studies, the dataset of accurately labelled images has enabled us to test other aspects of ecosystem response. For example, examining morphometric changes in nannofossils over geological time can provide valuable insights into the potential impact of current global warming on modern phytoplankton assemblages (Mancini et al. 2021). This is particularly important for coccolithophores (Young et al. 2005), which play a critical role as primary producers in the global carbon cycle. A decrease in their size could lead to bottom-up ecosystem impacts and reduced carbon sequestration (Poulton et al. 2007, Krumhardt et al. 2017). Using our dataset, we conducted a deep-learning-enhanced automatic morphometric analysis focusing on the nannofossil species, Tranolithus orionatus. Our analysis revealed that two key morphometric parameters, minor axis and area size, showed statistically significant differences between the Cenomanian stage (approximately 100.5 to 93.9 million years ago) and the post-Cenomanian stages of the Late Cretaceous (approximately 93.9 to 66.0 million years ago). Kolmogorov-Smirnov tests (Massey 1951) between the two samples yielded p-values of 0.039 for the minor axis and 0.031 for the area size. Understanding these morphometric changes is crucial due to the close parallels between current climate projections and the warming and greenhouse climate of the Late Cretaceous, particularly the Cenomanian-Turonian boundary event (Arthur et al. 1990). Insights into how organisms changed morphologically during past periods of environmental stress can help us more effectively predict future responses of organisms under similar conditions (Razmjooei et al. 2020). These findings underscore the effectiveness of our approach in automating the identification and recognition of chalk nannofossils, helping to unlock natural science collections and to address key questions related to marine response to past climate change.

This paper describes the design and build of a pilot Natural Sciences Collections Digitisation Dashboard (CDD). The CDD will become a key service for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo) and aims to improve the discoverability of natural science collections (NSCs) held in European institutions, both digitised and undigitised. Furthermore, it will serve as a dynamic visual assessment tool for strategic decision-making, including the prioritisation of digitisation. The CDD pilot includes high-level information from nine European NSCs, covering the number of objects, taxonomic scope, storage type, chronostratigraphy (Earth Science Collections), geographical region and level of detail in digitisation. This information is structured through a standardised Collection Classification Scheme, which uses high-level categorisation to describe physical natural science collections.

The Natural History Museum, London (NHM) is home to an impressive collection of over 80 million specimens, of which just 5.5 million have been digitised. Like all similar collections, digitisation of these specimens is very labour intensive, requiring time-consuming manual handling. Each specimen is extracted from its curatorial unit, placed for imaging, labels are manually manipulated, and then returned to storage. Thanks to the NHM’s team of digitisers, workflows are becoming more efficient as they are refined. However, many of these workflows are highly repetitive and ideally suited to automation. The museum is now exploring integrating robots into the digitisation process. The NHM has purchased a Techman TM5 900 robotic arm, equipped with integrated Artificial Intelligence (AI) software and additional features such as custom grippers and a 3D scanner. This robotic arm combines advanced imaging technologies, machine learning algorithms, and robotic manipulation capabilities to capture high-quality specimen data, making it possible to digitise vast collections efficiently (Fig. 1). We showcase the NHM's application of robotics for digitisation, outlining the use cases developed for implementation and the prototypical workflows already in place at the museum. We will explore our invasive and non-invasive digitisation experiments, the many challenges, and the initial results of our early experiments with this transformative technology.

The Natural History Museum, London (NHM), in collaboration with Amazon Web Services (AWS), has embarked on a project to build the Planetary Knowledge Base (PKB), a comprehensive graph network comprising data on all specimens, collectors, and localities. In the initial prototype, we have concentrated on botanical specimens, using all plant taxa and specimens within the Global Biodiversity Information Facility (GBIF), combined with geographic data from GeoNames and biographic data from WikiData, Bionomia, Harvard Index of Botany, TL2, and Tropicos. Development of the PKB is a huge undertaking—our first proof of concept has more than 100 million nodes. The primary application of this knowledge graph (KG) is powering the automated transcription of specimen labels. Using Graph Convolutional Neural Networks, textual information from labels can be aligned to the entities in the graph, creating structured semantic data from the raw text. Text is extracted from images using services from the AWS ecosystem, including Optical Character Recognition and Natural Language Processing to identify the units of information, creating a high-throughput auto-digitisation workflow for extracting structured data. The PKB graph network enables new ways to interrogate collections. It can help identify species that may require re-examination or re-identification due to taxonomic updates or inconsistencies. It can also flag potential discrepancies or conflicts in the data, such as cases where the same species is recorded under different names or classifications across various sources. Moreover, the PKB can detect possible errors and outliers in the knowledge graph and point out specimens that could represent new species misidentified within the collection. By cross-validating species with the International Union for Conservation of Nature (IUCN) Red List, it can also assist in analysing species populations with insufficient data. The PKB is being developed as a cloud service, so researchers and other institutions can experiment with this transformative technology, using it to support their own digitisation efforts.

The Distributed System of Scientific Collections UK (DiSSCo United Kingdom, Smith et al. 2022) is a proposal to the UK Research and Innovation (UKRI) Infrastructure Programme to revolutionise how we manage, share and use the UK’s natural science collections, creating a distributed network that provides a step change in research infrastructure for the UK. While the physical integration of such a collection would be almost inconceivable, its digital integration is within reach. Building on the UK Natural History Museum’s (NHM) digitisation programme and in partnership with more than 90 collection-holding institutions across the length and breadth of the UK, DiSSCo UK seeks to unlock the full scientific, economic and social benefits of the UK’s natural science collections, which are presently constrained by the limits of physical access. With just 8% of the UK’s 137 million specimens currently available digitally, their role in the emerging biodiversity data revolution is diminished. Through nationally coordinated action, DiSSCo UK seeks to massively accelerate the digitisation of these collections and the impact of these data. Five options to digitise UK collections are presently under consideration. These options iterate across the collection groups, number and type of institution, technical infrastructure level and "catalysis" to capitalise on the benefits of unlocking data and accelerating data production. Subject to UKRI approval, the full business cases for a preferred option will go through an 18–24 month approval process starting November 2023, unlocking tens to hundreds of millions of pounds of investment in UK collections. We will outline the strategic case, options and operational model for DISSCo UK, updating on our coordination, digitisation and catalysis activities.

The digitisation of natural science specimens is a shared ambition of many of the largest collections, but the scale of these collections, estimated at at least 1.1 billion specimens (Johnson et al. 2023), continues to challenge even the most resource-rich organisations. The Natural History Museum, London (NHM) has been pioneering work to accelerate the digitisation of its 80 million specimens. Since the inception of the NHM Digital Collection Programme in 2014, more than 5.5 million specimen records have been made digitally accessible. This has enabled the museum to deliver a tenfold increase in digitisation, compared to when rates were first measured by the NHM in 2008. Even with this investment, it will take circa 150 years to digitise its remaining collections, leading the museum to pursue technology-led solutions alongside increased funding to deliver the next increase in digitisation rate. Insects comprise approximately half of all described species and, at the NHM, represent more than one-third (c. 30 million specimens) of the NHM’s overall collection. Their most common preservation method, attached to a pin alongside a series of labels with metadata, makes insect specimens challenging to digitise. Early Artificial Intelligence (AI)-led innovations (Price et al. 2018) resulted in the development of ALICE, the museum's Angled Label Image Capture Equipment, in which a pinned specimen is placed inside a multi-camera setup, which captures a series of partial views of a specimen and its labels. Centred around the pin, these images can be digitally combined and reconstructed, using the accompanying ALICE software, to provide a clean image of each label. To do this, a Convolutional Neural Network (CNN) model is incorporated, to locate all labels within the images. This is followed by various image processing tools to transform the labels into a two-dimensional viewpoint, align the associated label images together, and merge them into one label. This allows users to manually, or computationally (e.g., using Optical Character Recognition [OCR] tools) extract label data from the processed label images (Salili-James et al. 2022). With the ALICE setup, a user might average imaging 800 digitised specimens per day, and exceptionally, up to 1,300. This compares with an average of 250 specimens or fewer daily, using more traditional methods involving separating the labels and photographing them off of the pin. Despite this, our original version of ALICE was only suited to a small subset of the collection. In situations when the specimen is very large, there are too many labels, or these labels are too close together, ALICE fails (Dupont and Price 2019). Using a combination of updated AI processing tools, we hereby present ALICE version 2. This new version of ALICE provides faster rates, improved software accuracy, and a more streamlined pipeline. It includes the following updates: Hardware : after conducting various tests, we have optimised the camera setup. Further hardware updates include a Light-Emitting Diode (LED) ring light, as well as modifications to the camera mounting. Software : our latest software incorporates machine learning and other computer vision tools to segment labels from ALICE images and stitch them together more quickly and with a higher level of accuracy, significantly reducing the image processing failure rate. These processed label images can be combined with the latest OCR tools for automatic transcription and data segmentation. Buildkit : we aim to provide a toolkit that any individual or institution can incorporate into their digitisation pipeline. This includes hardware instructions, an extensive guide detailing the pipeline, and new software code accessible via Github. Hardware : after conducting various tests, we have optimised the camera setup. Further hardware updates include a Light-Emitting Diode (LED) ring light, as well as modifications to the camera mounting. Software : our latest software incorporates machine learning and other computer vision tools to segment labels from ALICE images and stitch them together more quickly and with a higher level of accuracy, significantly reducing the image processing failure rate. These processed label images can be combined with the latest OCR tools for automatic transcription and data segmentation. Buildkit : we aim to provide a toolkit that any individual or institution can incorporate into their digitisation pipeline. This includes hardware instructions, an extensive guide detailing the pipeline, and new software code accessible via Github. We provide test data and workflows to demonstrate the potential of ALICE version 2 as an effective, accessible, and cost-saving solution to digitising pinned insect specimens. We also describe potential modifications, enabling it to work with other types of specimens.

UK natural science collections hold over 137 million items, an unrivalled source of data about 4.56 billion years of planetary development and hundreds of years of biological change, including the differences made by humans — but the scientific, commercial, and societal benefits of these collections are constrained by the limits of physical access, and by highly fragmented digitisation efforts with less than 10% digitally available. Following work with Frontier Economics in 2021, which showed potential for £2 billion in benefits to the UK economy from digitising all UK natural science collections, in 2022–23 the Natural History Museum London worked, with analytical support from McKinsey and Company, to understand the impact of what has already been digitised and shared by UK natural science collections — what is the demand for these data, what are they used for, and how does this deliver efficient, effective and impactful research? This study focuses on usage via the Global Biodiversity Information Facility, the largest source of relevant usage data, examining 7.6 million records from twelve UK institutions. While these UK collections data are just 0.3% of total GBIF occurrences, they are cited in 12% of peer reviewed publications citing GBIF data, showing the disproportionate impact of UK collections data and the historical, geographical, and taxonomic richness that they bring. Researchers have already benefited from more than £18 million of efficiency savings from digital UK specimen data. Data from natural science collections held in the UK are uniquely impactful resources, vital to a future in which people and planet thrive, and a step change in the pace of digitisation is needed to unlock their potential for researchers, policymakers, and society.