Traditional approaches for digitizing natural history collections, which include both imaging and metadata capture, are both labour- and time-intensive. Mass-di

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The world’s natural history collections constitute an enormous evidence base for scientific research on the natural world. To facilitate these studies and improve access to collections, many organisations are embarking on major programmes of digitization. This requires automated approaches to mass-digitization that support rapid imaging of specimens and associated data capture, in order to process the tens of millions of specimens common to most natural history collections. In this paper we present Inselect—a modular, easy-to-use, cross-platform suite of open-source software tools that supports the semi-automated processing of specimen images generated by natural history digitization programmes. The software is made up of a Windows, Mac OS X, and Linux desktop application, together with command-line tools that are designed for unattended operation on batches of images. Blending image visualisation algorithms that automatically recognise specimens together with workflows to support post-processing tasks such as barcode reading, label transcription and metadata capture, Inselect fills a critical gap to increase the rate of specimen digitization.

The Natural History Museum, London has been creating digital data about collections for many years, with a formal Digital Collections Programme since 2014. Efforts to monitor the outcomes and impact of this work have focused on metrics of digital access, such as download events, and on citations of digital specimens as a measure of use. Digitisation projects and resulting research have also been used as impact case studies, highlighting areas such as human health and conservation. In 2021, the Museum decided to explore the economic impacts of collections data in more depth, and commissioned Frontier Economics to undertake modelling, resulting in this report. While the methods in this report are relevant to collections globally, this modelling focuses on benefits to the UK, and is intended to support the Museum’s own digitisation work, as well as a current scoping study funded by the Arts & Humanities Research Council about the case for digitising all UK natural science collections as a research infrastructure. This study focuses on digitisation in the round, not distinguishing between different collection types or levels of data creation at this stage. Three methods have been used: first, analysing five key thematic areas or sectors where data from natural science collections are likely to lead to benefits; secondly, analysing typical returns on investment in scientific research; and thirdly, examining the efficiency savings that can be reinvested in research if data are available freely and openly. Together, these methods confirm benefits in excess of £2 billion over 30 years, representing a seven to ten times return on investment.

The Natural History Museum, London (NHM) has embarked on an ambitious programme to digitise its collections. One aim of the programme has been to improve the workflows and infrastructure needed to support high-throughput digitisation and create comprehensive digital inventories of large scientific collections. This paper presents the workflow developed to digitise the entire Phthiraptera (parasitic lice) microscope slide collection (70,663 slides). Here we describe a novel process of semi-automated mass digitisation using both temporary and permanent barcode labels applied before and during slide imaging. By using a series of barcodes encoding information associated with each slide (i.e. unique identifier, location in the collection and taxonomic name), we can run a series of automated processes, including file renaming, image processing and bulk import into the NHM’s collection management system. We provide data on the comparative efficiency of these processes, illustrating how simple activities, like automated file renaming, reduces image post-processing time, minimises human error and can be applied across multiple collection types.