Laurence Livermore

Natural History Museum, London · Department of Life Sciences

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 accelerat...

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 t...

The Distributed System of Scientific Collections (DiSSCo) is a new world-class Research Infrastructure (RI) for Natural Science Collections. The DiSSCo RI aims to create a new business model for one European collection that digitally unifies all European natural science assets under common access, curation, policies and practices that ensure that a...

Natural history collections are the foundations upon which all knowledge of natural history is constructed. Biological specimens are the best documentation of variation within each species, increasingly serve as curated sources for reference DNA, and are frequently our only evidence for historical species distribution. Collections represent an enor...

In 2018, the Natural History Museum (NHMUK, herbarium code: BM) undertook a pilot digitisation project together with the Royal Botanic Gardens Kew (project Lead) and the Royal Botanic Garden Edinburgh to collectively digitise non-type herbarium material of the subtribe Phaseolinae and the genera Dalbergia L.f. and Pterocarpus Jacq. (rosewoods and p...

Abstract: Tens of millions of images from biological collections have become available online in the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. Whilst image analysis has become mainstream in consumer applicatio...

Specimen Data Refinery (SDR) is a developing platform for automating transcription of specimens from natural history collections (Hardisty et al. 2022). SDR is based on computational workflows and digital twins using FAIR Digital Objects. We show our recent experiences with building SDR using the Galaxy workflow system and combining two FDO methodo...

Over the past three years, we have been developing the Specimen Data Refinery (SDR) to automate the extraction of data from specimen images as part of the SYNTHESYS project (Walton et al. 2020). The SDR provides an easy to deploy, open source, web-based interface to multiple workflows that enable a user to create new or enhance existing natural his...

Determining when animal populations have experienced stress in the past is fundamental to understanding how risk factors drive contemporary and future species' responses to environmental change. For insects, quantifying stress and associating it with environmental factors has been challenging due to a paucity of time‐series data and because detecta...

Semantic segmentation has been proposed as a tool to accelerate the processing of natural history collection images. However, developing a flexible and resilient segmentation network requires an approach for adaptation which allows processing different datasets with minimal training and validation. This paper presents a cross-validation approach de...

A key limiting factor in organising and using information from physical specimens curated in natural science collections is making that information computable, with institutional digitization tending to focus more on imaging the specimens themselves than on efficiently capturing computable data about them. Label data are traditionally manually tran...

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. Digitisatio...

The Natural History Museum holds over 80 million specimens and 300 million pages of scientific text. This information is a vital research tool to help solve the most important challenge humans face over the coming years – mapping a sustainable future for ourselves and the ecosystems on which we depend. Digitising these collections and providing the...

Natural history collections constitute an enormous wealth of information of Life on Earth. It is estimated that over 2 billion specimens are preserved at institutions worldwide, of which less than 10% are accessible via biodiversity data aggregators such as GBIF. Moreover, they are a very important resource for eco‐evolutionary research, which grea...

This report investigates the current state of physical (mechanical) robotics, automated warehousing approaches and assistive technologies in relation to the storage, handling and processing (particularly digitisation) of natural history collections. Robotics can sound futuristic, however we provide case studies that show many and growing examples o...

There has been little work to compare and understand the operating costs of digitisation using a standardised approach. This paper discusses a first attempt at gathering digitisation cost information from multiple institutions and analysing the data. This paper has been written: for other digitisation managers who want to breakdown and compare proj...

We describe an effective approach to automated text digitisation with respect to natural history specimen labels. These labels contain much useful data about the specimen including its collector, country of origin, and collection date. Our approach to automatically extracting these data takes the form of a pipeline. Recommendations are made for the...

Digitisation of natural science collections is fundamental to the vision for the Distributed System of Scientific Collections (DiSSCo), and given the low proportion of collections digitally accessible, it is proposed that ‘Centres of Excellence’ be developed to accelerate the creation of digital copies of original specimens. Within the ICEDIG proje...

This report reviews the current state-of-the-art applied approaches on automated tools, services and workflows for extracting information from images of natural history specimens and their labels. We consider the potential for repurposing existing tools, including workflow management systems; and areas where more development is required. This paper...

We compare different approaches to transcribing natural history data and summarise the advantages and disadvantages of each approach using six case studies from four different natural history collections. We summarise the main cost considerations when planning a transcription project and discuss the limitations we current have in understanding the...

DiSSCo, the Distributed System of Scientific Collections, is seeking to centralise certain infrastructure and activities relating to the digitisation of natural science collections. Deciding what activities to distribute, what to centralise, and what geographic level of aggregation (e.g. regional, national or pan European) is most appropriate for e...

We describe an effective approach to automated text digitisation with respect to natural history specimen labels. These labels contain much useful data about the specimen including its collector, country of origin, and collection date. Our approach to automatically extracting these data takes the form of a pipeline. Recommendations are made for the...

The digitising efforts of herbaria aim to increase access to and impact of scientific collections, by making the data digitally accessible to the global community. Digitising the NHMUK’s botanical collection of around 5.1 million specimens is an ongoing process, but the majority of the type collections have already been imaged. The Chinese type col...

European natural history collections are a critical infrastructure for meeting the most important challenge humans face over the next 30 years – creating a sustainable future for ourselves and the natural systems on which we depend – and for answering fundamental scientific questions about ecological, evolutionary, and geological processes. Since 2...

Capturing data from specimen images is the most viable way of enriching specimen metadata cheaply and quickly compared to traditional digitisation. Advances in machine learning and computer vision-based tools, and their increasing accessibility and affordability, are greatly increasing the potential to take automated measurements and capture other...

The Natural History Museum, London (NHM) has embarked on an ambitious Digital Collections 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. Pilot projects hav...

The Natural History Museum, London (NHM) has now carried out more than five years of digitisation under its Digital Collections Programme (DCP), working with peers from around the world as well as with industrial partners. Data from this and similar programmes are a key input to shared infrastructures and knowledge, for informing research and decis...

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 devel...

More and more herbaria are digitising their collections. Images of specimens are made available online to facilitate access to them and allow extraction of information from them. Transcription of the data written on specimens is critical for general discoverability and enables incorporation into large aggregated research datasets. Different methods...

R script used for this paper

R script used to map data from FinBIF API to DwC

Table of specimen data, DOIs and URIs

Python script to upload the dataset to Zenodo

Taxonomic coverage (interactive HTML file)

R Script to compile JSON files from CSV

This report will address how private collections can be included in Europe’s digitisation efforts. First, it is necessary to identify the current volume, scope and level of digitisation of private collections within Europe to have a better view of the background and inform our next steps. Secondly, a protocol to keep this inventory up to date in th...

For specimen level imaging of microscope slides automated digital microscopy systems are widely used, however, these systems are not always suitable for non-standard slides such as those found in natural history collections. For these types of slides imaging will require the use of non-automated alternatives. This paper presents a low cost option f...

A workflow for microscope slide digitisation (whole slide imaging), which is composed of individual slide imaging and automated post-processing made possible by using multiple barcodes that encode specimen metadata to enable automated file renaming, and a fixed imaging template to enable automated image processing. This workflow consists of three m...

The world’s natural history collections contain at least 2 billion specimens, representing a unique data source for answering fundamental scientific questions about ecological, evolutionary, and geological processes. Unlocking this treasure trove of data, stored in thousands of museum drawers and cabinets, is crucial to help map a sustainable futur...

Scratchpads are an online Virtual Research Environment (VRE) for biodiversity scientists, allowing anyone to share their data and create their own research networks (http://scratchpads.eu/). In operation since 2007, the platform has supported more than 1,000 communities in their efforts to share, manage and aggregate information on the natural worl...

Taxonomy provides a universal method to classify biodiversity at different scales locally and globally. Currently, existing taxonomic treatments are scattered, limiting their accessibility and utility. The Convention on Biological Diversity has responded to this challenge by setting the goal of compiling a World Flora Online (Global Strategy for Pl...

British and Irish Chalcidoidea checklist 2016 data

Taxa added from collections surveys

Comparison of 1978 checklist species with 2016 checklist

Background A revised checklist of the British and Irish Chalcidoidea and Mymarommatoidea substantially updates the previous comprehensive checklist, dating from 1978. Country level data (i.e. occurrence in England, Scotland, Wales, Ireland and the Isle of Man) is reported where known. New information A total of 1754 British and Irish Chalcidoidea s...

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 specimen...

The family Coreidae is distributed worldwide, but these phytophagous bugs are most abundant in the tropics and subtropics. In the Neotropical region, all of the subfamilies and 16 tribes are represented. In tropical ecosystems, these bugs feed on herbs and shrubs in open areas of forests as well as at the forest edge. Some species are spectacularly...

[The number of confirmed British sawfly species now stands at 537, compared to 471 in the 1978 checklist]

Coreoidea Species File Online (CSFO) is a comprehensive taxonomic database of the world's Coreoidea which has been available online since 2008. Prior to CSFO there was no recent catalogue for the group with the last comprehensive catalogue published in by Lethierry & Severin (1894) followed by a significant update by Bergroth (1913). As of May 2014...

Virtual Biodiversity Research and Access Network for Taxonomy (ViBRANT) is a European Union funded project that supports the development of virtual research communities involved in biodiversity science. In 2009, when ViBRANT was planned, the landscape of biodiversity informatics systems and software was highly fragmented. The FP7 funded ViBRANT pr...

Free-to-use information services and e-Infrastructures aim to provide a stable, accessible and reliable environment for their communities. As the majority of e-infrastructures are initially designed, developed and implemented under externally funded projects over a defined time period, post-project sustainability is often a concern. In the absence...

Free-to-use information services and e-Infrastructures aim to provide a stable, accessible and reliable environment for their communities. As the majority of European e-infrastructures are initially designed, developed and implemented under externally funded projects over a defined time period, post-project sustainability is often a concern. The f...

Traditional approaches for digitizing natural history collections, which include both imaging and metadata capture, are both labour- and time-intensive. Mass-digitization can only be completed if the resource-intensive steps, such as specimen selection and databasing of associated information, are minimized. Digitization of larger collections shoul...

The Scratchpad Virtual Research Environment (http://scratchpads.eu/) is a flexible system for people to create their own research networks supporting natural history science. Here we describe Version 2 of the system characterised by the move to Drupal 7 as the Scratchpad core development framework and timed to coincide with the fifth year of the pr...