Dataset Information

ICollections methodology: workflow, results and lessons learned.

ABSTRACT: The Natural History Museum, London (NHMUK) has embarked on an ambitious programme to digitise its collections. The first phase of this programme was to undertake a series of pilot projects to develop the workflows and infrastructure needed to support mass digitisation of very large scientific collections. This paper presents the results of one of the pilot projects - iCollections. This project digitised all the lepidopteran specimens usually considered as butterflies, 181,545 specimens representing 89 species from the British Isles and Ireland. The data digitised includes, species name, georeferenced location, collector and collection date - the what, where, who and when of specimen data. In addition, a digital image of each specimen was taken. A previous paper explained the way the data were obtained and the background to the collections that made up the project. The present paper describes the technical, logistical, and economic aspects of managing the project.

SUBMITTER: Blagoderov V

PROVIDER: S-EPMC5665004 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

<i>iCollections</i> methodology: workflow, results and lessons learned.

Blagoderov Vladimir V Penn Malcolm M Sadka Mike M Hine Adrian A Brooks Stephen S Siebert Darrell J DJ Sleep Chris C Cafferty Steve S Cane Elisa E Martin Geoff G Toloni Flavia F Wing Peter P Chainey John J Duffell Liz L Huxley Rob R Ledger Sophie S McLaughlin Caitlin C Mazzetta Gerardo G Perera Jasmin J Crowther Robyn R Douglas Lyndsey L Durant Joanna J Scialabba Elisabetta E Honey Martin M Huertas Blanca B Howard Theresa T Carter Victoria V Albuquerque Sara S Paterson Gordon G Kitching Ian J IJ

Biodiversity data journal 20170928 5

The Natural History Museum, London (NHMUK) has embarked on an ambitious programme to digitise its collections. The first phase of this programme was to undertake a series of pilot projects to develop the workflows and infrastructure needed to support mass digitisation of very large scientific collections. This paper presents the results of one of the pilot projects - iCollections. This project digitised all the lepidopteran specimens usually considered as butterflies, 181,545 specimens represent ...[more]

PMID: 29104442

Similar Datasets

Project description:BackgroundWorkflow automation, which involves identifying sequences of tasks that can be streamlined by using technology and modern computing, offers opportunities to address the United States health care system's challenges with quality, safety, and efficiency. Other industries have successfully implemented workflow automation to address these concerns, and lessons learned from those experiences may inform its application in health care.ObjectiveOur aim was to identify and synthesize (1) current approaches in workflow automation across industries, (2) opportunities for applying workflow automation in health care, and (3) considerations for designing and implementing workflow automation that may be relevant to health care.MethodsWe conducted a targeted review of peer-reviewed and gray literature on automation approaches. We identified relevant databases and terms to conduct the searches across sources and reviewed abstracts to identify 123 relevant articles across 11 disciplines.ResultsWorkflow automation is used across industries such as finance, manufacturing, and travel to increase efficiency, productivity, and quality. We found automation ranged from low to full automation, and this variation was associated with task and technology characteristics. The level of automation is linked to how well a task is defined, whether a task is repetitive, the degree of human intervention and decision-making required, and the sophistication of available technology. We found that identifying automation goals and assessing whether those goals were reached was critical, and ongoing monitoring and improvement would help to ensure successful automation.ConclusionUse of workflow automation in other industries can inform automating health care workflows by considering the critical role of people, process, and technology in design, testing, implementation, use, and ongoing monitoring of automated workflows. Insights gained from other industries will inform an interdisciplinary effort by the Office of the National Coordinator for Health Information Technology to outline priorities for advancing health care workflow automation.

Dataset Information

ICollections methodology: workflow, results and lessons learned.

Publications

<i>iCollections</i> methodology: workflow, results and lessons learned.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets