Data concepts and their relevance for data capture in large scale digitisation of biological collections

Haston, Elspeth; Cubey, Robert; Harris, David

doi:10.3366/ijhac.2012.0042

Cited by 5 publications

(6 citation statements)

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“…In the following, the information located on the herbarium vouchers, in particular the information on the labels, is referred to as `data’ ( 5 , 6 ). The term metadata is used in the sense of Meon et al ( 7 ) to designate transformed, enhanced and structured data.…”

Section: Introductionmentioning

confidence: 99%

Toward a service-based workflow for automated information extraction from herbarium specimens

et al. 2018

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Over the past years, herbarium collections worldwide have started to digitize millions of specimens on an industrial scale. Although the imaging costs are steadily falling, capturing the accompanying label information is still predominantly done manually and develops into the principal cost factor. In order to streamline the process of capturing herbarium specimen metadata, we specified a formal extensible workflow integrating a wide range of automated specimen image analysis services. We implemented the workflow on the basis of OpenRefine together with a plugin for handling service calls and responses. The evolving system presently covers the generation of optical character recognition (OCR) from specimen images, the identification of regions of interest in images and the extraction of meaningful information items from OCR. These implementations were developed as part of the Deutsche Forschungsgemeinschaft-funded a standardised and optimised process for data acquisition from digital images of herbarium specimens (StanDAP-Herb) Project.

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Section: Introductionmentioning

confidence: 99%

Toward a service-based workflow for automated information extraction from herbarium specimens

et al. 2018

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“…Adobe Systems Inc. Photoshop ( http://www.adobe.com/products/photoshop.html ) and Lightroom ( http://www.adobe.com/products/photoshop-lightroom.html )) . It should be noted that capturing and preserving high quality specimen label images offers opportunities to take advantages of future improvements in image analysis ( La Salle et al 2009 ), optical character recognition ( Haston et al 2012 ), natural language processing, handwriting analysis, and data-mining technologies.…”

Section: Observed Workflow Componentsmentioning

confidence: 99%

Five task clusters that enable efficient and effective digitization of biological collections

Nelson¹,

Paul²,

Riccardi³

et al. 2012

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This paper describes and illustrates five major clusters of related tasks (herein referred to as task clusters) that are common to efficient and effective practices in the digitization of biological specimen data and media. Examples of these clusters come from the observation of diverse digitization processes. The staff of iDigBio (The U.S. National Science Foundation’s National Resource for Advancing Digitization of Biological Collections) visited active biological and paleontological collections digitization programs for the purpose of documenting and assessing current digitization practices and tools. These observations identified five task clusters that comprise the digitization process leading up to data publication: (1) pre-digitization curation and staging, (2) specimen image capture, (3) specimen image processing, (4) electronic data capture, and (5) georeferencing locality descriptions. While not all institutions are completing each of these task clusters for each specimen, these clusters describe a composite picture of digitization of biological and paleontological specimens across the programs that were observed. We describe these clusters, three workflow patterns that dominate the implemention of these clusters, and offer a set of workflow recommendations for digitization programs.

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“…The data workflow here includes all elements of capturing and managing data associated with the specimens, and linking these to the images and image management data. Logistically, the data associated with biological collections can be divided into three main categories for digitisation ( Haston et al 2012 ). Label data which are present on the specimen; curatorial data which are found on the containers holding the specimens; and supplementary data which are held separately from the collections in indexes, archives and literature.…”

Section: Workflows and Processesmentioning

confidence: 99%

“…The need for the digitisation of biological collections is widely recognised (eg European Commission 2011 , Kroes 2011 , Niggeman et al 2011 ) resulting in the development of national digitisation strategies (eg Beach et al 2010 ). The challenges of digitising natural history specimens have been explored (eg Beaman et al 2007 , Vollmar et al 2010 ) and there have been several studies investigating data capture methods ( Beaman et al 2006 , Heidorn and Wei 2008 , Best et al 2009 , Lafferty and Landrum 2009 , Granzow-de la Cerda and Beach 2010 , Haston et al 2012 ). Within this context of large scale digitisation of natural history collections, there is a need for the development of digitisation workflows to manage each of the elements of the digitisation process.…”

Section: Introductionmentioning

confidence: 99%

Developing integrated workflows for the digitisation of herbarium specimens using a modular and scalable approach

Haston¹,

Cubey²,

Pullan³

et al. 2012

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Digitisation programmes in many institutes frequently involve disparate and irregular funding, diverse selection criteria and scope, with different members of staff managing and operating the processes. These factors have influenced the decision at the Royal Botanic Garden Edinburgh to develop an integrated workflow for the digitisation of herbarium specimens which is modular and scalable to enable a single overall workflow to be used for all digitisation projects. This integrated workflow is comprised of three principal elements: a specimen workflow, a data workflow and an image workflow.The specimen workflow is strongly linked to curatorial processes which will impact on the prioritisation, selection and preparation of the specimens. The importance of including a conservation element within the digitisation workflow is highlighted. The data workflow includes the concept of three main categories of collection data: label data, curatorial data and supplementary data. It is shown that each category of data has its own properties which influence the timing of data capture within the workflow. Development of software has been carried out for the rapid capture of curatorial data, and optical character recognition (OCR) software is being used to increase the efficiency of capturing label data and supplementary data. The large number and size of the images has necessitated the inclusion of automated systems within the image workflow.

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Data concepts and their relevance for data capture in large scale digitisation of biological collections

Cited by 5 publications

References 1 publication

Toward a service-based workflow for automated information extraction from herbarium specimens

Toward a service-based workflow for automated information extraction from herbarium specimens

Five task clusters that enable efficient and effective digitization of biological collections

Developing integrated workflows for the digitisation of herbarium specimens using a modular and scalable approach

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