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

Abstract. 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 di...

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Detalles Bibliográficos
Autores principales: Nelson, Gil, Paul, Deborah, Riccardi, Gregory, Mast, Austin R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pensoft Publishers 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3406464/
https://www.ncbi.nlm.nih.gov/pubmed/22859876
http://dx.doi.org/10.3897/zookeys.209.3135
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author Nelson, Gil
Paul, Deborah
Riccardi, Gregory
Mast, Austin R.
author_facet Nelson, Gil
Paul, Deborah
Riccardi, Gregory
Mast, Austin R.
author_sort Nelson, Gil
collection PubMed
description Abstract. 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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spelling pubmed-34064642012-08-02 Five task clusters that enable efficient and effective digitization of biological collections Nelson, Gil Paul, Deborah Riccardi, Gregory Mast, Austin R. Zookeys Article Abstract. 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. Pensoft Publishers 2012-07-20 /pmc/articles/PMC3406464/ /pubmed/22859876 http://dx.doi.org/10.3897/zookeys.209.3135 Text en Gil Nelson, Deborah Paul, Gregory Riccardi, Austin R. Mast http://creativecommons.org/licenses/by/3.0 This is an open access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Article
Nelson, Gil
Paul, Deborah
Riccardi, Gregory
Mast, Austin R.
Five task clusters that enable efficient and effective digitization of biological collections
title Five task clusters that enable efficient and effective digitization of biological collections
title_full Five task clusters that enable efficient and effective digitization of biological collections
title_fullStr Five task clusters that enable efficient and effective digitization of biological collections
title_full_unstemmed Five task clusters that enable efficient and effective digitization of biological collections
title_short Five task clusters that enable efficient and effective digitization of biological collections
title_sort five task clusters that enable efficient and effective digitization of biological collections
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3406464/
https://www.ncbi.nlm.nih.gov/pubmed/22859876
http://dx.doi.org/10.3897/zookeys.209.3135
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