Cargando…
Improved standardization of transcribed digital specimen data
There are more than 1.2 billion biological specimens in the world’s museums and herbaria. These objects are particularly important forms of biological sample and observation. They underpin biological taxonomy but the data they contain have many other uses in the biological and environmental sciences...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6901386/ https://www.ncbi.nlm.nih.gov/pubmed/31819990 http://dx.doi.org/10.1093/database/baz129 |
_version_ | 1783477487512059904 |
---|---|
author | Groom, Quentin Dillen, Mathias Hardy, Helen Phillips, Sarah Willemse, Luc Wu, Zhengzhe |
author_facet | Groom, Quentin Dillen, Mathias Hardy, Helen Phillips, Sarah Willemse, Luc Wu, Zhengzhe |
author_sort | Groom, Quentin |
collection | PubMed |
description | There are more than 1.2 billion biological specimens in the world’s museums and herbaria. These objects are particularly important forms of biological sample and observation. They underpin biological taxonomy but the data they contain have many other uses in the biological and environmental sciences. Nevertheless, from their conception they are almost entirely documented on paper, either as labels attached to the specimens or in catalogues linked with catalogue numbers. In order to make the best use of these data and to improve the findability of these specimens, these data must be transcribed digitally and made to conform to standards, so that these data are also interoperable and reusable. Through various digitization projects, the authors have experimented with transcription by volunteers, expert technicians, scientists, commercial transcription services and automated systems. We have also been consumers of specimen data for taxonomical, biogeographical and ecological research. In this paper, we draw from our experiences to make specific recommendations to improve transcription data. The paper is split into two sections. We first address issues related to database implementation with relevance to data transcription, namely versioning, annotation, unknown and incomplete data and issues related to language. We then focus on particular data types that are relevant to biological collection specimens, namely nomenclature, dates, geography, collector numbers and uniquely identifying people. We make recommendations to standards organizations, software developers, data scientists and transcribers to improve these data with the specific aim of improving interoperability between collection datasets. |
format | Online Article Text |
id | pubmed-6901386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-69013862019-12-16 Improved standardization of transcribed digital specimen data Groom, Quentin Dillen, Mathias Hardy, Helen Phillips, Sarah Willemse, Luc Wu, Zhengzhe Database (Oxford) Original Article There are more than 1.2 billion biological specimens in the world’s museums and herbaria. These objects are particularly important forms of biological sample and observation. They underpin biological taxonomy but the data they contain have many other uses in the biological and environmental sciences. Nevertheless, from their conception they are almost entirely documented on paper, either as labels attached to the specimens or in catalogues linked with catalogue numbers. In order to make the best use of these data and to improve the findability of these specimens, these data must be transcribed digitally and made to conform to standards, so that these data are also interoperable and reusable. Through various digitization projects, the authors have experimented with transcription by volunteers, expert technicians, scientists, commercial transcription services and automated systems. We have also been consumers of specimen data for taxonomical, biogeographical and ecological research. In this paper, we draw from our experiences to make specific recommendations to improve transcription data. The paper is split into two sections. We first address issues related to database implementation with relevance to data transcription, namely versioning, annotation, unknown and incomplete data and issues related to language. We then focus on particular data types that are relevant to biological collection specimens, namely nomenclature, dates, geography, collector numbers and uniquely identifying people. We make recommendations to standards organizations, software developers, data scientists and transcribers to improve these data with the specific aim of improving interoperability between collection datasets. Oxford University Press 2019-12-09 /pmc/articles/PMC6901386/ /pubmed/31819990 http://dx.doi.org/10.1093/database/baz129 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Groom, Quentin Dillen, Mathias Hardy, Helen Phillips, Sarah Willemse, Luc Wu, Zhengzhe Improved standardization of transcribed digital specimen data |
title | Improved standardization of transcribed digital specimen data |
title_full | Improved standardization of transcribed digital specimen data |
title_fullStr | Improved standardization of transcribed digital specimen data |
title_full_unstemmed | Improved standardization of transcribed digital specimen data |
title_short | Improved standardization of transcribed digital specimen data |
title_sort | improved standardization of transcribed digital specimen data |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6901386/ https://www.ncbi.nlm.nih.gov/pubmed/31819990 http://dx.doi.org/10.1093/database/baz129 |
work_keys_str_mv | AT groomquentin improvedstandardizationoftranscribeddigitalspecimendata AT dillenmathias improvedstandardizationoftranscribeddigitalspecimendata AT hardyhelen improvedstandardizationoftranscribeddigitalspecimendata AT phillipssarah improvedstandardizationoftranscribeddigitalspecimendata AT willemseluc improvedstandardizationoftranscribeddigitalspecimendata AT wuzhengzhe improvedstandardizationoftranscribeddigitalspecimendata |