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Data integration enables global biodiversity synthesis
The accessibility of global biodiversity information has surged in the past two decades, notably through widespread funding initiatives for museum specimen digitization and emergence of large-scale public participation in community science. Effective use of these data requires the integration of dis...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017944/ https://www.ncbi.nlm.nih.gov/pubmed/33526679 http://dx.doi.org/10.1073/pnas.2018093118 |
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author | Heberling, J. Mason Miller, Joseph T. Noesgaard, Daniel Weingart, Scott B. Schigel, Dmitry |
author_facet | Heberling, J. Mason Miller, Joseph T. Noesgaard, Daniel Weingart, Scott B. Schigel, Dmitry |
author_sort | Heberling, J. Mason |
collection | PubMed |
description | The accessibility of global biodiversity information has surged in the past two decades, notably through widespread funding initiatives for museum specimen digitization and emergence of large-scale public participation in community science. Effective use of these data requires the integration of disconnected datasets, but the scientific impacts of consolidated biodiversity data networks have not yet been quantified. To determine whether data integration enables novel research, we carried out a quantitative text analysis and bibliographic synthesis of >4,000 studies published from 2003 to 2019 that use data mediated by the world’s largest biodiversity data network, the Global Biodiversity Information Facility (GBIF). Data available through GBIF increased 12-fold since 2007, a trend matched by global data use with roughly two publications using GBIF-mediated data per day in 2019. Data-use patterns were diverse by authorship, geographic extent, taxonomic group, and dataset type. Despite facilitating global authorship, legacies of colonial science remain. Studies involving species distribution modeling were most prevalent (31% of literature surveyed) but recently shifted in focus from theory to application. Topic prevalence was stable across the 17-y period for some research areas (e.g., macroecology), yet other topics proportionately declined (e.g., taxonomy) or increased (e.g., species interactions, disease). Although centered on biological subfields, GBIF-enabled research extends surprisingly across all major scientific disciplines. Biodiversity data mobilization through global data aggregation has enabled basic and applied research use at temporal, spatial, and taxonomic scales otherwise not possible, launching biodiversity sciences into a new era. |
format | Online Article Text |
id | pubmed-8017944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-80179442021-04-12 Data integration enables global biodiversity synthesis Heberling, J. Mason Miller, Joseph T. Noesgaard, Daniel Weingart, Scott B. Schigel, Dmitry Proc Natl Acad Sci U S A Biological Sciences The accessibility of global biodiversity information has surged in the past two decades, notably through widespread funding initiatives for museum specimen digitization and emergence of large-scale public participation in community science. Effective use of these data requires the integration of disconnected datasets, but the scientific impacts of consolidated biodiversity data networks have not yet been quantified. To determine whether data integration enables novel research, we carried out a quantitative text analysis and bibliographic synthesis of >4,000 studies published from 2003 to 2019 that use data mediated by the world’s largest biodiversity data network, the Global Biodiversity Information Facility (GBIF). Data available through GBIF increased 12-fold since 2007, a trend matched by global data use with roughly two publications using GBIF-mediated data per day in 2019. Data-use patterns were diverse by authorship, geographic extent, taxonomic group, and dataset type. Despite facilitating global authorship, legacies of colonial science remain. Studies involving species distribution modeling were most prevalent (31% of literature surveyed) but recently shifted in focus from theory to application. Topic prevalence was stable across the 17-y period for some research areas (e.g., macroecology), yet other topics proportionately declined (e.g., taxonomy) or increased (e.g., species interactions, disease). Although centered on biological subfields, GBIF-enabled research extends surprisingly across all major scientific disciplines. Biodiversity data mobilization through global data aggregation has enabled basic and applied research use at temporal, spatial, and taxonomic scales otherwise not possible, launching biodiversity sciences into a new era. National Academy of Sciences 2021-02-09 2021-02-01 /pmc/articles/PMC8017944/ /pubmed/33526679 http://dx.doi.org/10.1073/pnas.2018093118 Text en Copyright © 2021 the Author(s). Published by PNAS. http://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (http://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Biological Sciences Heberling, J. Mason Miller, Joseph T. Noesgaard, Daniel Weingart, Scott B. Schigel, Dmitry Data integration enables global biodiversity synthesis |
title | Data integration enables global biodiversity synthesis |
title_full | Data integration enables global biodiversity synthesis |
title_fullStr | Data integration enables global biodiversity synthesis |
title_full_unstemmed | Data integration enables global biodiversity synthesis |
title_short | Data integration enables global biodiversity synthesis |
title_sort | data integration enables global biodiversity synthesis |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017944/ https://www.ncbi.nlm.nih.gov/pubmed/33526679 http://dx.doi.org/10.1073/pnas.2018093118 |
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