24
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Data integration enables global biodiversity synthesis

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Significance

          As anthropogenic impacts to Earth systems accelerate, biodiversity knowledge integration is urgently required to support responses to underpin a sustainable future. Consolidating information from disparate sources (e.g., community science programs, museums) and data types (e.g., environmental, biological) can connect the biological sciences across taxonomic, disciplinary, geographical, and socioeconomic boundaries. In an analysis of the research uses of the world’s largest cross-taxon biodiversity data network, we report the emerging roles of open-access data aggregation in the development of increasingly diverse, global research. These results indicate a new biodiversity science landscape centered on big data integration, informing ongoing initiatives and the strategic prioritization of biodiversity data aggregation across diverse knowledge domains, including environmental sciences and policy, evolutionary biology, conservation, and human health.

          Abstract

          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.

          Related collections

          Most cited references63

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Maximum entropy modeling of species geographic distributions

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Very high resolution interpolated climate surfaces for global land areas

                Bookmark

                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                09 February 2021
                01 February 2021
                01 February 2021
                : 118
                : 6
                : e2018093118
                Affiliations
                [1] aSection of Botany, Carnegie Museum of Natural History , Pittsburgh, PA 15213;
                [2] bGlobal Biodiversity Information Facility, Secretariat , DK-2100 Copenhagen Ø, Denmark;
                [3] cDigital Humanities Program, University Libraries, Carnegie Mellon University , Pittsburgh, PA 15213
                Author notes
                1To whom correspondence may be addressed. Email: heberlingm@ 123456carnegiemnh.org .

                Edited by Douglas E. Soltis, University of Florida, Gainesville, FL, and approved December 8, 2020 (received for review September 1, 2020)

                Author contributions: J.M.H., J.T.M., D.N., and D.S. designed research; J.M.H. and D.N. performed research; S.B.W. contributed new reagents/analytic tools; J.M.H. and S.B.W. analyzed data; and J.M.H., J.T.M., D.N., S.B.W., and D.S. wrote the paper.

                Author information
                https://orcid.org/0000-0003-0756-5090
                https://orcid.org/0000-0002-5788-9010
                https://orcid.org/0000-0002-0407-1805
                https://orcid.org/0000-0003-4802-1364
                https://orcid.org/0000-0002-2919-1168
                Article
                202018093
                10.1073/pnas.2018093118
                8017944
                33526679
                6104e76e-2e49-4168-8f22-afa080e729cf
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 7
                Categories
                417
                Biological Sciences
                Environmental Sciences

                biodiversity informatics,community science,global biodiversity information facility (gbif),biological collections,scientometrics

                Comments

                Comment on this article