Evidence map›Paper›PMID 33526679›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Data integration enables global biodiversity synthesis.

J Mason Heberling, Joseph T Miller, Daniel Noesgaard, Scott B Weingart, Dmitry Schigel

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.

0numbers the graph read from it
0cells of the map it votes in
103citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

103 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Nine changes needed to deliver a radical transformation in biodiversity measurement.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  10. Leveraging local species data, a global database, and an occupancy model to explore bee-plant interactions.Ecological applications : a publication of the Ecological Society of America · 2026
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
  20. Globally aggregated biodiversity data impact predictive and descriptive research.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article

43 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

J Mason HeberlingSection of Botany, Carnegie Museum of Natural History, Pittsburgh, PA 15213; heberlingm@carnegiemnh.org.ORCID 0000-0003-0756-5090
Joseph T MillerGlobal Biodiversity Information Facility, Secretariat, DK-2100 Copenhagen Ø, Denmark.ORCID 0000-0002-5788-9010
Daniel NoesgaardGlobal Biodiversity Information Facility, Secretariat, DK-2100 Copenhagen Ø, Denmark.ORCID 0000-0002-0407-1805
Scott B WeingartDigital Humanities Program, University Libraries, Carnegie Mellon University, Pittsburgh, PA 15213.ORCID 0000-0003-4802-1364
Dmitry SchigelGlobal Biodiversity Information Facility, Secretariat, DK-2100 Copenhagen Ø, Denmark.ORCID 0000-0002-2919-1168

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

BiodiversityAnimalsClassificationDatabases, FactualHumansMuseumsbiodiversity informaticsbiological collectionscommunity scienceGlobal Biodiversity Information Facility (GBIF)scientometrics

Identifiers

PMID33526679
PMCPMC8017944

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.