Dataset for a globally synthesised and flagged bee occurrence dataset and cleaning workflow

  • James B. Dorey (Creator)
  • Erica E. Fischer (Creator)
  • Paige Chesshire (Creator)
  • Angela Nava-Bolaños (Creator)
  • Robert L. O’Reilly (Contributor)
  • Silas Bossert (Creator)
  • Shannon M. Collins (Creator)
  • Elinor M. Lichtenberg (Creator)
  • Erika M. Tucker (Creator)
  • Allan Smith-Pardo (Creator)
  • Armando Falcon-Brindis (Contributor)
  • Diego A. Guevara (Creator)
  • Bruno Ribeiro (Creator)
  • Diego de Pedro (Contributor)
  • Keng-Lou James Hung (Creator)
  • Katherine A. Parys (Creator)
  • Lindsie M. McCabe (Creator)
  • Matthew S. Rogan (Creator)
  • Robert L. Minckley (Creator)
  • Santiago J.E. Velazco (Creator)
  • Terry L. Griswold (Creator)
  • Tracy A. Zarrillo (Creator)
  • Walter Jetz (Creator)
  • Yanina V. Sica (Creator)
  • Michael C. Orr (Contributor)
  • Laura Melissa Guzman (Creator)
  • John S. Ascher (Creator)
  • Alice C. Hughes (Creator)
  • Neil S. Cobb (Biodiversity Outreach Network) (Creator)



Species occurrence data are foundational for research, conservation, and science communication, but the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We present BeeBDC, a new R package, and a global bee occurrence dataset to address this issue. We combined >18.3 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducible BeeBDCR-workflow. Specifically, we harmonised species names (following established global taxonomy), country names, and collection dates and we added record-level flags for a series of potential quality issues. These data are provided in two formats, “cleaned” and “flagged-but-uncleaned”. The BeeBDC package with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducible R workflows and globally cleaned datasets, we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation.
Date made available2024
PublisherFlinders University

Cite this