A globally synthesised and flagged bee occurrence dataset and cleaning workflow

• Published in: Scientific data Vol. 10; no. 1; p. 747
• Main Authors: Dorey, James B., Fischer, Erica E., Chesshire, Paige R., Nava-Bolaños, Angela, O’Reilly, Robert L., Bossert, Silas, Collins, Shannon M., Lichtenberg, Elinor M., Tucker, Erika M., Smith-Pardo, Allan, Falcon-Brindis, Armando, Guevara, Diego A., Ribeiro, Bruno, de Pedro, Diego, Pickering, John, Hung, Keng-Lou James, Parys, Katherine A., McCabe, Lindsie M., Rogan, Matthew S., Minckley, Robert L., Velazco, Santiago J. E., Griswold, Terry, Zarrillo, Tracy A., Jetz, Walter, Sica, Yanina V., Orr, Michael C., Guzman, Laura Melissa, Ascher, John S., Hughes, Alice C., Cobb, Neil S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/158/670; 631/158/851; 631/601/1466; Animals; Bees; communication (human); Conservation; data collection; Data Descriptor; Datasets; Humanities and Social Sciences; multidisciplinary; Publishing; Science; Science (multidisciplinary); Workflow
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-023-02626-w

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 BeeBDC R -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.