ArticleScientific data2023
A globally synthesised and flagged bee occurrence dataset and cleaning workflow.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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Who cites it
13 citing papers in PubMed.
- Cinderella Biodiversity: Three Decades of Monitoring Reveal Earlier Emergence and Increasing Abundance in Undervalued and Sparsely Recorded Flying Insects and Ballooning Spiders.Global change biology · 2026Article
- Population Collapse of the Spidery Wattle (Ecology and evolution · 2026Article
- A response to Portman et al. (2025): USGS Bee Lab collaborative science and big data are essential for understanding bee biodiversity.Annals of the Entomological Society of America · 2026Article
- A curated and integrated dataset for exploring global bee-plant interactions.Scientific data · 2026Article
- 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 · 2026Article
- Estimating global bee species richness and taxonomic gaps.Nature communications · 2026Article
- Article
- Investigating the Spatial Biases and Temporal Trends in Insect Pollinator Occurrence Data on GBIF.Insects · 2025Article
- WildFrontiers in plant science · 2025Article
- Introducing a global database of entomopathogenic fungi and their host associations.Scientific data · 2024Article
- Article
- A globally synthesised and flagged bee occurrence dataset and cleaning workflow.Scientific data · 2023Article
- Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
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.
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Registered trials
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.