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

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.

物种出现数据对于研究、保护和科学传播至关重要，然而，可靠数据的有限可获得性和可访问性构成了一个主要障碍，尤其是对于面临日益增长压力的昆虫而言。本报告推出了一种名为 BeeBDC 的新型 R 包以及一个全球蜜蜂出现数据集，旨在解决这一问题。我们整合了来自多个公共数据仓库（包括 GBIF、SCAN、iDigBio、USGS 和 ALA）以及较小数据集的超过 1830 万条蜜蜂出现记录，随后采用可复现的 BeeBDCR 工作流程对数据进行标准化、标记、去重和清洗。具体而言，我们对物种名称（遵循全球公认的分类体系）、国家名称以及采集日期进行了协调，并为一系列潜在的质量问题添加了记录级标记。这些数据以“清洗”和“标记但未清洗”两种格式提供。配备在线文档的 BeeBDC 包为最终用户提供了修改过滤参数的能力，以应对其研究问题。通过发布可复现的 R 工作流程和全球清洗后的数据集，我们可以提高后续分析的可获得性和可靠性。此工作流程可应用于其他物种，以支持研究和保护工作。