<b>Dataset for a globally synthesised and flagged bee occurrence dataset and cleaning workflow</b>

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 <i>BeeBDC</i>, a new <i>R</i><i> </i>package, and a global bee occurrence dataset to address this issue. We combined &gt;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 <i>BeeBDC</i><i>R</i>-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 <i>BeeBDC</i> package with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducible <i>R</i><i> </i>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.

物种出现记录数据是开展研究、物种保护与科学传播的核心基础，但可靠数据的可获取性与可用性不足仍是一大阻碍，对于面临生存压力日益加剧的昆虫类群而言尤为如此。我们推出了全新的R语言包（R package）BeeBDC，以及一套全球蜜蜂出现记录数据集，旨在解决上述问题。我们整合了来自多个公共数据库（GBIF、SCAN、iDigBio、USGS、ALA）及小型数据集的超1830万条蜜蜂出现记录，并通过可复现的BeeBDC R工作流对数据进行标准化处理、质量标记、去重与清洗。具体而言，我们遵循已确立的全球分类学体系统一了物种名称、国家名称与采集日期，并为每条记录添加了一系列潜在质量问题的标记字段。本次发布的数据分为两种格式："已清洗"与"已标记但未清洗"。附带在线文档的BeeBDC包可让终端用户自定义筛选参数，以适配自身的研究需求。通过发布可复现的R工作流与全球清洗后数据集，我们能够提升下游分析的可获取性与可靠性。该工作流可推广至其他生物类群，为相关研究与保护工作提供支撑。