<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科研、生物保护及科学传播的基础，但可靠数据的可得性与可及性不足仍是一大阻碍，对于面临日益严峻生存压力的昆虫类群而言尤为如此。为此，我们推出了全新的R语言包<BeeBDC>及全球蜜蜂出现记录数据集，以解决这一问题。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，我们依据现行全球分类学标准统一了物种名称、国家名称及采集日期，并为每条记录添加了一系列潜在质量问题的标记字段。本数据集提供两种格式："已清洗"与"已标记但未清洗"。附带在线文档的<BeeBDC>包支持终端用户自定义过滤参数，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的数据集，我们能够提升下游分析的可及性与可靠性。该工作流可推广应用于其他生物类群，以助力相关科研与保护工作。