AimSpecies occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses.InnovationWe introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed.Main conclusionsoccTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

中文翻译：

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occTest：物种发生数据质量控制的综合方法

AimSpecies 发生数据是有价值的信息，使人们能够估计地理分布、描述生态位及其演化特征并指导空间保护规划。物种发生数据的快速增加源于不断增加的数字化和聚合工作以及公民科学倡议。然而，事件数据中持续存在的质量问题可能会影响科学发现的准确性，这凸显了在生物多样性分析中过滤错误事件记录的重要性。创新我们引入了一个 R 包 occTest，它综合了一个不断增长的生物多样性清洁工作流程的开源生态系统，以准备不同建模应用程序的发生数据。它提供了一组结构化算法，通过采用多个测试的分层组织来识别物种发生记录的潜在问题。工作流程具有按测试组织的层次结构阶段（即清洁与测试）包含不同的测试块分组不同测试类型（例如环境异常值检测），可能会使用不同的测试方法（例如罗斯纳测试，折刀，ETC。）。四种不同测试块描述地理、环境、人类影响和时间维度的潜在问题。合并了过滤和绘图功能以方便测试的解释。我们提供具有不同数据源、默认参数和用户定义参数的示例。与其他可用的工具和工作流程相比，occTest 提供了一套全面的集成测试，并允许与每个测试相关的多种方法来探索数据清理方法之间的共识。它独特地结合了坐标精度分析和事件记录的环境分析。此外，它提供了一个层次结构，以纳入尚未开发的未来测试。主要结论soccTest 将帮助用户在开始数据分析之前了解可用数据的质量和数量，同时还使用户能够使用预定义规则或自定义过滤数据。建立的规则。因此，occTest 可以更好地评估每条记录对其预期应用的适当性。