Open-access occurrence data are useful for studying spatial patterns of fungi, but often have quality issues. These include errors in taxonomy and geo-coordinates, and incomplete coverage across areas and taxonomic groups. We identify 15 quality issues that can lead to incorrect biogeographic inference, and develop a reproducible pipeline that flags and removes problematic entries. This pipeline tests accuracy of geographic records and names. Then, if information on non-native status is unavailable or unreliable, it detects non-native species via a predictive model. Finally, it identifies spatial and environmental outliers and removes them when biologically improbable. We test the pipeline by cleaning data for Australian fungi, with 251,642 records retained after cleaning the initial 1,034,601 records. Exploratory analysis showed that the cleaned data is useful for analyses such as biogeographic regionalisation, but recording gaps and lack of saturation in collection effort also caution that more surveys are needed to improve collection completeness.

开放获取的发生数据对于研究真菌的空间模式很有用，但通常存在质量问题。这些包括分类和地理坐标中的错误，以及跨区域和分类组的不完整覆盖。我们确定了 15 个可能导致错误生物地理推断的质量问题，并开发了一个可重现的管道来标记和删除有问题的条目。该管道测试地理记录和名称的准确性。然后，如果有关非本地状态的信息不可用或不可靠，它会通过预测模型检测非本地物种。最后，它识别空间和环境异常值，并在生物学上不可能时将其删除。我们通过清理澳大利亚真菌数据来测试管道，在清理最初的 1,034,601 条记录后保留了 251,642 条记录。