Despite growing evidence of open biodiversity data reuse by scientists, information about how data is reused and cited is rarely openly accessible from research data repositories. This study explores data citation and reuse practices in biodiversity by using openly available metadata for 43,802 datasets indexed in the Global Biodiversity Information Facility (GBIF) and content analyses of articles citing GBIF data. Results from quantitative and content analyses suggest that even though the number of studies making use of openly available biodiversity data has been increasing steadily, best practice for data citation is not yet common. It is encouraging, however, that an increasing number of recent articles (16 out of 23 in 2019) in biodiversity cite datasets in a standard way. A content analysis of a random sample of unique citing articles (n = 100) found various types of background (n = 18) and foreground (n = 81) reuse cases for GBIF data, ranging from combining with other data sources to create species distribution modelling to software testing. This demonstrates some unique research opportunities created by open data. Among the citing articles, 27% mentioned the dataset in references and 13% in data access statements in addition to the methods section. Citation practice was inconsistent especially when a large number of subsets (12 ~ 50) were used. Even though many GBIF dataset records had altmetric scores, most posts only mentioned the articles linked to those datasets. Among the altmetric mentions of datasets, blogs can be the most informative, even though rare, and most tweets and Facebook posts were for promotional purposes.

尽管有越来越多的证据表明科学家可以公开使用生物多样性数据，但是有关如何重用和引用数据的信息却很少能从研究数据存储库中公开获得。本研究通过使用全球生物多样性信息基金（GBIF）索引的43,802个数据集的公开可用元数据以及引用GBIF数据的文章的内容分析，探索了生物多样性中的数据引用和重用实践。定量分析和内容分析的结果表明，尽管利用公开可用的生物多样性数据的研究数量一直在稳步增长，但数据引用的最佳做法仍不普遍。然而，令人鼓舞的是，越来越多的关于生物多样性的近期文章（2019年23篇文章中的16篇）以标准方式引用了数据集。对独特引用文章（n = 100）的随机样本进行的内容分析发现GBIF数据的各种类型的背景（n = 18）和前景（n = 81）重用案例，包括与其他数据源组合以创建物种分布建模到软件测试。这表明了由开放数据创造的一些独特的研究机会。在引用的文章中，除方法部分外，有27％的人在参考文献中提到了数据集，在数据访问语句中提及了13％。特别是当使用大量子集（12〜50）时，引用实践是不一致的。即使许多GBIF数据集记录都具有高度得分，但大多数帖子仅提及与这些数据集链接的文章。在数据集的高度提及中，博客可能是最有用的信息，尽管很少见，