The availability of citizen science data has resulted in growing applications in biodiversity science. One widely used platform, iNaturalist, provides millions of digitally vouchered observations submitted by a global user base. These observation records include a date and a location but otherwise do not contain any information about the sampling process. As a result, sampling biases must be inferred from the data themselves. In the present article, we examine spatial and temporal biases in iNaturalist observations from the platform's launch in 2008 through the end of 2019. We also characterize user behavior on the platform in terms of individual activity level and taxonomic specialization. We found that, at the level of taxonomic class, the users typically specialized on a particular group, especially plants or insects, and rarely made observations of the same species twice. Biodiversity scientists should consider whether user behavior results in systematic biases in their analyses before using iNaturalist data.

中文翻译：

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观察观察者：参与者如何为自然主义者贡献数据以及对生物多样性科学的影响

公民科学数据的可用性导致在生物多样性科学中的应用越来越多。一个广泛使用的平台 iNaturalist 提供由全球用户群提交的数以百万计的数字凭证观察。这些观察记录包括日期和地点，但不包含有关采样过程的任何信息。因此，必须从数据本身推断出抽样偏差。在本文中，我们检查了从 2008 年平台推出到 2019 年底 iNaturalist 观察中的空间和时间偏差。我们还根据个人活动水平和分类专业化来描述平台上的用户行为。我们发现，在分类级别上，用户通常专注于特定的群体，尤其是植物或昆虫，并且很少对同一物种进行两次观察。在使用 iNaturalist 数据之前，生物多样性科学家应考虑用户行为是否会导致分析中的系统性偏差。