Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.

中文翻译：

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引发数据质量的文化转变


生态研究需要高质量的数据来描述生态过程的本质并增进对生态系统变化的理解。越来越多的大数据获取增加了确保数据质量的负担和复杂性。生态学错误的代价包括数据使用率低、清理数据的时间增加以及可重复性差，这些都可能导致对生态系统过程和动态的误解，所有这些都会削弱生态研究的有效性和信任。尽管概念和技术的进步改善了生态数据的访问和管理，但需要进行文化转变，将数据质量嵌入到文化实践中。我们提出了一个全面的数据质量框架来引发这种文化转变。数据质量框架灵活支持不同的协作模式，支持所有类型的生态数据，可用于描述短期和长期生态研究中的数据质量。