The value of research data has grown as the emphasis on research transparency and data-intensive research has increased. Data sharing is now required by funders and publishers and is becoming a disciplinary expectation in many fields. However, practices promoting data reusability and research transparency are poorly understood, making it difficult for statisticians and other researchers to reframe study methods to facilitate data sharing. This article reviews the larger landscape of open research and describes contextual information that data reusers need to understand, evaluate, and appropriately analyze shared data. The article connects data reusability to statistical thinking by considering the impact of the type and quality of shared research artifacts on the capacity to reproduce or replicate studies and examining quality evaluation frameworks to understand the nature of data errors and how they can be mitigated prior to sharing. Actions statisticians can take to update their research approaches for their own and collaborative investigations are suggested.

中文翻译：

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统计在促进数据可重用性和研究透明度方面的作用


随着对研究透明度和数据密集型研究的重视程度增加，研究数据的价值也在增加。现在，资助者和出版商都要求数据共享，并且正在成为许多领域的学科期望。然而，人们对促进数据可重用性和研究透明度的做法知之甚少，这使得统计学家和其他研究人员难以重新构建研究方法以促进数据共享。本文回顾了开放研究的更大前景，并描述了数据再使用者理解、评估和适当分析共享数据所需的上下文信息。本文通过考虑共享研究工件的类型和质量对复制或复制研究能力的影响，并检查质量评估框架以了解数据错误的性质以及如何在共享之前减轻这些错误，将数据可重用性与统计思维联系起来。建议统计学家可以采取行动来更新他们的研究方法，以便他们自己进行合作调查。