Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD).
Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD).
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观察研究设计的数据获取，质量和策展指南（DAQCORD）。
DOI：
10.1017/cts.2020.24
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发表时间：
2020-03-13
影响因子：
2.6
通讯作者：
DAQCORD collaborators
中科院分区：
文献类型：
--
作者：
Ercole A;Brinck V;George P;Hicks R;Huijben J;Jarrett M;Vassar M;Wilson L;DAQCORD collaborators
高质量的数据对整个科学事业至关重要，但数据管理的复杂性和工作量却被大大低估。这对于大型观察性临床研究尤其如此，因为捕获的多模态数据量以及通过单独或与其他数据集结合分析解决众多研究问题的机会。然而，缺乏有关数据管理方法的细节可能会导致有关数据稳健性的未解决问题，其用于解决特定研究问题或假设的效用以及如何解释结果。我们的目标是为数据管理方法的设计、记录和报告开发一个框架，以提高数据的科学严谨性、可重复性和分析性。46名专家参加了经修改的德尔菲程序，以便就可用于研究设计和报告的数据整理指标达成共识。我们确定了46个指标，适用于研究的设计、培训/测试、运行时间和收集后阶段。观察性研究设计的数据采集、质量和管理（DAQCORD）指南是大型观察性研究的第一套全面的数据质量指标。它们是围绕神经科学项目的需求开发的，但我们相信它们与其他健康研究领域以及较小的观察性研究和临床前研究都是相关和可推广的。DAQCORD指南为获得高质量数据提供了一个框架，是健康研究的基石。
High-quality data are critical to the entire scientific enterprise, yet the complexity and effort involved in data curation are vastly under-appreciated. This is especially true for large observational, clinical studies because of the amount of multimodal data that is captured and the opportunity for addressing numerous research questions through analysis, either alone or in combination with other data sets. However, a lack of details concerning data curation methods can result in unresolved questions about the robustness of the data, its utility for addressing specific research questions or hypotheses and how to interpret the results. We aimed to develop a framework for the design, documentation and reporting of data curation methods in order to advance the scientific rigour, reproducibility and analysis of the data. Forty-six experts participated in a modified Delphi process to reach consensus on indicators of data curation that could be used in the design and reporting of studies. We identified 46 indicators that are applicable to the design, training/testing, run time and post-collection phases of studies. The Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) Guidelines are the first comprehensive set of data quality indicators for large observational studies. They were developed around the needs of neuroscience projects, but we believe they are relevant and generalisable, in whole or in part, to other fields of health research, and also to smaller observational studies and preclinical research. The DAQCORD Guidelines provide a framework for achieving high-quality data; a cornerstone of health research.
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