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Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: We identified 46 indicators that are applicable to the design, training/testing, run time and post-collection phases of studies.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: We identified 46 indicators that are applicable to the design, training/testing, run time and post-collection phases of studies.
CONCLUSIONS: 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.
摘要：
背景：高质量的数据对整个科学企业至关重要，然而，数据管理所涉及的复杂性和工作量却被大大低估了。对于大型观测来说尤其如此，临床研究，因为捕获的多模态数据的数量和通过分析解决众多研究问题的机会，单独或与其他数据集结合使用。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悬而未决的问题，它用于解决特定的研究问题或假设以及如何解释结果。我们旨在开发一个设计框架，为了提高科学的严谨性，对数据整理方法进行记录和报告，数据的可重复性和分析。
方法：46名专家参与了一个改进的德尔菲过程，以就可用于设计和报告研究的数据管理指标达成共识。
结果：我们确定了适用于设计的46个指标，培训/测试，研究的运行时间和收集后阶段。
结论：数据采集，观察性研究设计的质量和固化（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要而开发的，但是我们相信它们是相关的和普遍的，全部或部分，其他健康研究领域，以及较小的观察性研究和临床前研究。DAQCORD指南提供了实现高质量数据的框架；健康研究的基石。
方法：46名专家参与了一个改进的德尔菲过程，以就可用于设计和报告研究的数据管理指标达成共识。
结果：我们确定了适用于设计的46个指标，培训/测试，研究的运行时间和收集后阶段。
结论：数据采集，观察性研究设计的质量和固化（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要而开发的，但是我们相信它们是相关的和普遍的，全部或部分，其他健康研究领域，以及较小的观察性研究和临床前研究。DAQCORD指南提供了实现高质量数据的框架；健康研究的基石。
