Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD). Issue 4 (13th March 2020)

• Record Type: Journal Article
• Authors: Ercole, Ari; Brinck, Vibeke; George, Pradeep; Hicks, Ramona; Huijben, Jilske; Jarrett, Michael; Vassar, Mary; Wilson, Lindsay
• Is Part Of: Journal of clinical and translational science. Volume 4:Issue 4(2020)
• Page Start: 354
• Page End: 359
• Publication Date: 2020-03-13
• Subjects: Data quality, -- curation, -- observational studies, -- Delphi process, -- design, -- reporting; Clinical medicine -- Research -- Periodicals; Medicine, Experimental -- Periodicals; Human experimentation in medicine -- Periodicals; 616.027
• DOI: 10.1017/cts.2020.24
• Languages: English
• ISSNs: 2059-8661

Abstract

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. Conclusion: 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.