In neuroscience and beyond, sharing data, especially ‘big, complex data’, has enormous potential to accelerate research and knowledge. However, if assumptions are made without a full understanding of the strengths and limitations of the data methods, then there is a risk for misinterpretation and confusion about the findings. Instead of accelerating knowledge, data sharing could actually have the opposite effect, and ultimately be counterproductive.
Created with the leadership support of One Mind, the DAQCORD Guidelines were developed by an international group of clinicians, scientists and data experts who, based on a review of the literature and their own experiences, reached consensus on items relevant to assessing the quality of clinical research data. These items have been incorporated into an online reporting system that provides a framework for research study design as well as standardized self-assessment and reporting of data quality.
The DAQCORD Guidelines provide a framework for:
- Robust study design of clinical research, including those that use electronic health records and data quality management
- Standardized reporting on data methods and curation to enable the same conclusions to be reached by external stakeholders
- Systematic appraisal of data methods for external reviewers (e.g. research funders) and investigators interested in using the data for new questions or reanalysis
DAQCORD shared in the Journal of Clinical and Translational Science
The Journal of Clinical and Translational Science published a paper from the DAQCORD collaborators on the methodology that enabled them to identify 46 indicators of data quality that are applicable to the design, training/testing, run time, and post-collection phases of research studies.