Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD)

Area of Science:

• Health Research
• Observational Studies
• Neuroscience

Background:

• High-quality data is crucial for scientific research, but data curation efforts are often underestimated.
• Large observational and clinical studies generate vast amounts of multimodal data, presenting unique curation challenges.
• Inadequate documentation of data curation methods can compromise data robustness, utility, and interpretation.

Purpose of the Study:

• To develop a framework for designing, documenting, and reporting data curation methods.
• To enhance the scientific rigor, reproducibility, and analytical capabilities of observational study data.
• To address the need for standardized data quality indicators in large-scale research.

Main Methods:

• A modified Delphi process involving 46 experts was employed.
• Consensus was reached on key indicators for data curation.
• The process focused on indicators applicable across study phases.

Main Results:

• Forty-six data curation indicators were identified.
• These indicators cover study design, training/testing, runtime, and post-collection phases.
• The indicators are designed to ensure data quality throughout the research lifecycle.

Conclusions:

• The Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) Guidelines represent a comprehensive set of data quality indicators.
• While developed for neuroscience, the DAQCORD Guidelines are generalizable to other health research fields, smaller observational studies, and preclinical research.
• These guidelines provide a foundational framework for achieving high-quality data, essential for advancing health research.