The work

packages of this research project target expanded analysis tools,

cross-disciplinary standards, and guidance materials to foster the sustainable

and widespread use of our developments on harmonized data quality analyses in

cohort studies and observational health research. The first objective is to

improve the scope and methodology of data quality assessments. We will improve

transdisciplinary exchange by utilizing the overlap across epidemiological and

social science data collection methods. GESIS will contribute its expertise to

reveal important yet uncovered issues in the current data quality concept, such

as adverse response behaviors. Vice versa, no comparable data quality framework

exists in the social sciences. The current data quality concept may be of

substantial use for observational studies in this field. Second, we will derive

methods for the automated grading of data quality issues with a focus on

observer, device, and center effects as well as time trends. The second

objective targets the FAIRness – findability, accessibility, interoperability,

and reusability - of data quality assessments.