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.