The ultimate goal of the Consortium for Longitudinal behavioral and Social Science data Integration and Coordination (CLASSIC) research development network and publicly available Open, Deep, Rich and Withinperson Resource (OpenDRaWeR) meta-data catalog is to provide a novel resource that will allow for more rigorous tests of aging theories and their boundary conditions, which will improve understanding of aging and health. We propose to do this by bringing deeply phenotyped studies and researchers working across disciplines of aging together, creating a user-friendly meta-data catalog with tools like Cohort Builder to allow investigators to identify studies to investigate social determinants of health, provide training in age-period cohort analysis (APC; to leverage temporally ordered datasets for analysis of time-related changes in health disparities and aging-related processes) and coordinated data analysis (CDA; impossible in single siloed datasets). 

The premise is that the resulting collaborations and multi-study analyses will systematically test whether findings from behavioral and social science studies hold when tested in a diversity of sample characteristics, conditions and across time. Exceeding the RFA requirements, 9 NIA-funded small- to mid-sized longitudinal studies provided letters of support of their interest to join our CLASSIC network (5 committed to join in Year 1, including Minority Aging Research Study [MARS]), engage in our training and financial support and ingress their meta-data to our OpenDRaWeR catalog. As part of Sage Bionetworks’ biomedical platform, the end product will amplify CLASSIC studies across a broader spectrum of disciplines by enhancing visibility, developing cross-disciplinary meta-data tagging, providing advanced search capabilities from integration with global data catalogs and improving collaborative network effects arising from this multidisciplinary approach. 

Our aims address critical technological and human infrastructure barriers related to meta-data sharing and collaboration (inputs) and data analysis (outputs). We address “input” barriers such as PI reluctance and study team burden of meta-data sharing and “output” barriers such as difficulty finding, accessing and interpreting data as well as a lack of training in necessary multi-study methods such as coordinated data analysis and age-period-cohort analysis.