DMAC The Data Management and Analysis Core (DMAC) of our NC State SRP Center proposal is designed to integrate results from all data streams into a true synthesis that advances environmental and public health related to per- and polyfluoroalkyl substances (PFAS). The first activity (Specific Aim) of the DMAC in establishing Center-wide data management and integration was the development of a Comprehensive Data Management Plan (cDMP). The cDMP was designed to instantiate FAIR (Findable, Accessible, Interoperable, Reusable) principles in managing Center data and was used for the coordinated development of individual project/core DMPs. A key element of our cDMP is the derivation of an ontology of data types, which recognizes that data have common elements that cross disciplinary (as well as project-specific) boundaries. This formalization of connections between nominally different data streams and assignment of individual points-of-contact for each type will establish the DMAC as a resource for operationalizing data integration. Subsequent DMAC Specific Aims establish processes for monitoring data analysis, coordinate analysis through shared data structures and associated software, provide means for visualization and sharing of results, and coordinate data-centric training activities. Thus, the DMAC will enable synthesis not possible from singular Projects/Cores alone, through coordination amongst projects and cores, fostering data sharing and interoperability, and providing formal data quality assurance and quality control.
DMAC The Data Management and Analysis Core (DMAC) will manage the integration of data streams generated by the Projects and Cores of the NC State Superfund Research Center. The DMAC aims lay out a data-centric approach to synthesize this multi-scale integration into actionable discoveries that advance public health related to per- and polyfluoroalkyl substances (PFAS). The translational impacts of DMAC will include new methods for tackling the interface between environmental chemical sampling and resultant public health consequences, distributed software to make these methods available to SRP teams addressing similar questions in other compound families, and promotion of computational fluency in the next generation of Environmental Health Scientists.