Data Management and Analysis Core (DMAC)

PROJECT SUMMARY/ABSTRACT – DATA MANAGEMENT AND ANALYSIS CORE (DMAC) To understand the link between PFAS exposure and disease, there is a need for data integration from a broad range of scientific disciplines and for researchers to acknowledge the importance of the entire lifecycle of the data in a context beyond their immediate research objective. The long-term goal is to establish a data science infrastructure that promotes best practice, i.e., high-quality data that are Findable, Accessible, Interoperable, and Reusable (FAIR), and that will be easily applicable to other interdisciplinary team projects. DMAC’s overall objective is to work closely with all STEEP project members and equip them with low-cost, user-friendly, FAIR- integrated processes, as well as cutting-edge statistical and computing methods. Guided by the team’s experience, DMAC will pursue four specific aims: (i) develop, coordinate, and monitor a user-friendly, easily- accessible infrastructure and processes for creating, storing, and sharing data and metadata, irrespective of size, both internally and publicly, (ii) address metadata needs across all STEEP research data products, (iii) provide integrative methodological and computational support, as well as develop mission-oriented methods, and (iv) develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP projects. The approach is innovative because it departs from the status quo by providing: (i) an easy-to- implement, modern, and integrative data management infrastructure that is compliant with all FAIR principles and QA/QC, (ii) cutting-edge statistical methods (e.g., causal inference, Bayesian, and time series models) to draw mathematically-precise inferences from complex data structures (e.g., non-randomized, longitudinal), and (iii) high-performance computing resources. The proposed research is significant because it is expected to advance and expand the use of FAIR-compliant research in the field of environmental health. Ultimately, such practice has the potential to inform policy makers with precise and reliable findings and help reduce the reproducibility crisis. STEEP’s DMAC will pursue these goals via these Specific Aims: Specific Aim 1: Develop and support infrastructure and processes for sharing data and metadata Specific Aim 2: Address metadata needs across all STEEP research data products: Specific Aim 3: Provide integrative statistical support Specific Aim 4: Develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP research projects