Core E: Data Management and Analysis Core (DMAC)

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Project Summary Although this is a revised submission of the Baylor-Rice Superfund Research Program (SRP) grant, the Data Management and Analysis Core (DMAC) is a completely new, and indeed, required core. The team represented by this Core is not new to data management or to multi- project programs of research and has a strong track-record of experience and accomplishment. The Data Management and Analysis Core (DMAC) will act as the central hub for the SRP. The DMAC will provide for storage, annotation, and integration of multidisciplinary data generated by the biomedical projects P2, P3, P4 and the engineering projects P1 and P5. The DMAC has the following aims: 1) Implement a robust and comprehensive Data Management Plan; 2) Provide expert analysis in the specialized areas of statistics and bioinformatics for all Projects; 3) Develop new SRP-related data and management and data analysis methods; 4) Provide data management and analysis education and training for the graduate students and postdoctoral trainees connected with Superfund projects. The DMAC will act as the central resource for storage and cross-project access to chemical, physical, biological, and multi-omics data generated by the SRP projects and cores. In collaboration with the SRP investigators, the DMAC will accept a wide variety of data formats, not just ‘big data’, and will systematically incorporate metadata describing samples and laboratory conditions. The DMAC will leverage the extensive experience and infrastructure of the Duncan Cancer Center’s Biostatistics and Informatics Shared Resource, especially for biobanking and sample information management. We will work with the engineering projects to extend this infrastructure to the engineering projects. The DMAC will carry out primary and integrative data analysis for the biomedical projects and engineering projects. The analysis arm of the DMAC will provide advanced integrative methods spanning both omics and engineering data, including omics/phenotypes/chemical data multivariate regression and deep learning. In consultation with the Administrative Core and SRP investigators, the DMAC will enact data governance policies, enabling appropriate role-based access to data, primary and secondary analyses for investigators from the SRP, and from other SRPs nation-wide. The DMAC will submit the data to appropriate national repositories for each data type, and enhance the ability of the scientific community to find and retrieve the data, guided by the FAIR principles.