Superfund Research Program
Data Management and Analysis Core
- Project Summary
Project Summary (2020-2025)
The Data Management and Analysis Core (DMAC) acts as the central hub for the Center, providing storage, annotation, and integration of multidisciplinary data generated by the biomedical and engineering projects. The DMAC has the following aims:
- Implement a robust and comprehensive Data Management Plan;
- Provide expert analysis in specialized areas of statistics and bioinformatics for all projects;
- Develop new SRP-related data management and analysis methods; and
- Provide data management and analysis education and training for the graduate students and postdoctoral trainees connected with Center projects.
The DMAC acts as the central resource for storage and cross-project access to chemical, physical, biological, and multi-omics data generated by the Center projects and cores. In collaboration with Center investigators, the DMAC accepts a wide variety of data formats, not just "big data," and systematically incorporates metadata describing samples and laboratory conditions. The DMAC leverages the extensive experience and infrastructure of the Duncan Cancer Center's Biostatistics and Informatics Shared Resource, especially for biobanking and sample information management. It works with the engineering projects to extend this infrastructure to meet their needs. The DMAC carries out primary and integrative data analysis for the biomedical and engineering projects. The analysis arm of the DMAC provides 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 Center investigators, the DMAC enacts data governance policies, enabling appropriate role-based access to data, primary and secondary analyses for Center investigators, and from other Centers nationwide. The DMAC submits data to appropriate national repositories for each data type and enhances the ability of the scientific community to find and retrieve those data, guided by the FAIR principles (Findable, Accessible, Interoperable, and Reusable).