The Data Management and Analysis Core (DMAC) will provide data management, biostatistical, bioinformatics, and geographical information system (GIS) support and ensure resource sharing and reproducible science for all four projects and all cores supported by the program. In addition to this support, core faculty and researchers will engage in mission-related research that will develop methods to integrate high dimensional exposure, molecular, and phenotypic data. Data management and resource sharing activities will span three tenets of reproducibility: (1) data reproducibility, which will include data management plans and other quality assurance / quality control procedures for all Center data, (2) analysis reproducibility, which will analysis protocols that are pre-specified, standardized, and rigorous, and (3) result replicability, accomplished by a resource sharing plan that meets the desired principle of data being Findable, Accessible, Interoperable, and Reusable (FAIR) introduced in the NIH Data Science Strategic Plan. The DMAC will directly support each of the four Center projects, help the Administrative and CEC Cores translate complex research findings obtained from cutting-edge methodology to easily-understood and easily-visualized result summaries, and work with the RETCC to provide training to Center faculty, students, and fellows working on Center-related projects in the areas of biostatistics, quantitative genomics, and data science, including methods to promote reproducibility and replicability. Through its activities, DMAC will foster and enable the interoperability of data and methods between the Center?s biomedical and environmental science and engineering projects as well as between this Center and the broader scientific community, accelerating the impact of the Center's research.
The Data Management and Analysis Core (DMAC) will provide data management, biostatistical, bioinformatics, and geographical information system (GIS) support and ensure resource sharing and reproducible science for all four projects and all cores supported by the program. In addition to this support, core faculty and researchers will engage in mission-related research that will develop methods to integrate high dimensional exposure, molecular, and phenotypic data and train Superfund investigators in best practices for data management and analysis. Through its activities, DMAC will foster and enable the interoperability of data and methods between the Center?s biomedical and environmental science and engineering projects as well as between this Center and the broader scientific community, accelerating the impact of the Center's research.
