Superfund Research Program
Data Management and Analysis Core
Project Summary (2020-2025)
The unifying theme/goal of the University of Alabama at Birmingham Superfund Research Program (UAB SRP) is the development of a problem-focused (environmental lung injury secondary to heavy metal exposure), solution-oriented (biomarker discovery and therapeutic target identification in humans; mitigation/remediation in the air and soil), and integrated (bringing together biomedical scientists, engineers, ecologists, and social scientists) program. To achieve this goal, the UAB SRP has assembled five research projects and four Cores. The research projects have inherently complex data management and analysis needs in addition to the rigorous study design and data collection methodologies required by biomedical and human studies. The Data Management and Analysis Core (DMAC) provides centralized data management support for the research projects and biostatistics support as well as data integration and collaborative research. The DMAC provides database management that complies with NIH regulations and innovative statistical support via seamless consultation. Through collaboration and consultation, the DMAC provides UAB SRP investigators with assistance and direction in their experimental design, data collection, data management, and interim and final statistical data analyses. The DMAC leads the effort to design a biomedical and basic science research database with a user-friendly interface for data entry, data retrieval, and patient or sample tracking following the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles for scientific data management and stewardship, as well as data integration. It also develops procedures to ensure data quality, integrity, confidentiality, and sharing.
The DMAC is integrated into the UAB SRP research projects, including the trainees, from study inception through data collection, management, integration, and analysis. Its close partnership with the project leaders at UAB helps ensure the projects have proper biostatistical and data management support, are using appropriate and novel methods, and are correctly interpreting study findings for scientific publications.