Data Management & Analysis Core (DMAC)

The DMAC provides two main services:  

1. Access to a data repository.
2. Statistical analysis assistance to Superfund Research Center investigators and research collaborators.  

The UofL DMAC assures the authenticity and quality of samples for long-term storage and secondary analysis.  

The core provides scientifically valid and rigorous statistical analysis of data and supports the development of innovative methods to enhance the basic and translational research efforts of center investigators. It provides state-of-the-art biostatistics and bioinformatics expertise and analytical support. Core biostatisticians develop new statistical methods, such as quantitative risk assessment models, multipollutant exposure analysis and land-use regression models for estimating cardiometabolic disease risk in exposed populations and for the analysis and evaluation of exposures and health effects.  

In collaboration with other projects and cores, DMAC statisticians harmonize the data by defining standards (e.g., creating searchable data dictionaries and defining variables) relevant for each project. This core allows center investigators easy access to centralized, high-quality data management and statistical services, and permits biostatisticians to be involved from the initial planning stage of a project (when statistical consultation is most beneficial) throughout its implementation, analysis and completion. The DMAC provides a stable and collegial environment that fosters long-term working relationships between biostatisticians and investigators and promotes sophisticated approaches to experimental design and analysis.    

DMAC is now the hub for all the data storage and processing for statistical analyses. This allows the center to establish data frameworks and data dictionaries that are consistent across cores. From core/project data, we have created searchable data dictionaries for center members to develop projects. The DMAC has created a new process for Superfund members to request statistical analysis support and new data pipelines to receive data from the Metabolism and Toxicity Core. DMAC has built a close relationship with the Training Core and provides ongoing project and statistical support to trainees.  

DMAC has created a process for both internal and external investigators to utilize Superfund-related data in their own projects. This includes the development of a manuscript proposal process in which investigators submit a manuscript proposal outlining their project (background, methods) as well as a data selection tool for investigators to select each variable required to complete their proposed project. Submitted manuscript proposals are reviewed by a committee of eight faculty members to approve the proposals.  

DMAC Faculty

• Dan Riggs (Co-Leader)
• Clara Sears (Co-Leader)
• Olfa Nasraoui