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Louisiana State University

Superfund Research Program

Data Management and Analysis Core

Project Leader: Donald Mercante (LSU Health Sciences Center - New Orleans)
Co-Investigator: Qingzhao Yu (LSU Health Sciences Center - New Orleans)
Grant Number: P42ES013648
Funding Period: 2020-2025
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2020-2025)

Data Management & Analysis Core Project Summary (2020-2024) The Data Management & Analysis Core (DMAC) enhances the Louisiana State University (LSU) Superfund Research Program’s understanding of how environmentally persistent free radicals (EPFRs) induce pulmonary/cardiovascular dysfunction and how to prevent formation, enhance decay, and limit exposure to EPFRs, with the goal of improving human health and the environment. The five projects and supporting cores in the LSU SRP present considerable data management and biostatistical challenges that are crucial to the overall success of the Center. The DMAC’s Specific Aims are to:

  1. Develop and implement a comprehensive data management plan for LSU SRP;
  2. Develop and implement informatics solutions, including data collection, distribution, and analysis tools and secure storage for data generated by LSU SRP projects and cores;
  3. Provide statistical expertise to SRP projects and cores;
  4. Provide expertise in the application and development of novel statistical models and methodology for analysis of complex multidimensional data; and
  5. Provide educational initiatives and resources to serve a wide audience of graduate students, postdoctoral researchers, and junior faculty.

DMAC members possess the knowledge, skills, and experience necessary for tackling the complex multi-disciplinary issues addressed by the LSU SRP. DMAC is implementing a comprehensive data management strategy, leveraging recent advances within the LSU system in high-speed computing and data distribution, along with stable and secure data collection, management, and storage platforms for facilitating multi-disciplinary collaborations. It is committed to promoting transparent and reproducible research through the adoption of software, providing time-stamped version control over documents, files, and code, such as the Open Science Framework and the "workflowr" R package for statistical analysis. The DMAC biostatisticians are expanding the toolsets available to the Superfund research community by developing novel approaches and methods for understanding the relationship between EPFR exposures and respiratory health effects using (multivariate) multiple mediation analysis, as well as reliable machine learning methods for dimension reduction in the investigation of the microstructural pathway of EPFR formation and decay mechanisms, among other advancements. Lastly, the DMAC is developing and promoting a wide array of initiatives in various formats and venues for educating SRP investigators, postdoctoral researchers, and graduate students on topics such as effective data management strategies; study design principles; and conducting transparent, valid, generalizable, and repeatable research.

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