# Data Management & Analysis Core (DMAC)

> **NIH NIH P42** · LOUISIANA STATE UNIV A&M COL BATON ROUGE · 2024 · $166,771

## Abstract

Project Summary/Abstract: Data Management and Analysis Core (DMAC) 
The Data Management and Analysis Core (DMAC) is designed to enhance the LSU Superfund Research 
Program's (SRP’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 ultimate 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; (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 
experiences necessary for tackling the complex multi-disciplinary issues to be addressed by the LSU SRP. We 
will implement 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. Our Core 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 will expand 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 the use of 
reliable machine learning methods for dimension reduction in the investigation of the microstructural pathway 
of EPFR formation and decay mechanisms, among other advancements. Last, the DMAC will develop and 
promote 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 on conducting transparent, valid, generalizable, and repeatable research.

## Key facts

- **NIH application ID:** 10772184
- **Project number:** 5P42ES013648-12
- **Recipient organization:** LOUISIANA STATE UNIV A&M COL BATON ROUGE
- **Principal Investigator:** Qingzhao Yu
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $166,771
- **Award type:** 5
- **Project period:** 2009-08-15 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10772184

## Citation

> US National Institutes of Health, RePORTER application 10772184, Data Management & Analysis Core (DMAC) (5P42ES013648-12). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10772184. Licensed CC0.

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