# Data Management & Analysis Core (DMAC)

> **NIH NIH P42** · LOUISIANA STATE UNIV A&M COL BATON ROUGE · 2021 · $133,347

## 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:** 10116397
- **Project number:** 5P42ES013648-09
- **Recipient organization:** LOUISIANA STATE UNIV A&M COL BATON ROUGE
- **Principal Investigator:** Donald E Mercante
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $133,347
- **Award type:** 5
- **Project period:** 2009-08-15 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10116397

## Citation

> US National Institutes of Health, RePORTER application 10116397, Data Management & Analysis Core (DMAC) (5P42ES013648-09). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10116397. Licensed CC0.

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