# Data Management and Analysis Core

> **NIH NIH P42** · TEXAS A&M UNIVERSITY · 2024 · $219,828

## Abstract

Data Management & Analysis Core (DMAC) ABSTRACT
The Texas A&M University Superfund Research Center aims to develop descriptive models and tools that can
predict the possible hazardous outcomes of chemical exposure during environmental emergencies while
providing powerful solutions that can mitigate their negative effects on human health. The Data Management &
Analysis Core (DMAC) is one of the key components of the Center that will support all projects and cores in their
data management, analysis, quality control needs. Directed by Dr. Efstratios N. Pistikopoulos and in collaboration
with co-Investigators Dr. Fred A. Wright, Dr. Lan Zhou, and Dr. Candice Brinkmeyer-Langford, the DMAC will
provide a number of essential services to the Center’s researchers by assisting them is achieving key
environmental and biomedical outcomes under four specific aims: (i) providing a new platform for data
management and sharing across the Center, (ii) applying best-practice analysis methods to Center data, (iii)
developing new methods that are urgently needed to solve the problems posed in the Projects, and (iv)
maintaining research and data quality control protocols for the Center. The DMAC will establish a data universe
(“dataverse”) for data sharing, integration, and collaboration. The “dataverse” will be used to manage Center
datasets where each component will securely deposit and access data through a web-based platform and ensure
Center generated data comply with Findable, Accessible, Interoperable, and Reusable (FAIR) principles. The
DMAC will also provide additional assistance in developing and utilizing advanced data science methodologies
for translating raw experimental data into actionable insights and predictive models for all projects. Project 1 will
perform and optimize ion mobility spectrometry and mass spectrometry analyses of complex environmental
samples; DMAC will provide guidance on geospatial sampling, feature selection, and classification analysis.
Project 2 will develop in vitro pediatric lung model to characterize respiratory risks from VOCs; DMAC will perform
concentration-response modeling, nonlinear, and spatial modeling techniques to evaluate the respiratory risks
from ambient VOCs. Project 3 will address pregnancy risk implications of exposures to hazardous substances
by developing a feto-maternal interface organ-on-a-chip model; DMAC will provide expertise in hypothesis
testing, regression analysis, and ANOVA testing for analyzing proinflammatory cytokine measures. Project 4 will
utilize in vitro cultures and reverse toxicokinetic analysis to characterize hazards of environmental mixtures;
DMAC will provide service in analyzing high-content screening data, high-throughput transcriptomics data, and
will perform population variability analyses. Project 5 will study the mitigation of adverse health effects of
chemicals through broad-acting sorption materials; DMAC will provide services for experimental design and
statistical testing. Th...

## Key facts

- **NIH application ID:** 10874511
- **Project number:** 5P42ES027704-08
- **Recipient organization:** TEXAS A&M UNIVERSITY
- **Principal Investigator:** Efstratios Pistikopoulos
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $219,828
- **Award type:** 5
- **Project period:** 2022-09-20 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10874511

## Citation

> US National Institutes of Health, RePORTER application 10874511, Data Management and Analysis Core (5P42ES027704-08). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10874511. Licensed CC0.

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