# Core E: Data Management and Analysis Core (DMAC)

> **NIH NIH P42** · BAYLOR COLLEGE OF MEDICINE · 2020 · $143,205

## Abstract

Project Summary
Although this is a revised submission of the Baylor-Rice Superfund Research Program (SRP)
grant, the Data Management and Analysis Core (DMAC) is a completely new, and indeed,
required core. The team represented by this Core is not new to data management or to multi-
project programs of research and has a strong track-record of experience and accomplishment.
The Data Management and Analysis Core (DMAC) will act as the central hub for the SRP. The
DMAC will provide for storage, annotation, and integration of multidisciplinary data generated by
the biomedical projects P2, P3, P4 and the engineering projects P1 and P5. The DMAC has the
following aims: 1) Implement a robust and comprehensive Data Management Plan; 2) Provide
expert analysis in the specialized areas of statistics and bioinformatics for all Projects; 3) Develop
new SRP-related data and management and data analysis methods; 4) Provide data
management and analysis education and training for the graduate students and postdoctoral
trainees connected with Superfund projects. The DMAC will act as the central resource for
storage and cross-project access to chemical, physical, biological, and multi-omics data
generated by the SRP projects and cores. In collaboration with the SRP investigators, the DMAC
will accept a wide variety of data formats, not just ‘big data’, and will systematically incorporate
metadata describing samples and laboratory conditions. The DMAC will leverage the extensive
experience and infrastructure of the Duncan Cancer Center’s Biostatistics and Informatics Shared
Resource, especially for biobanking and sample information management. We will work with the
engineering projects to extend this infrastructure to the engineering projects. The DMAC will carry
out primary and integrative data analysis for the biomedical projects and engineering projects.
The analysis arm of the DMAC will provide advanced integrative methods spanning both omics
and engineering data, including omics/phenotypes/chemical data multivariate regression and
deep learning. In consultation with the Administrative Core and SRP investigators, the DMAC will
enact data governance policies, enabling appropriate role-based access to data, primary and
secondary analyses for investigators from the SRP, and from other SRPs nation-wide. The DMAC
will submit the data to appropriate national repositories for each data type, and enhance the ability
of the scientific community to find and retrieve the data, guided by the FAIR principles.

## Key facts

- **NIH application ID:** 9841258
- **Project number:** 1P42ES027725-01A1
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** SUSAN G. HILSENBECK
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $143,205
- **Award type:** 1
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9841258

## Citation

> US National Institutes of Health, RePORTER application 9841258, Core E: Data Management and Analysis Core (DMAC) (1P42ES027725-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9841258. Licensed CC0.

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