# Data Management and Analysis Core

> **NIH NIH P42** · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · 2022 · $159,838

## Abstract

PROJECT SUMMARY – Data Management and Analysis Core (DMAC)
The Data Management and Analysis Core (DMAC) provides state-of-the-art data management support and
cutting-edge statistical and geospatial analysis assistance for the Research Projects and Cores of UNM METALS
Phase 2 Center. The DMAC will be co-directed by Li Luo and Yan Lin with complementary and collaborative
experience in biostatistical and geospatial analyses to manage all aspects of the Core. DMAC members include
eight quantitative scientists with complementary expertise in data management and statistical and geospatial
analysis for a variety of scientific domains. DMAC members have backgrounds and expertise in modern data
management, biostatistical methods, geospatial analysis, Bayesian methods, causal inference, as well as
analyses of data from basic science, translational research, population studies, and environmental health
disparities research. The team members have collaborated for over five years on multiple environmental health
projects, and have streamlined the workflow from data collection, quality control, statistical and geospatial
analysis support, and methodology developments to enhance the analysis of complex metal mixtures. The
DMAC will implement and provide data management support that is compliant with four foundational principles:
Findability, Accessibility, Interoperability, and Reusability (FAIR). DMAC will develop and utilize a centralized
data repository that connects different types of data storage systems and FAIR platforms to faciliate data sharing
and integration. The data and analytical support provided by the Core will contribute to many aspects of the
research process including, but not limited to, efficient study design, appropriate monitoring of data safety,
enhanced data management capacity, state-of-the-art statistical and geospatial analyses, assistance in the
development of study protocols and contract proposals, as well as sample size and power calculations. In
addition, DMAC members will assist Research Projects and Cores in preparing summary analytical reports and
manuscripts. Furthermore, DMAC will develop integrated statistical and geospatial modeling to refine the risk
classification based on study participants’ comprehensive environmental exposure profile, and to understand the
mechanisms of the adverse effects of toxic exposures on the health outcomes mediated through intermediate
biomarkers. Core faculty will also provide educational opportunities to Center investigators and trainees through
lectures, workshops, and hands-on training. The collaborative efforts of Core members will contribute not only
to effectively pursuing hypothesis-driven research questions but also to developing novel research questions
and methods for complex analyses of large datasets. Core members will work closely with METALS Research
Projects and Cores to develop and integrate new methodologies into various research projects. The DMAC is
well-positioned to promo...

## Key facts

- **NIH application ID:** 10353209
- **Project number:** 2P42ES025589-06
- **Recipient organization:** UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
- **Principal Investigator:** Li Luo
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $159,838
- **Award type:** 2
- **Project period:** 2017-08-15 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10353209

## Citation

> US National Institutes of Health, RePORTER application 10353209, Data Management and Analysis Core (2P42ES025589-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10353209. Licensed CC0.

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