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University of New Mexico

Superfund Research Program

Data Management and Analysis Core

Project Leader: Li Luo
Co-Investigator: Yan Lin
Grant Number: P42ES025589
Funding Period: 2022-2027
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2022-2027)

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 the University of new Mexico Metal Exposure and Toxicity Assessment on Tribal Lands of the Southwest (METALS) Phase 2 Center. The DMAC is 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 implements and provides data management support that is compliant with four foundational principles: Findability, Accessibility, Interoperability, and Reusability (FAIR). DMAC develops and utilizes a centralized data repository that connects different types of data storage systems and FAIR platforms to facilitate data sharing and integration. The data and analytical support provided by the core 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 assist research projects and cores in preparing summary analytical reports and manuscripts.

Furthermore, DMAC develops 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 also provides educational opportunities to center investigators and trainees through lectures, workshops, and hands-on training. The collaborative efforts of the core members 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 work closely with METALS research projects and cores to develop and integrate new methodologies into various research projects. The DMAC is well-positioned to promote multidisciplinary research collaboration and develop analytical strategies to contribute to the successful completion and integration of research projects and cores within the METALS Center.

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