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Harvard School of Public Health

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Superfund Research Program

Data Management and Analysis Core

Project Leader: Brent A. Coull
Co-Investigators: Xihong Lin, Rajarshi Mukherjee, Yaoyu Wang, Hufeng Zhou
Grant Number: P42ES030990
Funding Period: 2020-2025
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2020-2025)

The Data Management and Analysis Core (DMAC) provides data management, biostatistical, and bioinformatics support and ensures resource sharing and reproducible science for all four projects and all cores supported by the Center. In addition to this support, core faculty and researchers engage in mission-related research that develops methods to integrate high dimensional exposure, molecular, and phenotypic data. Data management and resource sharing activities span three tenets of reproducibility:

  1. Data reproducibility, which includes data management plans and other quality assurance / quality control procedures for all Center data;
  2. Analysis reproducibility, which analyzes protocols that are pre-specified, standardized, and rigorous; and
  3. Result replicability, accomplished by a resource sharing plan that meets the desired principle of data being Findable, Accessible, Interoperable, and Reusable (FAIR), introduced in the NIH Data Science Strategic Plan.

The DMAC directly supports each of the four Center projects and helps the Administrative and Community Engagement Cores translate complex research findings obtained from cutting-edge methodology to easily understood and easily visualized result summaries. Through its activities, DMAC fosters and enables the interoperability of data and methods between the Center’s biomedical and environmental science and engineering projects, as well as between this Center and the broader scientific community, accelerating the impact of the Center’s research.

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