Cores

 

Administrative/Translation Core

The Administrative Core housed at the Harvard T.H. Chan School of Public Health and representative of all participating institutions organizes the intellectual activities of the Superfund Research Center (SRC). The Core’s major responsibilities are to oversee the scientific progress and integration of SRC components, including successful mentoring of trainees, fostering communication between the SRC and diverse stakeholders, providing fiscal oversight, carrying out research translation, and ensuring compliance with NIH requirements for data and resource sharing and protection of human subjects.
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Research Experience and Training Coordination Core (RETCC)

The Research Experience and Training Coordination Core develops resources to supplement trainee research activities beyond individual projects to promote: 1) cross-disciplinary experience in environmental science; 2) enhanced development of communication and leadership skills; and 3) understanding of the wide-ranging impact of Superfund research and the implications of this context on professional and intellectual development.
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Community Engagement Core (CEC)

Communities that are exposed to metals often have limited resources, partnerships, and scientific knowledge on adverse health effects nor means to reduce or mitigate exposure. The goal of the Community Engagement Core is to use an approach based on bi-directional communication, citizen science, and co-development and implementation of an intervention to remediate metals exposures to reduce exposure in Superfund affected communities.
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Data Management and Analysis Core (DMAC)

The Data Management and Analysis Core (DMAC) will provide data management, biostatistical, bioinformatics, and geographical information system (GIS) support and ensure resource sharing and reproducible science for all four projects and all cores supported by the program. In addition to this support, core faculty and researchers will engage in mission-related research that will develop methods to integrate high dimensional exposure, molecular, and phenotypic data and train Superfund investigators in best practices for data management and analysis.
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