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Superfund Research Program

Data Management and Analysis Core (DMAC)

Project Leader: Harrison Dekker
Co-Investigators: Gavino Puggioni, Marie-Abele Bind (Massachusetts General Hospital)
Grant Number: P42ES027706
Funding Period: 2022-2027
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2022-2027)

To understand the link between PFAS exposure and disease, there is a need for data integration from a broad range of scientific disciplines and for researchers to acknowledge the importance of the entire lifecycle of the data in a context beyond their immediate research objective. The long-term goal of the Data Management and Analysis Core (DMAC) is to establish a data science infrastructure that promotes best practices to produce high-quality data that are Findable, Accessible, Interoperable, and Reusable (FAIR), and that will be easily applicable to other interdisciplinary team projects.

The DMAC’s overall objective is to work closely with all Center project members and equip them with low-cost, user-friendly, FAIR- integrated processes, as well as cutting-edge statistical and computing methods. Guided by the team’s experience, the DMAC has four specific aims.

  • Develop, coordinate, and monitor a user-friendly, easily- accessible infrastructure and processes for creating, storing, and sharing data and metadata, irrespective of size, both internally and publicly.
  • Address metadata needs across all STEEP research data products.
  • Provide integrative methodological and computational support, as well as develop mission-oriented methods.
  • Develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP projects.

The approach is innovative because it departs from the status quo by providing an easy-to- implement, modern, and integrative data management infrastructure that is compliant with all FAIR principles and QA/QC. It also provides cutting-edge statistical methods (e.g., causal inference, Bayesian, and time series models) to draw mathematically-precise inferences from complex data structures (e.g., non-randomized, longitudinal), and high-performance computing resources. The DMAC’s activities advance and expand the use of FAIR-compliant research in the field of environmental health with the potential to inform policy makers with precise and reliable findings and help reduce the reproducibility crisis.

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