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Your Environment. Your Health.

Michigan State University

Superfund Research Program

Data Management and Analysis Core

Project Leader: Eric P. Kasten
Grant Number: P42ES004911
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) coordinates data management and analysis within the Michigan State University (MSU) Superfund Research Program (SRP) to support the identification of sensitive populations and reduce exposure to toxic aryl hydrocarbon receptor (AHR) ligands. Implementation of a comprehensive data management and analysis plan (DMAP) encourages data sharing and interoperability, and promotes the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. DMAC facilitates the collection of MSU SRP data sets into a center-based data commons to support data exploration, visualization, and analysis. Using established minimum information requirement standards and ontologies, DMAC captures the essential information required to enable reproduction of the processes used to create and analyze MSU SRC datasets and facilitates reproducible results for the translation of research to practice. Data quality assurance processes ensure sharing of high-quality data between projects and cores, as well as with other centers and stakeholders. To accomplish these objectives, DMAC:

  • Implements the Investigation Study Assay (ISA) framework for the collection of data and metadata from projects and cores. This framework promotes the use of data standards and ontologies which are critical for the application of FAIR principles. The ISA infrastructure is used to collect, validate, and archive all data produced by the MSU SRC.
  • Establishes a center-based data commons using the open source Gen3 data commons framework which co-locates data, analysis tools, and computational resources. The data commons enhance data interoperability and sharing through a web-based user interface and standardized data model and is used to integrate the disparate datasets generated by the SRC.
  • Institutes quality assurance and quality control procedures and processes to assure data set integrity and that curated data sets are consistently used and understood across all projects and cores.
  • Provides center-wide training in data management and analysis principles.

Training activities are organized with the Research Experience Training Coordination Core (RETCC) to inform project leaders and trainees about optimal data management practices and procedures. Accomplishing these aims fosters data science approaches by assuring MSU SRP data can be found, accessed, and independently interpreted with the overall objective that data can be successfully used and reused in an interoperable manner. In summary, DMAC works closely with projects and cores to facilitate curation and sharing of data sets and analyses; training of MSU SRC staff; mentoring of trainees in best data management practices; and facilitate translation of research data processes into practice.

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