Data Management and Analysis Core

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PROJECT SUMMARY The University of Kentucky Superfund Research Center (UK-SRC) Data Management and Analysis Core (DMAC) has a goal to provide an overarching technology and research support infrastructure for the management and integration of data and information assets. Given that interdisciplinary research requires researchers to use methods and data from a range of disciplines, this goal addresses a critical need for interdisciplinary research to overcome hurdles posed by discipline-specific methods that impede progress when many disciplines attempt to share data. The DMAC is designed in alignment with FAIR (Findable, Assessable, Interoperable, and Reusable) data principles to encourage interaction of data users and sharing of data. The specific aims are: 1) coordination of projects and cores; 2) foster data integration, sharing, and interoperability; 3) ensure data quality assurance and quality control (QA/QC). The datasets to be managed and curated within the DMAC, encompass the full range of basic research to translational work, from animal- based studies to chemical analysis, from experimental studies evaluating remediation of hazardous substances to community engagement activities. The DMAC will engage regularly with project/core leaders to prioritize data sets. It will streamline analytic resources, data management, data quality validation, and data integration across projects and cores to improve efficiencies and reproducibility, enhance project coordination, and promote resource sharing. The DMAC will promote interoperability by incorporating FAIR guiding principles in its data dashboards, data archives, and its query exploration interface to permit projects and cores to better integrate. By developing common terminologies, data dictionaries, training, etc., the DMAC will facilitate a common data resource to foster sharing within, and beyond the UK-SRC. To facilitate interaction with investigators and trainees there will be (on-site and electronic) opportunities for regular interaction with biostatisticians, data scientists, and informaticians where formal and informal training can occur. These activities will be coordinated and advertised closely with the Research Experience Training Coordination Core (RETCC). The DMAC will create a new WHY ENVIRONMENT module to enable projects and cores to engage in investigator-initiated research translation activities to allow them to form new questions and hypotheses that can be tested in projects and cores and shared beyond the confines of UK-SRC. Lastly, the DMAC will incorporate best practices outlined by Data Observation Network for Earth (DataONE.org). It will establish a plan for data QA/QC so that UK-SRC research will not be subjected to pitfalls associated with poor quality data.