Superfund Research Program
Data Management and Analysis Core
Project Summary (2020-2025)
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 investigators to use methods and data from a range of disciplines, this goal addresses a critical need 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, Accessible, Interoperable, and Reusable) data principles to encourage interaction of data users and sharing of data. The specific aims are: (1) coordinating of projects and cores; (2) fostering data integration, sharing, and interoperability; 3) ensuring data quality assurance and quality control (QA/QC). The datasets 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 engages regularly with project/core leaders to prioritize datasets. It streamlines 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 promotes interoperability by incorporating FAIR guiding principles into its data dashboards, data archives, and query exploration interface to permit projects and cores to better integrate. By developing common terminologies, data dictionaries, training, etc., the DMAC facilitates a common data resource to foster sharing within and beyond the UK SRC. To facilitate interaction with investigators and trainees, there are both onsite and electronic/remote opportunities for regular interaction with biostatisticians, data scientists, and informaticians, where formal and informal training can occur. These activities are coordinated and advertised closely with the Research Experience and Training Coordination Core (RETCC). The DMAC is also creating 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 incorporates best practices outlined by the Data Observation Network for Earth (DataONE.org). It established a plan for data QA/QC so that UK SRC research will not be subjected to pitfalls associated with poor quality data.