Data Management and Analysis Core

Oregon State University, Corvallis OR

Investigators

Linked publications, trials & patents

Abstract

CORE SUMMARY – DATA MANAGEMENT AND ANALYSIS CORE Real-life exposures to hazardous substances from Superfund sites occur as mixtures of many contaminants. To improve and protect human health from exposure to hazardous substances, Superfund Research Centers (SRCs) must integrate biomedical research with environmental science and engineering. The integration of data from the diverse scientific disciplines in SRCs is critical if we are to fully understand the link between exposures and disease and prevent adverse health outcomes. Therefore, the data generated by the SRC represents an important research product that requires best practices for quality assurance, dissemination, and interoperability. The primary objective of the Data Management and Analysis Core is to discover, implement, and promulgate best practices for fostering and enabling the interoperability of data between biomedical research projects and environmental science and engineering projects to accomplish the goals of the overall Center. We will coordinate the development and refinement of an integrated data management plan for the entire Center. We will work closely with project & core leaders to identify data sharing platforms and to prioritize datasets for sharing across the program. We will establish data sharing guidelines and timelines. We will also continue to provide expert statistics for experimental design and multivariate data analyses. We will continue to develop and maintain software that provides an integrated data workflow for raw experimental data, important sample metadata, and downstream analysis pipelines. We will continue to model dose-response curves and biological response data. We will expand the SRP Analytics Portal with new data templates and implement data visualizations to facilitate data sharing and interpretation. The Portal also supports Findability, Accessibility, Interoperability and Reusability compliance and facilitate public data sharing with the scientific community. We will apply novel machine learning approaches to all processed data streams to link polycyclic aromatic hydrocarbon exposure to outcome, and to ultimately predict the effects of chemical mixtures on biological systems. We will work closely with project and core leaders to ensure high data quality throughout the lifecycle of data generation. We will review data, document quality control procedures that account for experimental, technical, or systematic problems, and resolve problems at each step of the data life cycle. We will integrate results across all research projects and cores and train the next generation of toxicologists to analyze their own data.