# Data Management & Analysis Core

> **NIH NIH P42** · OREGON STATE UNIVERSITY · 2020 · $332,940

## 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 Programs
(SRPs) must integrate biomedical research with environmental science and engineering. The integration of
data from the diverse scientific disciplines in SRPs 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 SRP
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 (DMAC) 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 Superfund Research 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 customize the Experimental
Data Management System with data templates for all projects. We will also implement new data visualizations
in our Superfund Analytics Tool to facilitate data sharing across projects and cores. We will apply novel deep-
learning algorithms to all processed data streams in to link PAH exposure to outcome, and to ultimately predict
the effects of PAH 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 we will train the next
generation of toxicologists to analyze their own data.

## Key facts

- **NIH application ID:** 9841196
- **Project number:** 2P42ES016465-11
- **Recipient organization:** OREGON STATE UNIVERSITY
- **Principal Investigator:** Katrina M. Waters
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $332,940
- **Award type:** 2
- **Project period:** — → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9841196

## Citation

> US National Institutes of Health, RePORTER application 9841196, Data Management & Analysis Core (2P42ES016465-11). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9841196. Licensed CC0.

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