Superfund Research Program

This page provides information and resources on data sharing, data repositories, citing data, data integration, and data science training.

Data Sharing Tools

• Bioinformatics Resource Manager, created by the Oregon State University SRP Center, is a platform for integrating and analyzing heterogenous high-throughput and high-content biological datasets.
• Chem-Mix, created by the Duke University SRP, is an app with tutorials related to statistical analysis of chemical mixtures using R.
• Conditional Toxicity Values, created by the Texas A&M University SRP Center, provides a web-interface to predict toxicity values with an uncertainty of an order of magnitude or less. 
• Germination Dose Response Curves App , created by the University of Oregon SRP Center, allows users to test the toxicity of one or two chemicals by identifying the concentrations required to reduce germination of seeds.
• Mwtab package for Metabolomics Workbench (MW), created by the University of Kentucky SRP Center, is a Python library that creates files formatted for MW deposition and allows for interaction with MW data.
• PUF-PAS Sampling Rate Model Interface, created by the University of Iowa SRP Center, provides information on environmental pollutants and can accurately predict the sampling volume of passive air samplers deployed anywhere in the world.
• Quantitative Histological Analysis Tool (QuHAnt), created by the Michigan State University SRP Center, enables high-throughput quantification of histological features and is available to all researchers.
• RDP’s Pyrosequencing Pipeline, created by the Michigan State University SRP Center, is a tool to simplify the processing of large RNA sequences by processing and converting the data to formats suitable for common ecological and statistical packages.
• UA SRC Olympus Application Server, created by the University of Arizona SRP Center, houses multiple apps (Cyverse account required) that visualize SRP data, analyze gene expression, predict regulation of genes, and more.
• Xander software package, created by the Michigan State University SRP Center, helps researchers working on novel methods for assembling protein-coding sequences for genes of interest from a metagenomic dataset.
• ZFIN, created by the University of Oregon SRP Center, provides researchers with optimized protocols for zebrafish research. Similar resources are available at the Tanguay Lab website.

Visualization/ Mapping Dashboards

• COVID-19 Pandemic Vulnerability Index (PVI) Dashboard, created by the North Carolina State University SRP Center and the Texas A&M University SRP Center, visualizes data to create COVID-19 risk profiles for every county in the United States. It is continuously updated with the latest data. Dashboard quick start guide.
• Drinking Water Tool, created by the University of California, Berkeley SRP Center, provides information on how communities across the state might be vulnerable to groundwater challenges, such as arsenic and PFAS contamination, that could affect availability of long-term safe and affordable drinking water.
• Flooding and Potential Environmental Contamination Sources in North Carolina ArcGIS Map, created by the Duke University SRP Center, helps individuals identify strategies to reduce or prevent potential exposure to harmful chemicals during flooding events.
• HGBEnviroScreen, created by the Texas A&M University SRP Center to serve the Houston-Galveston-Brazoria (HGB) region, this tool makes data on environmental, health, and social risks more available to community members and community-based organizations.
• NC ENVIROSCAN, created by the University of North Carolina Chapel Hill SRP Center, allows users to visualize trends across environmental contaminants, sociodemographic information, environmental justice indicators, and health outcomes throughout North Carolina.
• SuAVE Interactive Data Platform for Harris County, created by the Texas A&M University SRP Center, helps researchers identify parcels of land in need of regeneration and vacancy history. Users can combine the output table with the parcel Geographic Information Systems (GIS) shapefile to analyze or produce maps.

Data Repositories

• Collision Cross Section Database, created by the North Carolina State University and Texas A&M University SRP Centers, houses collision cross section values collected from numerous labs by drift tube ion mobility spectrometry in nitrogen buffer gas.
• Environmental Data Initiative, created by the University of New Mexico SRP Center, stores datasets in a findable, available, interoperable, and reusable manner.
• FunGene Pipeline Repository, created by the Michigan State University SRP Center, provides any researcher data for common ecofunctional genes and proteins along with integrated tools to build phylogenetic trees, test primers and probes, and download aligned sequences.
• Louisville Data Commons, created by the University of Louisville SRP Center, provides environmental and public health data about Louisville, Kentucky.
• Tox Data Commons, created by the Michigan State University SRP Center, is a Gen3 data commons for the collection, querying, and analysis of SRP data.

Metadata Standards/ Ontologies/ Data Integration

• Data Dictionaries, created by the Northeastern University SRP Center, provides metadata for human subject, environmental, targeted biological, and toxicity report data.
• Minimum Information about Animal Toxicology Experiments (MIATE), created by the University of Kentucky, Michigan State University, and University of Louisville SRP Centers, facilitates the standardized collection of in vivo toxicology experimental data.

Data Science Training

• Data Management and Data Operability Workshop, hosted by the University of Louisville SRP Center, fosters and enables best practice data management towards data interoperability in Louisville and across Superfund Programs to accelerate the impact of research for environmental health.
• inTelligence And Machine lEarning (TAME) Toolkit, created by the University of North Carolina Chapel Hill SRP Center, trains researchers in introductory data science, chemical-biological analyses, predictive modeling, and database mining for environmental health research.