Superfund Research Program
To solve complex environmental health problems, SRP grant recipients combined their expertise in diverse scientific disciplines to address a common research question, by integrating and reusing data generated from individual research projects. This page contains products from SRP collaborative data sharing products from the Data Management and Integration Use Cases.
Teams combined diverse datasets from disparate sources into one unified dataset or database to create new opportunities to understand complex environmental health questions.
Teams created dashboards to store, visualize, and interact with large amounts of data, and to communicate research findings.
Shared tools for exploring, processing, sharing, and analyzing data.
Code and workflows to enable reuse and facilitate reproducibility of project results.
Publications from collaborative research projects to combine expertise and answer complex environmental health questions.
Trainings and modules developed to facilitate implementation of the FAIR Principles.
- Data used in Bozack et al., 2021 , an epigenome-wide association study of arsenic exposure through drinking water (Arsenic Epigenetics META use case)
- Killifish genome and gene annotation resulting from integration of two large genome-sequencing datasets of fish populations living at Superfund sites. (Integrating Population Genomic Data to Understand Mechanisms of Chemical Susceptibility and Resistance use case)
- SuperFunBase is a portal hosting harmonized genomic data from different populations of Fundulus heteroclitus (Atlantic killfish). The platform is useful for examining genetic variants in different fish populations as well as human gene structure and expression profiles. Project page. (Integrating Population Genomic Data to Understand Mechanisms of Chemical Susceptibility and Resistance use case)
- The Xposome Portal is a collection gene and protein activity profiles resulting from short-term chemical exposures in cells. (Integration and Sharing of Xenobiotics-Associated Assays Across Species, Phenotypes, and Sites use case)
- SRP Data Analytics Portal provides public access to explore datasets linking environmental exposures to outcomes in zebrafish. (Integration and Sharing of Xenobiotics-Associated Assays Across Species, Phenotypes, and Sites use case)
- FAIRTox is an open-source web-based data exploration, visualization, and analysis application for gene and protein activity related to chemical exposures (Refining Species-Conserved Adverse Outcome Pathways (AOPs) of AhR-mediated Adverse Effects use case)
- Source-receptor-vis is an interactive source-receptor relationship visualization tool for airborne per- and polyfluoroalkyl substances. Project page (Validate and Develop Visualization and Reproducibility Documentation for Source-Receptor Relationships for Toxicants use case)
- Source-receptor-tools include other data tools related to source-receptor relationships. (Validate and Develop Visualization and Reproducibility Documentation for Source-Receptor Relationships for Toxicants use case)
- mwtab Python package facilitates reading, writing, and converting files in the mwTab format used by Metabolomics Workbench. (Two EUCS: Integration and Analysis of SRC-Generated Cardiometabolic Syndrome Data Streams from Animal Models, AND Refining Species-Conserved Adverse Outcome Pathways (AOPs) of AhR-mediated Adverse Effects use case)
- Minimum Information about Animal Toxicology Experiment (MIATE) Resources defines a minimum set of metadata required for animal toxicology experiments and recommended ontologies. The MIATE reporting standards are implemented as templates for ISAcommons, Center for Expanded Data Annotation and Retrieval, and Gene Expression Omnibus. (Two EUCS: Integration and Analysis of SRC-Generated Cardiometabolic Syndrome Data Streams from Animal Models, AND Refining Species-Conserved Adverse Outcome Pathways (AOPs) of AhR-mediated Adverse Effects use case)
Code and Workflows
- Beene D, Collender P, Cardenas A, Harvey CF, Huhmann LB, Lin Y, Lewis JL, Lolacono NJ, Navas-Acien A, Nigra AE, Steinmaus CM, van Geen AF. 2022. A mass-balance approach to evaluate arsenic intake and excretion in different populations. Environ Int 166:107371. doi:10.1016/j.envint.2022.107371 PMID:35809487
- Bozack AK, Boileau P, Wei L, Hubbard AE, Sille FC, Ferreccio Readi C, Acevedo J, Hou L, Ilievski V, Steinmaus CM, Smith MT, Navas-Acien A, Gamble MV, Cardenas A. 2021. Exposure to arsenic at different life-stages and DNA methylation meta-analysis in buccal cells and leukocytes. Environ Health 20:79. doi:10.1186/s12940-021-00754-7 PMID:34243768 PMCID:PMC8272372
- Powell CD, Moseley HN. 2021. The mwtab python library for RESTful Access and enhanced quality control, deposition, and curation of the metabolomics workbench data repository. Metabolites 11(3): doi:10.3390/metabo11030163
- Ramirez-Andreotta M, Walls R, Youens-Clark K, Blumberg K, Isaacs K, Kaufmann D, Maier RM. 2021. Alleviating environmental health disparities through community science and data integration. Front Sustain Food Sys 5:620470. doi:10.3389/fsufs.2021.620470 PMID:35664667 PMCID:PMC9165534
- Newman G, Malecha M, Atoba K. 2021. Integrating ToxPi outputs with ArcGIS Dashboards to identify neighborhood threat levels of contaminant transferal during flood events. J of Spa Sci 13: doi:10.1080/14498596.2021.1891149
- Malecha M, Kirsch KR, Karaye I, Horney JA, Newman G. 2020. Advancing the toxics mobility inventory: Development and application of a toxics mobility vulnerability index to Harris County, Texas. Sustainability 13(6):282-291. doi:10.1089/sus.2020.0067
- Roell K, Koval L, Boyles R, Patlewicz G, Ring C, Rider CV, Ward-Caviness C, Reif DM, Jaspers I, Fry RC, Rager JE. 2022. Development of the InTelligence and Machine LEarning (TAME) Toolkit for introductory data science, chemical-biological analyses, predictive modeling, and database mining for environmental health research. Front Toxicol 4: doi:10.3389/ftox.2022.893924 PMID:35812168 PMCID:PMC9257219
- The inTelligence And Machine lEarning (TAME) Toolkit provides learning modules for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.
- The free, virtual learning series Big Data in Environmental Science and Toxicology provides training from experts related to data sharing, handling large datasets, navigating statistical computing programs, and using online resources to predict chemical toxicity. Recordings of all six sessions are available online.