Exposure data, by its spatio-temporal character, is difficult to conceptualize and work with without visualization tools. To facilitate broad access to our exposure resources, we propose the development of an exposure visualization environment for exploring environmental and socio-environmental exposures. The plan envisions reusing existing Green data sources and visualization software components. We also envision working with NIEHS to pilot this approach in conjunction with their Environmental Polymorphism Registry, as well as several other initiatives.
Environmental Exposure Ontology Concept Modeling - Fully engage environmental data with relevant ontologies and models.
a. Annotate: Review annotation of each Green Team Exposures web API with identifiers from ontologies such as Schemas.org, Identifiers.org, PubChem, CHEMBL, MeSH, and ENVO. Follow annotation patterns and guidelines in the Translator API Registry.
b. Inventory: Document missing identifier schemes and concepts where we’re unable to find appropriate matches.
c. Conceptual Mapping: Map exposure concepts to upper ontologies like the biolink-model and into the Reasoner machine question framework to provide a language bridge between exposures and reasoners.
Source Type Service - The existing exposures service delivers concentrations. Develop an additional API for delivering exposure sources. This would be capable of fetching specific point sources of different types which would be incorporated into the visualization.
CMAQ Exposures - Expand existing exposure API to include additional pollutants for the longer time-series. We already started this after the last Hackathon, but this won't be done by end of this year.
ICEES - Use ICEES to answer specific CQs and develop manuscripts that integrate all components.
Note that (1) , (2), and (3) are priorities for the 2019 award period.
Overview
Exposure data, by its spatio-temporal character, is difficult to conceptualize and work with without visualization tools. To facilitate broad access to our exposure resources, we propose the development of an exposure visualization environment for exploring environmental and socio-environmental exposures. The plan envisions reusing existing Green data sources and visualization software components. We also envision working with NIEHS to pilot this approach in conjunction with their Environmental Polymorphism Registry, as well as several other initiatives.
Plan
Exposure Explorer: Create a visualization interface a. Examine the architecture of existing Institute for the Environment web based visualization systems. b. Repurpose components of the existing systems while using Green Team APIs as data sources. These includ: Roadway/School Exposures API; CMAQ Airborne Pollutant Exposures API; and ACS Socio-Environmental Exposures API.
Environmental Exposure Ontology Concept Modeling - Fully engage environmental data with relevant ontologies and models. a. Annotate: Review annotation of each Green Team Exposures web API with identifiers from ontologies such as Schemas.org, Identifiers.org, PubChem, CHEMBL, MeSH, and ENVO. Follow annotation patterns and guidelines in the Translator API Registry. b. Inventory: Document missing identifier schemes and concepts where we’re unable to find appropriate matches. c. Conceptual Mapping: Map exposure concepts to upper ontologies like the biolink-model and into the Reasoner machine question framework to provide a language bridge between exposures and reasoners.
Source Type Service - The existing exposures service delivers concentrations. Develop an additional API for delivering exposure sources. This would be capable of fetching specific point sources of different types which would be incorporated into the visualization.
CMAQ Exposures - Expand existing exposure API to include additional pollutants for the longer time-series. We already started this after the last Hackathon, but this won't be done by end of this year.
ICEES - Use ICEES to answer specific CQs and develop manuscripts that integrate all components.
Note that (1) , (2), and (3) are priorities for the 2019 award period.
Please see this doc for additional information.