As data science efforts grow in terms of variety and number of participants, analysis approaches, and evolving scientific content, vocabularies also grow and evolve. By supporting data and metadata vocabularies that are appropriate for community usage, data science centers will offer more effective integration and search services. This Resource aims to lead a community-driven effort to identify, develop, link, test, and promote languages and standards useful for exposure science and to develop and implement an infrastructure and tools that support finding and effectively using appropriate vocabulary, search, and inference services. We also aim to create a virtual community for evolving and maintaining ontologies as well as providing processes and methodologies that may live on beyond this data science center. Ultimately, these goals have the potential to provide a community-driven and community-accepted language for exposure science, thus facilitating the goals of the CHEAR Program in expediting progress in exposome-related research, analyses, and collaborations. The goals will be achieved through the following Specific Aims: 1) work with appropriate partners to identify promising candidate ontologies, controlled vocabularies, and metadata standards with appropriate overlap for CHEAR interests; and provide open-access encodings, including provenance encodings, for these features; 2) Build and engage the community for vocabularies and services, and produce a stakeholder-driven process for vocabulary identification, evaluation, mapping, and gap analysis; and 3) Develop an extensible online portal and services to support Data Science terminology work for CHEAR.
A well-designed epidemiologic study of children's health will be able to address the hypotheses the study is funded to investigate, but by accessing the resources of the CHEAR Data Center, studies will be able to exceed what they are funded to do and potentially accomplish research that was previously thought infeasible or too costly. Through the use of the CHEAR Data Center, investigators will be able to increase their productivity and advance the scientific community's ability to examine the environment on an exposome scale. Pooling multiple datasets in the Data Center repository in a cohesive and integrated manner will be made possible through the language standards we develop.
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