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Your Environment. Your Health.

Environmental Health Language Collaborative Harmonizing Data. Connecting Knowledge. Improving Health.

Become Involved

  • Sign up for our email distribution list and join the community of researchers, ontologists, informaticists, and engineers working together on environmental health common language standards
  • Check out the compilation of ontology resources

If you have questions about the community, use cases, or workshop events, please contact Stephanie Holmgren, Office of Data Science.

Welcome to the website for the proposed Environmental Health Language Collaborative!

The Collaborative is a new initiative to advance community development and application of a harmonized language for describing Environmental Health Science (EHS) research. In 2021, we strive to define the Collaborative and begin working together.

We welcome diverse representation of expertise, needs, and scientific interests to make this a successful and sustainable community.

To start the process of community building, several working teams have drafted the community name, mission, and goals; model structure; and use cases.

Several webinar events have been held during 2021 to help set the stage, begin discussions, and draft plans for how to make progress on the Collaborative’s goals.

We welcome your input on the proposed community and look forward to your active and engaged participation as we work towards developing a community-driven harmonized environmental health language.


The generation of environmental health science (EHS) data continues to increase at an exponential rate. Leveraging this data to answer large-scale complex research questions is a challenge today and will continue to be as new methods arise and sophisticated statistical and machine learning approaches are increasingly adopted and automated.

A primary goal of the NIEHS Strategic Plan is to support Data to Knowledge to Action – the translational cascade from research results into a collective body of knowledge that ultimately informs and supports public health action. The development of innovative data science and data-driven approaches is integral to this goal for EHS specifically, and health initiatives generally.

An essential component to creating knowledge from data is to describe data with a harmonized language. Applying a harmonized language to environmental health data enhances its value by enabling consistent classification of data, increasing interoperability of databases, and promoting data sharing, reuse, and reanalysis; thereby, accelerating the pace of scientific discovery in EHS research.

This initiative is based on work begun at an NIEHS-sponsored September 2014 workshop on Laying a Community-based Foundation for Data-driven Semantic Standards in Environmental Health Sciences and will leverage additional work achieved at the Computable Exposome Workshop.

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