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Driving the ability to identify, access, harmonize, and use earth and atmospheric observations, environmental monitoring, and local data sources with health data.

Extreme weather and emerging environmental harms threaten the health and well-being of people in the U.S. and globally. In response to growing needs in this area, the NIH Data Accelerator aims to build cyberinfrastructure and sustainable extramural investments to empower the use of environmental health data to understand the 1) health impacts of environmental exposures and extreme weather and 2) critical value of interventions. An integral part to advancing this goal is to bolster an environmental health data savvy workforce that employs standardized environmental health data for research across the NIH portfolio and collaborates with the interdisciplinary community to advance new research capabilities.

Complementary Data Resources

The Connecting Health Outcomes Research and Data Systems (CHORDS)

Facilitating the Linking of Environmental and Health Data to Advance Patient-centered Outcomes Research

 
The CHORDS project strengthens data infrastructure to facilitate research connections between environmental exposures and health outcomes.

By providing accessible, interoperable data and analytical resources, CHORDS supports researchers, health practitioners, and public officials in assessing environmental health risks, developing evidence-based interventions, and anticipating future health challenges to better protect communities.

CAFÉ Dataverse and Coding Resources

CAFÉ, jointly led by Harvard T.H. Chan School of Public Health and Boston University School of Public Health, is the Research Coordinating Center of the NIH Health and Extreme Weather (HEW) Program.

The Harvard Dataverse is an open-source generalist data repository of commonly used weather and health data and linkages are stored, including spatial data.

CAFÉ also has a collection of code, software, and tutorials on GitHub allowing researchers to contribute, share, and reuse existing code and software for data processing and analysis to facilitate reproducibility and reusability.