USGCRP Metadata Access Tool for Climate and Health (MATCH)

MATCH is a searchable clearinghouse of publicly available Federal metadata (i.e. data about data) and links to datasets on public health and climate change. The MATCH database records are available in the categories of: allergies, cancer, cardiovascular disease, environmental exposures, extreme weather events, food, heat, human development, infectious disease, mental health and stress, neurological disease, respiratory disease, and water. Most metadata on MATCH pertain to geospatial data sets ranging from local to global scales. 

The goals of MATCH are to:

1. Provide an easily accessible clearinghouse of relevant Federal metadata on climate and health that will increase efficiency in solving research problems.

2. Promote application of research and information to understand, mitigate, and adapt to the health effects of climate change.

3. Facilitate multidirectional communication among interested stakeholders to inform and shape Federal research directions.

4. Encourage collaboration among traditional and non-traditional partners in development of new initiatives to address emerging climate and health issues.

MATCH is a project of the Interagency Cross-Cutting Group on Climate Change and Human Health (CCHHG) of the U.S. Global Change Research Program (USGCRP). The CCHHG is charged with planning, coordinating, implementing, evaluating, and reporting on Federal research on the impacts of global climate change on human health in order to build more resilient communities. Technical support for MATCH is provided primarily by the CCHHG Data Integration (DI) work stream and its interagency members.

Short-term plans for MATCH include providing users with the ability to contribute metadata, identifying a “Top 100” list of datasets used by climate and health researchers, adding metadata  from  other  federal  agencies, and  highlighting  case studies. Long-term goals include supporting data overlay - identifying datasets with observations that overlap spatially and chronologically - and ultimately, providing a platform for integrating at least a critical subset of full datasets.


If you have any trouble accessing the website link above, please find here an archived page. You may find this has limited use.

Publication Date: 2014

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