New Public-Private Partnership Launched to Help Communities Bridge Gap Between Climate Data and Resiliency Planning

Future Earth is a partner on a newly-launched effort to put data and information to use in helping communities respond to the impacts of climate change.


Climate change is accelerating the intensity and frequency of extreme weather across the globe, with increasing risks to communities and businesses. The White House Office of Science and Technology Policy (OSTP), World Resources Institute (WRI), U.S. Global Change Research Program (USGCRP) and a network of partners today launched the Partnership for Resilience and Preparedness (PREP) to help communities, companies and investors use data to improve climate resilience planning.

Harnessing the data revolution to improve climate resilience efforts will require a diverse set of partners including government, civil society, the private sector, and international organizations.

PREP, which emerged out of the Climate Data Initiative, seeks to empower a data-driven approach to building climate resilience by:

  • Engaging communities to identify data needs.
  • Identifying and reducing the barriers to access, contribute, and use data and information products for climate resilience.
  • Developing an open-source platform to enhance access to and usability of climate-relevant data and information.
  • Translating data into actionable information to help inform planning decisions for people on the ground and providing access to the necessary tools and functionality communities need – data availability alone is not enough.
  • Providing data translators with easy access to usable data they need in order to develop the decision-support tools needed to inform and build climate resilience.

PREP will seek to expand data transparency so planners know if data comes from governments or commercial entities, clarifying data sources from the start to distinguish which data are most useful. Users will be able to share their plans, learn from others, and pull government data from multiple global datasets for specific locations.

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