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UCSB Event Fosters Networks between Environmental Researchers and Data Scientists

  • Admin
  • Sep 29, 2023
  • 1 min read

Feb. 2024 EDS Summit Applications Due Oct. 2

By Amina Aslam-Mir, SDSC REHS Intern


The modern era of science is accelerating the ability to develop data-driven solutions to environmental challenges, but the research to inform these solutions has not been as inclusive as it could be. Earlier this year, the National Center for Ecological Analysis and Synthesis (NCEAS) at the University of California Santa Barbara (UCSB) held an Environmental Data Science (EDS) summit to address issues of diversity and produced the trailblazing Santa Barbara Charter.


“This event served as a catalyst for re-envisioning how to make the field of Environmental Data Science welcoming and inclusive for all people,” said UCSB Professor Ben Halpern, Director of NCEAS.


The EDS Santa Barbara charter, which has been endorsed by national organizations and over 125 individuals within the EDS community, calls for increasing accessibility and diversity and provides principles on how to do so, as well as utilizing effective communication and resources among colleagues to prompt advancements within the field. These core principles aim to create an environment in which participants of all backgrounds can thrive.

“We are currently accepting applications for the 2024 EDS summit and look forward to reviewing the candidates next month,” noted Halpern.


The February 2024 EDS Summit will focus on the theme “Best Practices for Effective and Inclusive Communication” and panels will explore current practices of environmental data communication and translation methods. Explorations in all aspects of the data life cycle, from methods of data visualization to storytelling and journalism, will be discussed.

Those interested in the February Summit can find detailed information at the following URL: https://www.nceas.ucsb.edu/environmental-data-science-summit. Applications are due on October 2.

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The West Big Data Innovation Hub is supported by the National Science Foundation through awards #1916573, 1916481, and 1915774. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Learn more about the NSF Big Data Hubs community here.

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