Summer 2024 NSF Unidata Interns Wrap Up Their Projects
15 August 2024
The Unidata Program Center's two summer student interns — Ana Castaneda Montoya from the University of Michigan and Leo Matak from the University of Houston — have come to the end of their summer appointments. After a summer of dedicated work they presented the results of their projects to the UPC staff on July 31, 2024.
[Read More]Recent Changes at the NSF Unidata Program Center
13 August 2024
In the wake of the U. S. National Science Foundation's award of financial support in response to NSF Unidata's most recent core program funding proposal, there have been several changes at the Program Center. This article attempts to explain the Program's current situation, what changes have been made, and what we are planning to do next.
[Read More]NSF Unidata Funding Proposal Approved by U.S. National Science Foundation
16 July 2024
The NSF Unidata Program receives the majority of its funding from the U.S. National Science Foundation. Every five years, the program submits a new proposal for core program funding to the NSF, outlining past accomplishments and describing plans for future activities.
We are please to announce that our most recent five-year funding proposal, Unidata Reimagined: New Approaches to Community Data Services, has been awarded.
[Read More]NSF Unidata at the 2024 Earth Educators' Rendezvous
15 July 2024
NSF Unidata is at the 2024 Earth Educators' Rendezvous in Philadelphia, PA the week of July 15-19, 2024. Join Instructional Designer Nicole Corbin and AI/ML Software Engineer Thomas Martin on Friday's poster session to discuss their ongoing collaboration with Metropolitan State University of Denver, Machine Learning Foundations and Applications in the Earth Systems Sciences.
[Read More]New eLearning: Machine Learning Foundations in the Earth Systems Sciences
12 July 2024
Announcing a new eLearning module available now on Unidata eLearning: Machine Learning Foundations in the Earth Systems Sciences. This no-code module is designed to guide you through the very basics of supervised machine learning in the Earth Systems Sciences. You will discover how machine learning is currently being used by scientists, examine the process for supervised machine learning model development, explore how data plays a crucial role in making good predictions, and how to be an effective and ethical user of machine learning tools. You will also learn that machine learning is not a catch-all solution to every problem!
[Read More]
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