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05 June 2025

Linfeng Li

Linfeng Li joined the NSF Unidata Program Center as a student summer intern on May 19, 2025. Linfeng is a PhD student in Climate and Space Sciences and Engineering at the University of Michigan, where his research focus is in Planetary Sciences. “I am modeling the atmospheric dynamics of ice giants and lava planets, studying the potential intrinsic asymmetry of planetary atmosphere,” he says. “I've always been excited to learn how diverse and distinct the planetary environments are.”

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NSF Unidata Return to Operations

02 June 2025

We at NSF Unidata are pleased to announce that we have now received funding from the National Science Foundation (NSF) for the next year of the period of performance of our five-year award. This positive development allows us to end the current furlough of our staff and resume our operations. While we are grateful to receive our next increment of funding, we are mindful of the challenges that lie ahead with our continued funding given the administration’s proposed FY26 budget that cuts NSF’s budget by over fifty percent.

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Posted by Unidata News [ Comments [7] ]

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NSF Unidata Pause in Most Operations

09 May 2025

Due to the current gap in funding from the U.S. National Science Foundation (NSF), the NSF Unidata Program is pausing most operations. Nearly all staff will be furloughed until funds from our existing NSF grant become available.

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Posted by Unidata News [ Comments [7] ]

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netCDF-Java version 5.8.0 released

08 May 2025

The NSF Unidata THREDDS development team released netCDF-Java 5.8.0 on May 8th, 2025. This release contains a number of upgrades to third party libraries, a variety of bug fixes, and several new features and improvements.

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Posted by Sean Arms [ Comments [1] ]

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New eLearning: Supervised Machine Learning Readiness

07 May 2025

Announcing a new eLearning series available now on Unidata eLearning: Supervised Machine Learning Readiness. This learning series is a self-paced, beginner-friendly program designed for Earth systems scientists to explore the core principles of supervised machine learning. This series uses a combination of step-by-step frameworks, exploratory widgets, and low-code exercises in Jupyter Notebooks, to explore the full cycle of machine learning model development. No programming experience is required. By the end of the series, you will be able to recognize when machine learning is an appropriate tool and critically evaluate machine learning in Earth systems science contexts.

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Posted by Nicole Corbin [ Comments [0] ]