17 July 2024
The NSF Unidata THREDDS development team released the THREDDS Data Server (TDS) version 5.5 on July 16th, 2024. This release contains a number of security upgrades to third party libraries, a variety of bug fixes, and several new features and improvements. It is recommended that all TDS users upgrade to this version.
[Read More]netCDF-Java version 5.6.0 released
17 July 2024
The NSF Unidata THREDDS development team released netCDF-Java 5.6.0 on July 16th, 2024. This release contains a number of security upgrades to third party libraries, a variety of bug fixes, and several new features and improvements.
[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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