NSF Unidata offers JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. For the Spring 2024 term, NSF Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to their requirements.

Representation of nodes in a Self Organizing Map.

A self-organizing map (SOM), sometimes known as a Kohonen map after its originator the Finnish professor Teuvo Kohonen, is an unsupervised machine learning technique used to produce a low-dimensional representation of a higher dimensional data set. SOMs are a specific type of artificial neural network, but use a different training strategy compared to more traditional artificial neural networks (ANNs). SOMs can be used for clustering, dimensionality reduction, feature extraction, and classification — all of which suggest that they can be important tools for understanding large Earth Systems Science (ESS) datasets.

Since 2018, Unidata has been offering JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. For the fall 2023 term, Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the requirements of university atmospheric science courses and workshops. Unidata will work with you to customize the technologies and data requirements for your class. By using the Unidata Science Gateway, instructors can add Jupyter notebooks used in their coursework to a dedicated JupyterHub hosted using Unidata's resources in the NSF Jetstream cloud. Once logged in to the JupyterHub, individual students access pre-configured computing environments that allow them to work with the notebooks interactively, making and saving their own alterations to existing notebooks or creating their own new notebooks.

Machine Learning systems are often configured around Graphics Processing Units (GPUs) rather than Central Processing Units (CPUs). Why should this be the case, in an era when CPUs are powerful and (relatively) inexpensive? This article provides some insights into what GPUs are and why they provide advantages for certain types of computations, including some commonly used for machine learning and modeling.

Since 2018, Unidata has been offering JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. In that time, nearly 850 users — mostly undergraduates in atmospheric science programs — have been able to take advantage of cloud-based resources to access pre-configured computational notebooks for learning and teaching objectives.

For the spring 2023 term, Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the requirements of university atmospheric science courses and workshops. Unidata will work with you to customize the technologies and data requirements for your class.

For the fall 2022 term, Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the requirements of university atmospheric science courses and workshops. Unidata will work with you to customize the technologies and data requirements for your class. By using the Unidata Science Gateway, instructors can add Jupyter notebooks used in their coursework to a dedicated JupyterHub hosted using Unidata's resources in the NSF Jetstream cloud. Once logged in to the JupyterHub, individual students access pre-configured computing environments that allow them to work with the notebooks interactively, making and saving their own alterations to existing notebooks or creating their own new notebooks.

For the fall 2021 term, Unidata is once again offering to provide universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the needs of university atmospheric science courses and workshops. By using the Unidata Science Gateway, instructors can add Jupyter notebooks used in their coursework to a dedicated JupyterHub hosted using Unidata's resources in the NSF Jetstream cloud. Once logged in to the JupyterHub, individual students access pre-configured computing environments that allow them to work with the notebooks interactively, making and saving their own alterations to existing notebooks or creating their own new notebooks.

In the spring of 2020, Unidata made an offer of resources through the Science Gateway project in order to facilitate online learning in response to the ongoing COVID-19 epidemic. Since that time, nearly 440 users — mostly undergraduates in atmospheric science programs — have been able to take advantage of cloud-based resources to access pre-configured computational notebooks for learning and teaching objectives.

For the spring 2021 term, Unidata is once again offering to provide universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the needs of university atmospheric science courses and workshops. By using the Unidata Science Gateway, instructors can add Jupyter notebooks used in their coursework to a dedicated JupyterHub hosted using Unidata's resources in the NSF Jetstream cloud. Once logged in to the JupyterHub, individual students access pre-configured computing environments that allow them to work with the notebooks interactively, making and saving their own alterations to existing notebooks or creating their own new notebooks.

Angelie Nieves-Jiménez

Angelie Nieves-Jiménez entered UCAR's Significant Opportunities in Atmospheric Research and Science (SOARS) program as a protégé in 2019, beginning an undergraduate research project studying sea breezes affecting her home island of Puerto Rico. As a rising senior at the University of Puerto Rico-Mayagüez, she was set to continue her research as part of the SOARS program in 2020. While the worldwide COVID-19 pandemic up-ended her plan to return to Boulder for the summer, a combination of remote teaching, mentorship, and computing resources allowed her to make progress on her research.

In the spring of 2020, Unidata made an offer of resources through the Science Gateway project in order to facilitate online learning in response to the ongoing COVID-19 epidemic. Since that time, nearly three hundred and fifty users — mostly undergraduates in atmospheric science programs — have been able to take advantage of cloud-based resources to access pre-configured computational notebooks for learning and teaching objectives.

For the fall 2020 term, Unidata is once again offering to provide universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the needs of university atmospheric science courses and workshops. By using the Unidata Science Gateway, instructors can add Jupyter notebooks used in their coursework to a dedicated JupyterHub hosted using Unidata's resources in the NSF Jetstream cloud. Once logged in to the JupyterHub, individual students access pre-configured computing environments that allow them to work with the notebooks interactively, making and saving their own alterations to existing notebooks or creating their own new notebooks.