Reflections on the 2024 Emerging Pedagogies Summit
24 October 2024
The Emerging Pedagogies Summit is an annual event hosted by the Learning Innovation and Lifetime Education (LILE) group at Duke University, and I, Nicole Corbin, instructional designer at NSF Unidata, had the pleasure of attending. This year’s event was packed with thoughtfully curated topics relevant to the NSF Unidata higher education community, including AI and workforce development.
[Read More]netCDF vs Zarr, an Incomplete Comparison
09 September 2024
At NSF Unidata, we have been supporting and developing netCDF standards and packages since the original release of netCDF in 1990. We strongly believe in the usefulness of netCDF Common Data Model for Earth Systems Science data, and for other types of data! NetCDF files can be used efficiently in machine learning modeling applications and can be used as a virtual Zarr datasets.
NSF Unidata has been urged by our community to investigate options to allow netCDF to work more easily with modern cloud-based infrastructure. Based on the strong interest and rapid adoption of Zarr by the community, the netCDF team decided to begin working with the Zarr community to ensure that these two widely used data storage mechanisms can interoperate if necessary.
[Read More]Convolutional Neural Networks (CNNs) for Earth Systems Science
06 June 2024
Convolutional Neural Networks (CNNs) are a powerful class of deep learning models widely applied in Earth science for image analysis, classification, and regression problems. Leveraging the Keras framework in python, CNNs can efficiently process and extract spatial features from 2D and 3D remote sensing, model output, and other Earth Systems Science (ESS) data types.
[Read More]Why is the Keras 3 Release a Big Deal for the Deep Learning Community?
18 April 2024
The Keras package is an open-source library that provides a Python interface for deep learning. Keras is intended to be a user-friendly, modular, and extensible way to enable fast experimentation with deep neural networks. With Keras version 3, the package provides APIs for using three backends: TensorFlow, Jax, and PyTorch.
[Read More]04 March 2024
K Nearest Neighbors (KNN) is a supervised machine learning method that "memorizes" (stores) an entire dataset, then relies on the concepts of proximity and similarity to make predictions about new data. The basic idea is that if a new data point is in some sense "close" to existing data points, its value is likely to be similar to the values of its neighbors. In the Earth Systems Sciences, such techniques can be useful for small- to moderate-scale classification and regression problems.
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