Building Pathways for Open Science Education using NASA datasets
Here we will create a hypothetical scientific use case: A user wants to use the cloud to analyze change in landcover over a certain region in the Amazon river basin over a period of 20 years. The goal of this tutorial then is to introduce users to working with NASA satellite imagery and utilizing Amazon Web Services' Open Data program. Through a series of standardized modules, the user is shown an end-to-end workflow for working with satellite imagery. The modules will teach users how to utilize geoscientific Python tools, visualize the data and utilize a parallel computation platform on the cloud (i.e. the Pangeo Platform).
The contents of this repository consists of a series of Jupyter Notebooks and can be viewed on an open source, free computational platform like Binder. To view the contents of this repo, click the launch binder icon above.
We encourage users to have basic knowledge of Python and Git. The Software Carpentry education materials is a great place to start.
|Introduction to Cloud Computing||Working with Big Data||Tutorial 1|
|AWS Open datasets||Working with s3 buckets||Tutorial 2|
|Multi-dimensional analysis||Introduction to Xarray||Tutorial 3|
|Parallel Computing||Introduction to Dask||Tutorial 4|
|Visualization||Introduction to HoloViews||Tutorial 5|
|Scaling-up||Using Dask and Pangeo||Tutorial 6|
You can request new content or give general feedback through Gitub issues or collaborate on these materials by creating a Pull Request.
The contents of this repository was developed by Aji John and Amanda Tan at the University of Washington with funding from the ESIPLab Summer 2020 Incubator Award 2020.