Explore Atlantic Storms

A repository designed to facilitate exploration of hurricane data by STEM teachers attending the ESIP Education Committee's 2019 Summer Workshop.

Participants will explore the Jupyter notebook, ExploreAtlanticStorms.ipny, to plot hurricane tracks and plot graphs using simple statistics to explore hurricanes in the Atlantic through a computational narrative.

Welcome

There are many flavors of Jupyter environments: Jupyter Notebook, JupyterLab, JupyterHub, Google Collaboratory. Some environments run on the desktop while others run through a web browser. For the purposes of this workshop, each of these provides an interactive computing environment.

In this workshop, we are using Google Colaboratory which is a Jupyter notebook environment that requires no setup to use.

The name Jupyter refers to the three coding languages, Julia, Python, and R, that are pillars of the modern scientific world.

Workshop

Overview of workshop session

• Slides

Time to dive in!

We are using Google Colaboratory for this workshop. You need to have a Google account and be signed in to use Colaboratory.

• Click this link to open Colaboratory > Google Colaboratory
• Select File > Open notebook...
• Select Github Tab
• Type github user name: shelley-e-olds then hit Return on your keyboard
• Select ExploreAtlanticStorms.ipynb

Citation: [This notebook was inspired by... ]

• Explore Atlantic Storms Notebook is based on a notebook created by Mandli with modifications made by LuAnn Dahlman, Shelley Olds, Keith Maul, and Sean Gordon.

• Jupyter notebook background, pedagogical discussion, and example is informed by the handbook and chapter: Teaching and Learning with Jupyter: Chapter 7.6 Investigating hurricanes

More about Jupyter:

Jupyter environments have three components: notebook documents, kernels, and notebook editor:

• Notebook documents: Self-contained documents that contain all content cells of narrative text, executable code cells, plots (maps), images, equations, and inputs and outputs of the computations. Notebooks are similar to a textbook with word processing (such as Word), as well as interactive math functions (such as Excel), image generation and display, and coding.
• Kernels / Runtime: Separate processes started by Jupyter that runs the code in a notebook and returns output back to the notebook web application.
• Notebook editor: The editor enables you to create and run notebook documents. It is an interactive application for writing and running code interactively and editing notebook documents.

More about notebook documents

• Text areas use "markdown text" which is similar to html. (Markdown Cheat Sheet to help with writing markdown text)
• Code cells can be run!
• Each notebook document runs using its own kernel.
• Each notebook is complete and self-contained. When we open a Notebook document, we'll pull in the necessary code, libraries, and tools to make the notebook run.
• Jupyter Notebook documents very useful for learning topics that are step-by-step to tell an interactive, computational story.

Why use Jupyter notebooks & coding in teaching?

1. Engagement
2. Experimentation, Modification, Sharing, & Remixing
3. Agency
4. Collaboration
5. Creativity & Problem solving
6. Documentation

Notebook details

Your Python environment requires the following:

• python >=3.6
• numpy >= 1.16.4
• matplotlib >= 3.1.1
• basemap >= 1.2.0

This workshop has been tested in Jupyter >= 4.4.0 and Google colaboratory.

Atlantic Storms Notebook (NBViewer Link)

• This is the main notebook for this repository

Data Scrubber Notebook (NBViewer Link)

Explaining Python Imports (NBViewer Link)

Discussion / Questions / self-guided exploration

Additional resources for further exploration

• Teaching and Learning with Jupyter is a tutorial handbook from an educator's perspective. The first 4 chapters introduces Jupyter notebook concepts, discuss reasons for using noteboooks in teaching and learning, and pedagogical approaches for notebooks. They also provide many examples to try out.
• Google Colaboratory FAQs
• About Jupyter
• Google Colaboratory

Tutorials for exploration

• Colaboratory Getting Started
• Colaboratory Tutorial

Bibliography

• ESIPhub: Supporting Interactive Geoscience Workshops. (2018). HCL, National Data Service. Retrieved from https://github.com/nds-org/esiphub (Original work published 2018)
• Gordon, S. (2018). MILES: Metadata Improvement Lab at ESIP: schema.org for datasets. Python. Retrieved from https://github.com/scgordon/MILES (Original work published 2018)
• Gordon, S., Jelenak, A., & Habermann, T. (2018). MDeval: Python module for xml metadata analysis and reporting (Version .10). Retrieved from https://github.com/scgordon/MDeval (Original work published 2018)
• Jelenak, A., Gordon, S., & Habermann, T. (2017). Metadata Evaluation Web Service. Retrieved July 16, 2018, from http://metadig.nceas.ucsb.edu/metadata/evaluator
• JupyterHub — JupyterHub 0.9.1 documentation. (n.d.). Retrieved July 15, 2018, from https://jupyterhub.readthedocs.io/en/stable/
• JupyterLab Documentation — JupyterLab 1.0 Beta documentation. (n.d.). Retrieved July 15, 2018, from http://jupyterlab.readthedocs.io/en/stable/
• Structured Data Testing Tool. (n.d.). Retrieved July 16, 2018, from https://search.google.com/structured-data/testing-tool/u/0/