Jupyter Jumpstart – An Introduction to Literate Programming
Introduction:
Consider the following quote:
“An article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result.” - Claerbout and Karrenbach, Proceedings of the 62nd Annual International Meeting of the Society of Exploration Geophysics. 1992
This quote highlights a central tenet of the open science movement, namely that researchers doing computational work need to share their code and data to allow replication of their results and to more clearly demonstrate their methods. But sharing is not yet commonplace, in part due to technical impediments. Typical problems have to do with reproducing the computing environment in which the code was used and include operating system dependencies, software version incompatibilities, and dependencies on proprietary components.
Project Jupyter is an effort to provide a solution to these challenges by creating an opensource, computational environment where text and code can be intermingled.
The Jupyter Notebook is a web application that allows you to create and shared documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. (Source: [http://jupyter.org])
Numerous examples of Jupyter Notebooks now exist across the disciplines and in both research and teaching. There are even examples of their usage in journalism. We contend that libraries can and should play a role in promoting open science tools like Jupyter.
Dates
June 6, 2016
Instructors
- Harrison Dekker, University of California, Berkeley (@vagrantscholar)
- Tim Dennis, University of California, San Diego (@jt14den)
Audience:
All librarians, programmers, and openscience advocates. The material covered is most applicable to academic and science librarians, but could apply to public librarians with an interest in promoting programming and/or ‘citizen science’. Some programming experience will be helpful, but the workshop will also benefit those with a strong interest in learning Python.
Location
ELAG 2016, Copenhagen
Resources
Thanks to SageMathCloud for the Jupyter infrastructure: 