Jupyter Newsletter 8 • October 19, 2016
Figshare+Jupyter, Foundations of Data Science Course Grant
Figshare Renders Jupyter Notebooks
Over the past few years, figshare has become an important part of the open-science ecosystem. It offers a website that allows researchers to upload, manage, publish and share their research with others. It has active partnerships with a number of universities and traditional academic publishers. For computational research, figshare does offer GitHub integration, but previously figshare did not support the rendering of Jupyter Notebooks as GitHub does.
We are pleased that figshare has recently (quietly) added inline rendering of Jupyter Notebooks to their website. This enables researchers to upload notebooks to figshare and have them rendered as static HTML on their website, for the world to see. Given the large number of computational researchers that are using the Jupyter Notebook, we feel this is an important step forward for open computational science. Here is an example of a paper published in the IOPScience journal, Science and Technology of Advanced Materials, that provides Jupyter Notebooks on their figshare page:
Foundations of Data Science Course at UC Berkeley
A team at Berkeley has been awarded a $150,000 Google grant and $30,000 of in-kind credits to further develop the computing environment used in Foundations of Data Science, Berkeley’s introductory course for undergraduate students of all majors. The effort will be led by Computer Science professor John DeNero, with participation from Jupyter’s Fernando Perez as co-Investigator.
According to Professor Ani Adhikari, the course is built on three interrelated perspectives: inferential thinking, computational thinking, and real-world relevance. Students conduct hands-on analysis and gain valuable experience by learning to use the Python programming language and completing assignments in the Jupyter Notebook. This is a highly ambitious application of the Jupyter architecture across the entire campus and undergraduate student body.
Jupyter for data-intensive workflows in High-Performance Computing
Fernando Perez and co-Investigator Shreyas Cholia from Lawrence Berkeley National Laboratory, have been awarded a Department of Energy Laboratory Directed Research and Development grant to support research on how to bring interactive computation to data-intensive analysis workflows in High-Performance Computing systems. Further details on the project can be found on the original grant proposal, and if you’re a postdoctoral scholar interested in the opportunity to join this team we encourage you to take a look at their recruiting post.
Featured Use Case: JupyterHub with nbgrader for Teaching
Kristen Thyng teaches Python for Geosciences at Texas A & M and she has recently created a classroom model that includes getting nbgrader working with a secure version of JupyterHub (using https). She has taken the time to document how she did it and if you’re interested in implementing something similar we encourage you to read the full write up.
http://kristenthyng.com/blog/2016/09/07/jupyterhub+nbgrader/
Featured Community Members
Thomas Kluyver has grown used to looks of confused surprise when he tells people that he studied plant biology just a few years ago. Open-source development, once a hobby on the side of this crop plant evolution research, has become a full time job since he joined the IPython/Jupyter team as a postdoc at UC Berkeley. Within the project, he mostly focuses on the IPython kernel and on Nbconvert; he also helps to maintain the R kernel for Jupyter.
Jessica Hamrick is a Ph.D. candidate at UC Berkeley in Psychology, having previously completed a B.S. and M.Eng. in Computer Science at MIT. Jess studies computational models of human cognition, with a focus on understanding mental simulation and imagination from a computational perspective using a combination of methods from machine learning and cognitive psychology. In addition, Jess is a strong advocate for open source and open science. She is a member of the Jupyter Steering Council and is the lead maintainer of nbgrader, a tool for creating and grading assignments in the Jupyter notebook, as well as nbflow, a tool for creating reproducible, one-button workflows with Jupyter notebooks.
Upcoming events
Swedish Bioinformatics Workshop 2016: October 20-21, 2016 The audience for this two day conference in Sweden is PhD-students working in bioinformatics. Core Jupyter contributor Min Ragan-Kelley will be presenting a workshop titled, “Using IPython and Jupyter for reproducible research”. He will guide attendees through how the Jupyter notebook can help them record, share, and publish their work. Additionally, he will cover how IPython allows for interactive exploration which in turn enables more productive and more reproducible workflows. To read more about this event, visit the conference website http://sbw2016.se/
Create your own PyData meetup: Looking to meet folks in the data science community? Join the PyData Meetup chapter in your area, or find out how to start one. http://www.meetup.com/pro/pydata/ PyData brings together both users and developers of data analysis tools to share ideas and learn from each other. The PyData community gathers to discuss how best to apply Python tools, as well as tools using R and Julia, to meet evolving challenges in data management, processing, analytics, and visualization.
PyData is organized by, and all proceeds benefit, NumFOCUS, a 501(C)(3) public charity. www.numfocus.org
