Jupyter in education, Jupyter-in-the-loop, and reproducibility in science.
Lorena Barba is an associate professor of mechanical and aerospace engineering at George Washington University. She will be speaking at JupyterCon, August 22-25, 2017, in New York City.
Below, Barba shares her thoughts on the current and future state of Jupyter.
1. How has Jupyter changed the way you work?
My research group is pretty eclectic, with a focus on computational fluid dynamics and computational physics, but with a wide range of activities in education and open source software. My doctoral students and I use Jupyter daily. When any of us is studying a new topic—like, say, a new method for solving the equations of fluid dynamics—the exploration develops and gets recorded in a notebook. When a student is producing some preliminary results from simulations, they are organized for internal discussion in a notebook. Not to mention our educational materials: my students and I co-author many notebooks for teaching and learning. Some become an enhanced form of (free) textbooks for a course, others may support a one-off tutorial, and yet others encapsulate our own learning process with new topics.
2. How does Jupyter change the way your team works? How does it alter the dynamics of collaboration?
Combined with public hosting on the web and version control (on GitHub, for example), we use Jupyter to loop through our conversations about research. Through an iterative process, we often co-write asynchronously and develop the unpolished ideas that later turn into a research paper. We use “Jupyter-in-the-loop” to help us think and organize our work!
3. How do you expect Jupyter to be extended in the coming year?
Like everyone, I am eagerly awaiting the arrival of JupyterLab. It will change everything! Adoption in data science will be solid. Users will jump with joy at being able to read in large data files and manipulate them without slow-down. In the educational realm, I’m looking forward to the ecosystem—with tools like nbgrader and nbtutor—becoming easier to use and better integrated. We have brilliant people developing these tools, and I just hope their future employers will continue to support their open source contributions.
4. What will you be talking about at JupyterCon?
My talk is titled Design for Reproducibility. In recent years, the debates about reproducibility in science have really heated up. Often, the concerns include open sharing of code and data that helped arrive at a new finding. This is still controversial in some fields, believe it or not. Jupyter is promoted as a solution for creating reproducible computational narratives. Some see it as a means of putting into practice Knuth’s idea of literate programming, where code is directly annotated with comprehensible documentation. But here is one seeming contradiction: interactive tools were seen by the pioneers of reproducible research as the antithesis of reproducibility. What makes Jupyter different than, say, the dreaded spreadsheet? My talk will explore how we build into the design of our tools (like Jupyter) an enabling capacity to support reproducible research.
I also want to tell you about a Birds-of-a-Feather informal session that I’m co-leading with Robert Talbert: “Jupyter for Teaching & Learning,” on Thursday, August 25, 2017, at 7 p.m. We want to connect educators using Jupyter to share know-how and put our heads together to articulate the needs of teachers and learners to the Jupyter team. Anyone can join us! (Registration is at bit.ly/jupyter-ed-bof.)
5. What sessions are you looking forward to seeing at JupyterCon?
The conference turned out to have big focus areas in both education and reproducible research, which are of great interest to me. I’m really looking forward to hearing about the large deployments of JupyterHub in educational initiatives with hundreds of students. I’m also eager to learn more about how Jupyter is being used in contemporary AI work.