Project Jupyter

Project Jupyter is a community of open-source developers, scientists, educators, and data scientists. Its goal is to build open-source tools and create community that facilitates scientific research, reproducible and open workflows, education, computational narratives, and data analytics. Jupyter supports over 100 programming languages, and connects data analytics tools across a range of disciplines and communities.

There are several core projects of Jupyter that the Berkeley Institute for Data Science supports:

Provides remote access to Jupyter servers on shared infrastructure, with the goal of making high-powered computational environments and resources more accessible to students, researchers, and collaborators. JupyterHub runs in the cloud or on your own hardware, and makes it possible to serve a pre-configured data science environment to any user in the world. It is used in education and large-scale courses as well as in collaborative and massively-open data analytics projects.
Team members: Matthias Bussonnier, Jessica Forde, Chris Holdgraf, Yuvi Panda

Jupyter’s next-generation interface. JupyterLab empowers data scientists to compose the interface that suits their needs. It is a flexible and extensible user interface meant to support the diversity of workflows in data science. JupyterLab runs via the same Jupyter server as the traditional Notebook interface, which allows it to be accessed remotely on shared infrastructure (for example, via a JupyterHub).
Team members: Jessica Forde, Ian Rose

Jupyter Notebooks
A web-based interactive computing platform and open document format that allows users to author computational narratives that combine live code, equations, narrative text, interactive user interfaces, and other rich media. The Jupyter Notebook enables the collaborative creation of reproducible computational narratives that can be used across a wide range of audiences and contexts, and can be used in any data science workflow.
Team members: Matthias Bussonnier, Ian Rose

Allows users to create sharable, interactive, reproducible coding environments from materials that they put in online repositories like GitHub. Binder’s goal is to lower the barrier to sharing your scientific work, distributing educational materials, and communicating your work in an interactive fashion. It’s both free and open-source technology that others can deploy in the cloud, as well as a public service that hosts nearly 7,000 daily sessions at
Team members: Matthias Bussonnier, Jessica Forde, Chris Holdgraf, Yuvi Panda

BIDS Affiliates


Fernando Perez

Co-I for Moore/Sloan Data Science Environments

Chris Holdgraf

Data Science Education Program

Yuvi Panda

Data Science Education Program
DevOps Architect

Matthias Bussonnier

Project Jupyter
Postdoctoral Scholar

Ian Rose

Project Jupyter
Postdoctoral Scholar