See all webinars
WebinarWatch on demand

Reproducible science with Jupyter: Changing our publication models

Speaker

Fernando Pérez

Fernando Pérez

Lawrence Berkeley National Laboratory

Fernando Pérez (@fperez_org) is a staff scientist at Lawrence Berkeley National Laboratory and and a founding investigator of the Berkeley Institute for Data Science at UC Berkeley, created in 2013. He received a PhD in particle physics from the University of Colorado at Boulder, followed by postdoctoral research in applied mathematics, developing numerical algorithms. Today, his research focuses on creating tools for modern computational research and data science across domain disciplines, with an emphasis on high-level languages, interactive and literate computing, and reproducible research. He created IPython while a graduate student in 2001 and continues to lead its evolution into Project Jupyter, now as a collaborative effort with a talented team that does all the hard work. He regularly lectures about scientific computing and data science, and is a member of the Python Software Foundation, a founding member of the NumFOCUS Foundation, and a National Academy of Science Kavli Frontiers of Science Fellow. He is the recipient of the 2012 Award for the Advancement of Free Software from the Free Software Foundation.
In this talk, I will discuss what are the basic ideas that underpin Jupyter, and how they can be used to tackle the problem of reproducibility in computational research. In particular, I will discuss how the structures provided by Jupyter can help us to simultaneously improve access to scientific knowledge and a more productive relationship with the literature, by modifying our approach to scholarly publishing of code, data and narratives. Read more

Project Jupyter, evolved from the IPython environment, provides a platform for interactive computing that is widely used today in research, education, journalism and industry. The core premise of the Jupyter architecture is to design tools around the experience of interactive computing, building an environment, protocol, file format and libraries optimized for the computational process when there is a human in the loop, in a live iteration with ideas and data assisted by the computer.

In this talk, I will discuss what are the basic ideas that underpin Jupyter, and how they can be used to tackle the problem of reproducibility in computational research. In particular, I will discuss how the structures provided by Jupyter can help us to simultaneously improve access to scientific knowledge and a more productive relationship with the literature, by modifying our approach to scholarly publishing of code, data and narratives.

Watch previously recorded video
View Slides