Reproducible science with Jupyter: Changing our publication models
Lawrence Berkeley National Laboratory
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.