Open inquiry through reproducing results is fundamental to the scientific process. Contemporary research relies on software engineering pipelines to collect, process, and analyze data. The open source projects within Project Jupyter facilitate these objectives by bringing software engineering within the context of scientific communication. I will highlight specific projects that are computational building blocks for scientific communication, starting with the Jupyter Notebook. I will also explore applications of projects that build off of the Notebook such as Binder, JupyterHub, and repo2docker. I will discuss how these projects can individually and jointly improve reproducibility in scientific communication. Finally, I will demonstrate applications of Jupyter software that allow researchers to build upon the code of other scientists, both to extend their work and the work of others.
