Schedule: Reproducible research and open science sessions

Thorin Tabor offers an overview of the GenePattern Notebook, which allows Jupyter to communicate with the open source GenePattern environment for integrative genomics analysis. It wraps hundreds of software tools for analyzing omics data types, as well as general machine learning methods, and makes them available through a user-friendly interface. Read more.

Scientific research thrives on collaborations between computational and experimental groups, who work together to solve problems using their separate expertise. Zach Sailer highlights how tools like the Jupyter Notebook, JupyterHub, and ipywidgets can be used to make these collaborations smoother and more effective. Read more.

Traditionally, statistical training has focused on statistical methods and tests, without addressing the process of developing a technical artifact, such as a report. Hilary Parker argues that it's critical to teach students how to go about developing an analysis so they avoid common pitfalls and explains why we must adopt a blameless postmortem culture to address these pitfalls as they occur. Read more.

Kaggle Kernels, an in-browser code execution environment that includes a version of Jupyter Notebooks, has allowed Kaggle to flourish in new ways. Drawing on a diverse repository of user-created notebooks paired with competitions and public datasets, Megan Risdal and Wendy Chih-wen Kan explain how Kernels has impacted machine learning trends, collaborative data science, and learning. Read more.

Although researchers have traditionally cited code and data related to their publications, they are increasingly using the Jupyter Notebook to share the processes involved in the act of scientific inquiry. Bernie Randles and Hope Chen explore various aspects of citing Jupyter notebooks in publications, discussing benefits, pitfalls, and best practices for creating the "paper of the future." Read more.

Web-based textbooks and interactive simulations built in Jupyter notebooks provide an entry point for course participants to reproduce content they are shown and dive into the code used to build them. Lindsey Heagy and Rowan Cockett share strategies and tools for developing an educational stack that emerged from the deployment of a course on geophysics and some lessons learned along the way. Read more.

Reports of a lack of reproducibility have led funders and others to require open data and code as the outputs of research they fund. Mark Hahnel and Marius Tulbure discuss the opportunities for Jupyter notebooks to be the final output of academic research, arguing that Jupyter could help disrupt the inefficiencies in cost and scale of open access academic publishing. Read more.

While Jupyter notebooks are a boon for computational science, they are also a powerful tool in the digital humanities. Matt Burton offers an overview of the digital humanities community, discusses defactoring—a novel use of Jupyter notebooks to analyze computational research—and reflects upon Jupyter’s relationship to scholarly publishing and the production of knowledge. Read more.

Jupyter notebooks are a popular option for sharing data science workflows. Daniel Mietchen shares best practices for reproducibility and other aspects of usability (documentation, ease of reuse, etc.) gleaned from analyzing Jupyter notebooks referenced in PubMed Central, an ongoing project that started at a hackathon earlier this year and is being documented on GitHub. Read more.