Since the birth of the Jupyter Notebook several years ago, mentions of notebooks in research publications have increased. 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 as a complementary “recipe” to contextualize the data and code used in publications, discussing benefits, pitfalls, and best practices for creating the “paper of the future.”
Bernie and Hope share how researchers are currently mentioning notebooks and examine the traces created between the published paper, data, and code, which ordinarily live in different repositories. They then explain the FAIR (findable, accessible, interoperable, and reusable) principles of objects used in open science research and evaluate the Jupyter Notebook as a component of the FAIR ecosystem. Along the way, they outline past problems relating to linked code and data as well as new developments in the open source community that are addressing the issue of object permanence in a publicly accessible environment.
Bernie and Hope conclude by relating a case study of a paper submitted to an astronomy journal to showcase the various steps and decisions made in citing a Jupyter notebook. (One important aspect of the case study is that, because of collaborative work, some code can be made public, while other parts of the study must remain private, so decisions must be made to optimize these circumstances.) They then touch on improvements to how the Jupyter Notebook can be continued to be cited, its importance in the research process, and a potential primer for notebook citations that could be added to Jupyter.org.
Bernie Randles is a graduate student in the Information Studies program at UCLA. Her work is centered around knowledge creation in astronomy, specifically examining astronomers’ data and software pipeline practices. She also researches the use of open source software in scientific research organizations, primarily in data-rich and computationally intensive fields. Previously, Bernie worked in IT (wearing many hats, some red) at several colleges and universities. She holds degrees in math, computer science, and fine arts.
Born and raised in Taiwan, Hope Chen is a PhD candidate in astronomy and astrophysics at Harvard University. Since 2011, Hope has been a member of the Star Formation research group led by Alyssa A. Goodman of Harvard and has embarked on several research projects aimed at decoding the mysteries of structural formation in nearby cradles of stars, such as the Gould Belt molecular clouds. Hope is interested in making astronomy seamless and accessible and has been an ardent user of the Jupyter Notebook. Hope holds a degree from National Tsing Hua University with the highest distinction, the Dr. Mei Yi-Chih Prize.
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