Paper Info
Title
Ten Simple Rules for Reproducible Research in Jupyter Notebooks.
Abstract
Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses.
Year
Venue
Field
2018
arXiv: Other Computer Science
Data science,Visualization,Computer science,Reuse,Theoretical computer science,Software,Scientific progress,Workflow,Scientific method
DocType
Volume
Citations 
Journal
abs/1810.08055
1
PageRank 
References 
Authors
0.35
2
11
Name
Order
Citations
PageRank
Adam Rule1354.37
Amanda Birmingham231.81
Cristal Zuniga310.69
Ilkay Altintas41191106.09
Shih-Cheng Huang510.35
Rob Knight636626.19
Niema Moshiri710.35
Mai H. Nguyen842.47
Sara Rosenthal921.04
Fernando Perez1029465.33
Peter W. Rose1110.35