2020/06/22 by Sheeba Samuel, Birgitta König‐Ries, Samuel, Sheeba +2 · 4 citations
Computer Science · Decision Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Research Data Management Practices #Scientific Computing and Data Management #Software Engineering Research #cs.CY
paper · pdf · doi:10.48550/arxiv.2006.12110
Accepted at ProvenanceWeek 2020 (https://iitdbgroup.github.io/ProvenanceWeek2020/)
arxiv created 2020/06/22 · openalex publication_date 2020/06/22 · arxiv updated 2020/06/23 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Computational notebooks have gained widespread adoption among researchers from academia and industry as they support reproducible science. These notebooks allow users to combine code, text, and visualizations for easy sharing of experiments and results. They are widely shared in GitHub, which currently has more than 100 million repositories making it the largest host of source code in the world. Recent reproducibility studies have indicated that there exist good and bad practices in writing these notebooks which can affect their overall reproducibility. We present ReproduceMeGit, a visualization tool for analyzing the reproducibility of Jupyter Notebooks. This will help repository users and owners to reproduce and directly analyze and assess the reproducibility of any GitHub repository containing Jupyter Notebooks. The tool provides information on the number of notebooks that were successfully reproducible, those that resulted in exceptions, those with different results from the original notebooks, etc. Each notebook in the repository along with the provenance information of its execution can also be exported in RDF with the integration of the ProvBook tool.