Dataset of A Large-scale Study about Quality and Reproducibility of Jupyter Notebooks / Understanding and Improving the Quality and Reproducibility of Jupyter Notebooks (Q6695196)

The self-documenting aspects and the ability to reproduce results have been touted as significant benefits of Jupyter Notebooks. At the same time, there has been growing criticism that the way notebooks are being used leads to unexpected behavior, encourages poor coding practices and that their results can be hard to reproduce. To understand good and bad practices used in the development of real notebooks, we analyzed 1.4 million notebooks from GitHub. Based on the results, we proposed and evaluated Julynter, a linting tool for Jupyter Notebooks. Papers: PIMENTEL, J. F.; MURTA, L.; BRAGANHOLO, V.; FREIRE, J.; A large-scale study about quality and reproducibility of jupyter notebooks. In: International Conference on Mining Software Repositories (MSR), 2019, Montreal, Canada. PIMENTEL, J. F.; MURTA, L.; BRAGANHOLO, V.; FREIRE, J.; Understanding and Improving the Quality and Reproducibility of Jupyter Notebooks. Empirical Software Engineering, 2021 (in press) This repository contains three files: db2020-09-22.dump.gz sample.tar.gz julynter_reproducility.tar.gz Reproducing the Notebook Study Thedb2020-09-22.dump.gzfile contains a PostgreSQL dump of the database, with all the data we extracted from notebooks. For loading it, run: gunzip -c db2020-09-22.dump.gz | psql jupyter Note that this file contains only the database with the extracted data. The actual repositories are available in a google drive folder, which also contains the docker images we used in the reproducibility study. The repositories are stored ascontent/{hash_dir1}/{hash_dir2}.tar.bz2, wherehash_dir1andhash_dir2are columns ofrepositoriesin the database. For scripts, notebooks, and detailed instructionson how to analyze orreproduce the data collection, please check the instructions on the Jupyter Archaeology repository(tag 1.0.0) Thesample.tar.gzfile contains the repositories obtained during the manual sampling. Reproducing the Julynter Experiment Thejulynter_reproducility.tar.gzfile contains all the data collected in the Julynter experiment and the analysis notebooks. Reproducing the analysis isstraightforward: Uncompress the file: $ tar zxvf julynter_reproducibility.tar.gz Install the dependencies: $ pip install julynter/requirements.txt Run the notebooks in order: J1.Data.Collection.ipynb; J2.Recommendations.ipynb; J3.Usability.ipynb. The collected data is stored inthe julynter/data folder. Changelog 2019/01/14 - Version 1 - Initial version 2019/01/22 - Version 2 - UpdateN8.Execution.ipynb to calculate the rate of failure for each reason 2019/03/13 - Version 3 - Update package for camera ready. Add columns to db to detect duplicates, change notebooks to consider them,and add N1.Skip.Notebook.ipynb and N11.Repository.With.Notebook.Restriction.ipynb. 2021/03/15 - Version 4 - Add Julynter experiment; Update database dump to include new data collected for the second paper;remove scripts and analysisnotebooks from thispackage (moved to GitHub), add a link to Google Drive with collected repository files