Restoring Execution Environments of Jupyter Notebooks

Wang, Jiawei; Li, Li; Zeller, Andreas

doi:10.48550/arxiv.2103.02959

Cited by 2 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies analyzed computational notebook archives to understand how data scientists use notebooks and how these tools fit in a typical data science workflow [3], [10]. In addition, as notebooks have been criticized for inducing poor programming practices [11], some studies have investigated their quality and reproducibility [4]- [6] and proposed tools to improve them [12]- [14]. The results of these studies confirm that notebooks are often inundated by poor-quality code, ignoring even basic software engineering principles.…”

Section: Potential Research Applicationsmentioning

confidence: 99%

KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle

Quaranta

Calefato

Lanubile

2021

2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)

View full text Add to dashboard Cite

Computational notebooks have become the tool of choice for many data scientists and practitioners for performing analyses and disseminating results. Despite their increasing popularity, the research community cannot yet count on a large, curated dataset of computational notebooks. In this paper, we fill this gap by introducing KGTORRENT, a dataset of Python Jupyter notebooks with rich metadata retrieved from Kaggle, a platform hosting data science competitions for learners and practitioners with any levels of expertise. We describe how we built KGTORRENT, and provide instructions on how to use it and refresh the collection to keep it up to date. Our vision is that the research community will use KGTORRENT to study how data scientists, especially practitioners, use Jupyter Notebook in the wild and identify potential shortcomings to inform the design of its future extensions.

show abstract