  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.
