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KGTorrent is a dataset of Python Jupyter notebooks from the Kaggle platform. The dataset is accompanied by a MySQL database containing metadata about the notebooks and the activity of Kaggle users on the platform. The information to build the MySQL database has been derived from Meta Kaggle, a publicly available dataset containing Kaggle metadata. In this package, we share the complete KGTorrent dataset (consisting of the dataset itself plus its companion database), as well as the specific version of Meta Kaggle used to build the database. More specifically, the package comprises the following three compressed archives: KGT_dataset.tar.bz2, the dataset of Jupyter notebooks; KGTorrent_dump_10-2020.sql.tar.bz2, the dump of the MySQL companion database; MetaKaggle27Oct2020.tar.bz2, a copy of the Meta Kaggle version used to build the database. Moreover, we include KGTorrent_logical_schema.pdf, the logical schema of the KGTorrent MySQL database.
Kaggle, computational notebook, Jupyter, open dataset
Kaggle, computational notebook, Jupyter, open dataset
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 325 |

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