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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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Dataset Reuse Indicators Datasets

Authors: Koesten, Laura; Vougiouklis, Pavlos; Groth, Paul; Simperl, Elena;

Dataset Reuse Indicators Datasets

Abstract

This dataset contains two files. 1) A python pickle file (github_dataset.zip) that contains Github repositories with datasets. Specifically, using Google’s public dataset copy of Github and the BigQuery service to build a list of repositories that have a CSV or XLSX or XLS file. We then used the GitHub API to collect nformation about each repository in this list. The resulting dataset consists of 87936 repositories that contain at least a CSV, XLSX or XLS file, alongside with information about their features (e.g. number of open and closed issues and license) from GitHub. This corpus had more than two million data files. We then excluded those files withless then ten rows, which was the case for 65537 repositories with a total of 1,467,240 data files. 2) A python pickle file (processed_dataset.zip) containing the feature information necessary to train a machine learning model to predict reuse on these Github datasets Source code can be found at: https://github.com/laurakoesten/Dataset-Reuse-Indicators For a full description of the content see: Koesten, Laura and Vougiouklis, Pavlos and Simperl, Elena and Groth, Paul, Dataset Reuse: Translating Principles to Practice. Available at SSRN: https://ssrn.com/abstract=3589836 or http://dx.doi.org/10.2139/ssrn.3589836

Country
Netherlands
Related Organizations
Keywords

GitHub, data files, dataset reuse

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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).
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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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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