The following article was published earlier today by PLOS One.
Title
Citation Advantage of Linking Publications to Research Data
Authors
Giovanni Colavizza
The Alan Turing Institute
University of Amsterdam
Iain Hrynaszkiewicz
Springer Nature
Public Library of Science
Isla Staden
The Alan Turing Institute
Queen Mary University
Kirstie Whitaker
The Alan Turing Institute
University of Cambridge
Barbara McGillivray
The Alan Turing Institute
University of Cambridge
Source
PLoS ONE 15(4): e0230416.
DOI: 10.1371/journal.pone.0230416
Abstract
Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing such links.
We consider 531, 889 journal articles published by PLOS and BMC, develop an automatic system for labelling their data availability statements according to four categories based on their content and the type of data availability they display, and finally analyze the citation advantage of different statement categories via regression.
We find that, following mandated publisher policies, data availability statements become very common. In 2018 93.7% of 21,793 PLOS articles and 88.2% of 31,956 BMC articles had data availability statements. Data availability statements containing a link to data in a repository—rather than being available on request or included as supporting information files—are a fraction of the total. In 2017 and 2018, 20.8% of PLOS publications and 12.2% of BMC publications provided DAS containing a link to data in a repository. We also find an association between articles that include statements that link to data in a repository and up to 25.36% (± 1.07%) higher citation impact on average, using a citation prediction model.
We discuss the potential implications of these results for authors (researchers) and journal publishers who make the effort of sharing their data in repositories. All our data and code are made available in order to reproduce and extend our results.
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