The citation advantage of linking publications to research data

被引:150
|
作者
Colavizza, Giovanni [1 ,2 ]
Hrynaszkiewicz, Iain [3 ,4 ]
Staden, Isla [1 ,5 ]
Whitaker, Kirstie [1 ,6 ]
McGillivray, Barbara [1 ,6 ]
机构
[1] Alan Turing Inst, London, England
[2] Univ Amsterdam, Amsterdam, Netherlands
[3] Springer Nat, London, England
[4] Publ Lib Sci, Cambridge, England
[5] Queen Mary Univ, London, England
[6] Univ Cambridge, Cambridge, England
来源
PLOS ONE | 2020年 / 15卷 / 04期
基金
英国工程与自然科学研究理事会;
关键词
AUTHOR NAME DISAMBIGUATION; REGRESSION;
D O I
10.1371/journal.pone.0230416
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:18
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