The citation advantage of linking publications to research data

被引:152
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
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页数:18
相关论文
共 73 条
  • [51] Generalized additive models for location, scale and shape
    Rigby, RA
    Stasinopoulos, DM
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2005, 54 : 507 - 544
  • [52] Has open data arrived at the British Medical Journal (BMJ)? An observational study
    Rowhani-Farid, Anisa
    Barnett, Adrian G.
    [J]. BMJ OPEN, 2016, 6 (10):
  • [53] Open Data in Global Environmental Research: The Belmont Forum's Open Data Survey
    Schmidt, Birgit
    Gemeinholzer, Birgit
    Treloar, Andrew
    [J]. PLOS ONE, 2016, 11 (01):
  • [54] Sears JR, 2011, AGU FALL M
  • [55] The chaperone effect in scientific publishing
    Sekara, Vedran
    Deville, Pierre
    Ahnert, Sebastian E.
    Barabasi, Albert-Laszlo
    Sinatra, Roberta
    Lehmann, Sune
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2018, 115 (50) : 12603 - 12607
  • [56] Author name disambiguation: What difference does it make in author-based citation analysis?
    Strotmann, Andreas
    Zhao, Dangzhi
    [J]. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 2012, 63 (09): : 1820 - 1833
  • [57] Struck B, 2018, STI 2018 C P CTR SCI, P436
  • [58] The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression
    Thelwall, Mike
    [J]. JOURNAL OF INFORMETRICS, 2016, 10 (02) : 336 - 346
  • [59] Regression for citation data: An evaluation of different methods
    Thelwall, Mike
    Wilson, Paul
    [J]. JOURNAL OF INFORMETRICS, 2014, 8 (04) : 963 - 971
  • [60] Torgo L., 2010, Data Mining with R: Learning with Case Studies