Do journal data sharing mandates work? Life sciences evidence from Dryad

Publication Date16 January 2017
Date16 January 2017
AuthorMike Thelwall,Kayvan Kousha
SubjectLibrary & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management
Do journal data sharing mandates
work? Life sciences evidence
from Dryad
Mike Thelwall
School of Mathematics and Computing,
University of Wolverhampton, Wolverhampton, UK, and
Kayvan Kousha
Statistical Cybermetrics Research Group,
University of Wolverhampton, Wolverhampton, UK
Purpose Data sharing is widely thought to help research quality and efficiency. Data sharing mandates are
increasingly being adopted by journals and the purpose of this paper is to assess whether they work.
Design/methodology/approach This study examines two evolutionary biology journals, Evolution and
Heredity, that have data sharing mandates and make extensive use of Dryad. It uses a quantitative analysis of
presence in Dryad, downloads and citations.
Findings Within both journals, data sharing seems to be complete, showing that the mandates work on a
technical level. Low correlations (0.15-0.18) between data downloads and article citation counts for articles
published in 2012 within these journals indicate a weak relationship between data sharing and research
impact. An average of 40-55 data downloads per article after a few years suggests that some use is found for
shared life sciences data.
Research limitations/implications The value of shared data uses is unclear.
Practical implications Data sharing mandates should be encouraged as an effective strategy.
Originality/value This is the first analysis of the effectiveness of data sharing mandates.
Keywords Data sharing, Research data management, Citation analysis, Digital repository,
Digital archive, Dryad
Paper type Research paper
Scientific data sharing is increasingly encouraged or mandated by funders and journals
(Mennes et al., 2013). Some organisations even fund projects to rescue data that had initially
not been shared (Hsu et al., 2015). The advantages of shared data to science are that the
results of any associated paper can be at least partially verified, reducing mistakes and
fraud (Sandve et al., 2013) and other researchers or the public can re-use the data for
additional studies or purposes (Borgman, 2012; Caetano and Aisenberg, 2014). Thus, science
can be more replicable and more efficient.
Most researchers seem to be willing to share data in principle (Tenopir et al., 2011, 2015) and
data sharing is common in forensic and evolutionary genetics (Anagnostou et al., 2013).
Nevertheless, over 60 per cent of scientists responding to one survey were unwilling to share
their primary data before the final publication of their articles (Huang et al., 2012). There are
multiple possible reasons for individuals being reluctant to share: researchers need extra time to
format data for sharing by adding provenance, describing variables in detail (Kroon-Batenburg
and Helliwell, 2014), and anonymising if necessary (e.g. Hrynaszkiewicz et al., 2010); their work
may be discredited if it is found to be mistaken; and others may publish research on their data
that they were intending to do at a later stage. This last point is particularly important in
research areas that produce data sets of lasting value that are difficult to collect. It is also
disproportionally affects researchers in developing nations that may not have the resources to
publish quickly (Kenall et al., 2014). Conversely, the advantages for those sharing the data
Aslib Journal of Information
Vol. 69 No. 1, 2017
pp. 36-45
© Emerald PublishingLimited
DOI 10.1108/AJIM-09-2016-0159
Received 28 September 2016
Revised 5 December 2016
Accepted 5 December 2016
The current issue and full text archive of this journal is available on Emerald Insight at:

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