Do usage counts of scientific data make sense? An investigation of the Dryad repository
| Pages | 332-342 |
| DOI | https://doi.org/10.1108/LHT-12-2016-0158 |
| Published date | 19 June 2017 |
| Date | 19 June 2017 |
| Author | Lin He,Zhengbiao Han |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information user studies,Metadata,Information & knowledge management,Information & communications technology,Internet |
Do usage counts of scientific data
make sense? An investigation
of the Dryad repository
Lin He and Zhengbiao Han
Nanjing Agricultural University, Nanjing, China
Abstract
Purpose –The purpose of this paper is to evaluate the impact of scientific data in order to assess the
reliability of data to support data curation, to establish trust between researchers to support reuse of digital
data and encourage researchers to share more data.
Design/methodology/approach –The authors compared the correlations between usage counts of
associated data in Dryad and citation counts of articles in Web of Science in different subject areas inorder to
assess the possibility of using altmetric indicators to evaluate scientific data.
Findings –There are high positive correlations between usage counts of data and citation counts of
associated articles. The citation counts of article’s shared data are higher than the average citation counts in
most of the subject areas examined by the authors.
Practical implications –The papersuggests that usage counts ofdata could be potentially usedto evaluate
scholarly impactof scientific data, especially for thosesubject areas without special data repositories.
Originality/value –The study examines the possibility to use usage counts to evaluate the impact of
scientific data in a generic repository Dryad by different subject categories.
Keywords Bibliometrics, Data sharing, Citation counts, Dryad repository, Scientific data, Usage counts
Paper type Research paper
Introduction
Researchers are required to share scientific data produced in their research to public,
as the requirements of funding agencies and journal publishers (The National Science
Foundation, 2011). Data repositories are developing in rapid speeds and lots of
repositories have been in use in various domains, which play a significant role with regard
to data preservation, data sharing and data reuse (Hey et al., 2009; Pham-Kanter
et al., 2014; Borgman, 2012). However, the curators and researchers are facing some
problems in data preservation, sharing and reuse. They always struggle with making
judgments for which data sources are of enough value to be collected from tremendous
amounts of digital data (Uhlir, 2010). More generally, most of researchers encounter
problems when they reuse or re-analyze data due to lack of evidence of the credibility of
scientific data (Pham-Kanter et al., 2014). Despite data sharing increasingly widespread, it
is not clear how to evaluate whether scientific data are effective and valuable. On the other
hand, data sharing is often seen as an additional time-consuming effort, some researchers
are not willing to share their primary data. Researchers need to provide academic impact
benefits to encourage them to share more data in practice (Tenopir et al., 2011). It is
important to find a way to assess the perceived quality of the shared data and the
visibility of those data from academic user communities through data repositories.
Over the past few decades, widely accepted impact evaluation indicators have been built
for academic publications which fundamentally depend on citation counts (Garfield, 1979;
Norris and Oppenheim, 2010). However, it would not be wise to evaluate the academic
impact of scientific data based on their citation counts in the articles (Ingwersen and
Chavan, 2011; Ingwersen, 2014) because they are rarely cited or cited instead of the
associated article (Fear, 2013; Piwowar et al., 2007). Moreover, general and consistent
standardized data citation criteria have not been in use (CODATA-ICSTI Task Group, 2013;
Spengler, 2012; Ball and Duke, 2015; California Digital Library, 2015; Moritz et al., 2011) and
Library Hi Tech
Vol. 35 No. 2, 2017
pp. 332-342
© Emerald PublishingLimited
0737-8831
DOI 10.1108/LHT-12-2016-0158
Received 28 December 2016
Revised 18 April 2017
Accepted 18 April 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0737-8831.htm
332
LHT
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