Data bibliometrics: metrics before norms
| Pages | 428-435 |
| DOI | https://doi.org/10.1108/OIR-01-2017-0008 |
| Date | 12 June 2017 |
| Published date | 12 June 2017 |
| Author | David Stuart |
| Subject Matter | Library & information science,Information behaviour & retrieval,Collection building & management,Bibliometrics,Databases,Information & knowledge management,Information & communications technology,Internet,Records management & preservation,Document management |
Data bibliometrics: metrics
before norms
David Stuart
Statistical Cybermetrics Research Group, University of Wolverhampton, UK
Abstract
Purpose –The purpose of this paper is to highlight the problem of establishing metrics for the impact of
research data when norms of behaviour have not yet become established.
Design/methodology/approach –The paper considers existing research into data citation and explores
the citation of data journals.
Findings –The paper finds that the diversity of data and its citation precludes the drawing of any simple
conclusions about how to measure the impact of data, and an over emphasis on metrics before norms of
behaviour have become established may adversely affect the data ecosystem.
Originality/value –The paper considers multiple different types of data citation, including for the first time
the citation of data journals.
Keywords Scientometrics, Citation analysis, Open data, Data journals
Paper type Research paper
Introduction
As the scholarly publishing ecosystem changes so quickly, the gaps in our knowledge seem
to grow faster than our understanding, and nowhere can this be more clearly seen than in
the publishing and citing of research data. In recent years, research data have come to be
seen as a public good and citations are seen as having the potential to both incentivize and
reward the publishing of data. However, with limited understanding of the norms of data
publishing and data citation, too early an emphasis on metrics may do damage to the
nascent data ecosystem.
This viewpoint considers the heterogeneous nature of research data, the limited extent of
current research into data publication and citation, and the risks of an over emphasis on
metrics before norms are established. Particular attention is paid to the potential of data
journals, one of the more recent and less studied areas of data publishing. Data journals are
promoted as providing a recognisable and citable publication, “a bridge between traditional
journal publication and data set submission”(Force et al., 2016, p. 27), but as an initial
analysis of data journals shows here: the huge diversity in the extent to which data journals
are cited precludes the drawing of any simple conclusions.
Background
Evaluative citation analysis is based on relatively simple assumptions about the publishing
and citation process: researchers publish research which builds upon the work of others, the
intellectual debt is paid in the form of citations, and if the citations are aggregated they can
provide useful indicators of the influence of a work. For these indicators to be meaningful,
however, it is required that units of analysis are sufficiently similar to be aggregated,
i.e., there are norms of citing behaviour, and sufficiently similar genres of document.
The problem of aggregating units that are not sufficiently similar may be seen in criticisms
of both journal-based metrics and citation analysis more generally: articles in the same
journal are not necessarily of similar quality, research is cited for different reasons, and
there are disciplinary differences in citation and publishing practices. Issues surrounding
dissimilarity of units of analysis may be expected to be writ large when it comes to citation
of research data.
Online Information Review
Vol. 41 No. 3, 2017
pp. 428-435
© Emerald PublishingLimited
1468-4527
DOI 10.1108/OIR-01-2017-0008
Received 9 January 2017
Revised 9 January 2017
Accepted 20 January 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1468-4527.htm
428
OIR
41,3
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