Plots for visualizing paper impact and journal impact of single researchers in a single graph
Plots for visualizing paper impact and journal impact of single researchers in a single graph
Lutz Bornmann 0 1
Robin Haunschild 0 1
Robin Haunschild 0 1
0 Max Planck Institute for Solid State Research , Heisenbergstr. 1, 70569 Stuttgart , Germany
1 Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society , Hofgartenstr. 8, 80539 Munich , Germany
In research evaluation of single researchers, the assessment of paper and journal impact is of interest. High journal impact reflects the ability of researchers to convince strict reviewers, and high paper impact reflects the usefulness of papers for future research. In many bibliometric studies, metrics for journal and paper impact are separately presented. In this paper, we introduce two graph types, which combine both metrics in a single graph. The graphs can be used in research evaluation to visualize the performance of single researchers comprehensively.
Bibliometrics against mean plot
depends on their reputation (besides other factors), the ability to publish papers in
highimpact journals reflects one aspect of high performance. Several years subsequent to the
publication of a manuscript in a journal (at least 3 years), the citation impact of the
published manuscript can be determined. Citations are seen as a proxy of quality, which
reflects at least impact (but not, e.g., accuracy). Thus, high citations for the researcher’s
papers indicate his or her ability to publish influential research. This is a second success,
which can be reached by researchers.
In research evaluation of single researchers, the assessment of both events is interesting.
High journal impact reflects the ability to convince strict reviewers and editors, and high
paper impact reflects the usefulness of papers for future research
(Bornmann and Marx
. In many bibliometric studies, metrics for journal and paper impact are separately
presented. In this paper, we introduce two graph types, which combine both metrics in a
single graph. These plots analyze the magnitude of differences between two metrics (here:
journal and paper impact). The proposed graph types are able to show the share of a
researcher’s papers with higher paper impact than can be expected from the journal impact.
The proposals in this paper follow earlier proposals of visualizing bibliometric data of
single researchers with beamplots
(Bornmann and Marx 2014a, b)
. Beamplots show the
performance of single researchers in view of publication output and citation impact.
Percentiles have been introduced in bibliometrics to overcome a certain problem with
frequently used citation indicators: the indicators are based on the arithmetic average of citation
counts. However, since, as a rule, the distribution of citations across publications is skewed, the
arithmetic average should not be used with raw citation data. The citation percentile of a single
paper is an impact value below which a certain share of papers fall
(Bornmann et al. 2013)
Given a set with several papers and their citation counts, the citation percentile of 95 for a focal
paper means, for example, that 95% of the papers in a set have citations counts lower than the
focal paper. The formula by
((i - 0.5)/n * 100) is frequently used for the
calculation of percentiles, whereby n is the number of papers in the set and i is the rank position
of the papers in the set (concerning their citations). In bibliometrics, the set of papers is defined
field- and time-specific, e.g., by using Web of Science (WoS, provided by Clarivate Analytics,
formerly the IP and Science business of Thomson Reuters) subject categories and publication
years. The corresponding citation percentile for each paper is a field- and time-normalized
impact value, which can be used for cross-field comparisons.
In this study, we used citation percentiles, which are based on Hazen’s formula, for the
papers of two single anonymous researchers as examples to demonstrate the plots.
Researcher 1 has published 99 papers (articles and reviews) between 2004 and 2013;
researcher 2 has published 427 papers between 1997 and 2013. Citations were counted
until the end of 2016 for calculation of Hazen percentiles. For field classification, we used
the WoS subject categories. In the case of multiple assignments of papers to subject
categories, the average value of the percentile in each subject category was used to obtain a
paper-based percentile value
(Haunschild and Bornmann 2016)
. We retrieved Hazen
percentiles for the papers from our in-house database derived from the Science Citation
Index Expanded (SCI-E), Social Sciences Citation Index (SSCI), and Arts and Humanities
Citation Index (AHCI) provided by Clarivate Analytics. Percentiles on the paper basis are
not only available in our in-house database (or similar databases at other bibliometric
institutes), but also in advanced bibliometric tools, such as InCites (Clarivate Analytics)
and SciVal (Elsevier).
Table 1 shows some metrics for both researchers. These metrics have been
recommended by Bornmann and Marx (2014b) for the evaluation of single researchers.
Highlycited papers are those papers, which belong to the 10% most-frequently cited papers in the
corresponding subject categories and publication years. For the calculation of the
agenormalized number of highly-cited papers, the number of highly-cited papers is divided by
the number of years since publishing the first paper. This metric has been favored by
Bornmann and Marx (2014b) against the use of the h index
, because it is a
field- and age-normalized index. A further advantage of the age-normalized number of
highly-cited papers is that it does not work with the arbitrary threshold h to identify the
papers with the most impact. It uses for each researcher the same threshold: being among
the 10% most-frequently cited.
Pudovkin and Garfield (2004)
introduced a similar metric as citation percentiles
on the level of journals. Also, this metric leads to field-normalized values—on the level of
journals. The so-called rank normalized impact factor (rnIF) equals ((k - rj ? 1)/k * 100)
where rj is the descending rank of journal j in its subject category and k is the number of
journals in the category. In contrast to the usual Journal Impact Factor
rnIF can be used for cross-field comparisons of journals. Our in-house database also provides
the journal percentiles (rnIF) from the Journal Citation Reports (Clarivate Analytics). The
corresponding journal percentiles for the papers under study were retrieved.
In the following, we call citation percentiles on the level of papers as paper percentiles
and citation percentiles on the level of journals (rnIF) as journal percentiles. By using both
percentiles for a publication set of a single researcher, we have similar field-normalized
metrics available, which can be used for the comparison of two events: the success of
publishing in journals with a high reputation and the success of receiving high citation
counts. If average percentiles are reported in the following, these averages are medians.
We provide a Stata command (babibplot.ado) and an R package (BibPlots), which
produce the graphs proposed in this study. Both can be found in Statistical Software
Components (SSC) Archive (in the case of Stata) and Comprehensive R Archive
Network (CRAN, in the case of R), respectively.
Bornmann and Marx (2014a, b) introduced beamplots, which can be used to visualize the
productivity and citation impact of single researchers. It is an advantage of the plots that
they contain distributional information (the spread of papers across citation percentiles and
publication years) and index information (the mean impact of the papers over all
publication years and the mean impact within single publication years). Bornmann and
Marx (2014a, b) propose to use beamplots with paper percentiles, but they can also be used
with journal percentiles.
Figure 1 presents beamplots of paper (left) and journal (right) percentiles for both
researchers. The individual percentiles (paper or journal percentiles) are shown using grey
rhombi; the median over a publication year is displayed with red triangles. Furthermore, a red
dashed line visualizes the median of the (paper or journal) percentiles for all the years and a grey
line marks the value 50 (the median of the world’s impact). Whereas the metrics in Table 1
only condense the outcome of a career in only a few numbers, beamplots provide output and
input information for every year. Thus, it can be inspected, in which periods the researcher was
very productive and successful or not. Figure 1 demonstrates, for example, that researcher 1 has
considerable variations across the career, but this is scarcely visible for researcher 2.
Although the annual ability to publish high impact papers and papers in high impact
journals can be seen in the beamplots of both researchers, the connection between the two is
lost for the years where multiple papers with different paper impact were published. Thus, we
propose the possibility to keep the connection between paper and journal percentiles for each
data point in a scatter plot, as shown in Fig. 2. The horizontal and vertical red lines in the
figure indicate the world averages; the red dashed lines show the average values of paper and
journal percentiles in the data set. The diagonal red line is the bisecting line. Points below the
bisecting line indicate that the corresponding paper has a higher paper impact than journal
impact and vice versa. Each row (nr) and column (nc) as well as quadrant (nq) of the scatter plot
is labeled with the number and percentage of data points in the corresponding section, e.g.,
nr1 = 90; 91% for 90 papers in row 1 of researcher 1. The values of nc1 correspond to the
number and proportion of papers belonging to the 50% most frequently cited papers in the
corresponding subject categories and publication years. The red squares show the average
value (median) of all data points in each quadrant. The results in Fig. 2 demonstrate that both
researchers were able to publish most of the papers in better than average journals with better
than average impact later on. This is especially the case for researcher 2.
Figure 3 emphasizes the relation between paper impact and journal impact even more.
The figures for the researchers are difference against mean plots
(Altman and Bland 1983;
; they are scatter plots of the following two quantities: (1) difference
between paper and journal impact and (2) average of paper and journal impact. The
bisecting line is no longer necessary to judge whether the paper percentile is higher than
the journal percentile or vice versa. Additionally, the plots in Fig. 3 feature two dashed red
lines, which answer the following questions: (1) is there a general tendency of the
researcher to publish in journals with higher impact or to publish papers with higher impact
(see the horizontal, dashed line)? (2) Is the researcher generally able to publish papers in
good journals, which receive high impact later on (see the vertical, dashed line)? By
combining both quantities as two new indexes in the same plot, we provide additional
information compared to journal and paper percentiles. Furthermore, the individual paper
and journal impact values remain easily accessible: (1) paper impact = half of the
difference ? average and (2) journal impact = average - half of the difference.
Often, one is interested in identifying the papers which belong to the 10% most
frequently cited papers in the corresponding subject categories and publication years
(Bornmann et al. 2012)
. This is visible in the scatter plots in Fig. 2. The data points with a
paper impact of 90 or higher are among the top 10% of their subject category and in their
publication year. However, this information is lost in the difference against mean plots of
Fig. 3. In order to restore this information in the plots, the black circles with a paper impact
of 90 or higher (papers among the top 10%) are unfilled while the other circles are filled.
A benefit of the difference against mean plot lies in the analysis of the relationship
between the differences of journal and paper impact and their average. Papers with extreme
differences between journal and paper impact are clearly identifiable. The difference
against mean plots in Fig. 3 combines the need to see the distribution of the impact of the
individual papers and the journals they are published in with aggregated statistical values
over the quadrants, rows, and columns of the plot. Furthermore, the average of the
differences between paper and journal impact (see the horizontal dashed line in the plots of
Fig. 3) shows whether there is a general tendency of the researchers to publish in better
journals or papers with higher impact. The vertical dashed lines in the plots of Fig. 3 show
the overall average impact (paper and journal impact).
Researchers and decision makers in science policy are usually interested in receiving single
numbers on the performance of single researchers
(Leydesdorff et al. 2016)
. This explains
the popularity of the h index proposed by
and the many variants of this index
introduced in recent years
(Bornmann et al. 2011)
. However, since the reduction of the
performance into a single number leads to the loss of information, it is recommended in
bibliometrics to use distributions instead of only single numbers. For example,
et al. (2016)
propose to use a method for generating the citation distribution of journals,
instead of the use of the Journal Impact Factor (JIF) in research evaluation. Basically, the
JIF measures the mean citation impact of papers published in a journal. Similarly, the
performance of single scientists should be also measured by focusing on distributions than
only on single numbers. In this study, we have demonstrated the usefulness of scatter plots
and difference against mean plots to combine paper and journal percentiles in a single
graph. The availability of journal percentiles in the Journal Citation Reports (JCR,
Clarivate Analytics) makes it possible to contrast paper impact with journal impact—time- and
field-normalized by using percentiles.
Difference against mean plots are a standard instrument in the assessment of
equivalence between two metrics (usually two clinical measurement methods). We think that the
plots also allow interesting insights into the publication and citation profiles of single
scientists and can be used for research evaluation purposes. The plots can be applied to
inspect the amount of papers in a set, which—more or less—agree in a high or low paper
and journal impact. This is the average dimension of the plot. The other dimension
focusses on the differences between paper and journal impact. Are there many papers with
large differences between both metrics? Is the researcher more able to publish in
highimpact journals or papers with high impact? If we use journal percentiles as an expected
value, which can be contrasted with paper percentiles, a higher paper percentile
demonstrates that the paper received more impact than could be expected on the basis of the
In the scatter and difference against mean plots, which we present in this study, paper
and journal impact are categorized into four quadrants. A high general impact of the papers
in the set is indicated by high numbers and proportions of data points in quadrants 1 and 4
(i.e., high values of nq1 and nq4). High numbers and proportions of data points in quadrants
2 and 3 (high values of nq2 and nq3) indicate a low impact in general. The differences
between nq1 and nq4 as well as nq2 and nq3 indicate if the papers show a higher journal or
higher paper impact. With the categorization of journal and citation impact into four
quadrants, the plots follow approaches in bibliometrics, such as the Characteristics Scores
and Scales (CSS) method
(Gla¨nzel et al. 2016)
, which can also be used to assign citation
impact to (four) impact groups.
By exploring paper impact and journal impact using combined plots, the user should be
aware that both perspectives on researchers’ performance are frequently related. Many
studies have investigated the correlation between journal metrics and citation impact on the
single paper level. Overviews of these studies can be found in
Bornmann and Daniel
Onodera and Yoshikane (2014)
Tahamtan et al. (2016)
. Most of these studies
have revealed that paper and journal impact are positively correlated: one can expect more
citations if the manuscript has been published in a high-impact journal and fewer citations
if it is a low impact journal. This means for the use of the plots, which have been
introduced and explained in this paper, that similar results can be expected on the paper as
well as the journal side. Thus, the most interesting parts of the plots are those, which
visualize information about papers with disagreeing journal and paper impact.
Acknowledgements Open access funding provided by Max Planck Society. The bibliometric data used in
this paper are from an in-house database developed and maintained by the Max Planck Digital Library
(MPDL, Munich) and derived from the Science Citation Index Expanded (SCI-E), Social Sciences Citation
Index (SSCI), Arts and Humanities Citation Index (AHCI) provided by Clarivate Analytics, formerly the IP
and Science business of Thomson Reuters (Philadelphia, Pennsylvania, USA).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
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