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What Can Article-Level Metrics Do for You?

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Article-level metrics (ALMs) provide a wide range of metrics about the uptake of an individual journal article by the scientific community after publication. They include citations, usage statistics, discussions in online comments and social media, social bookmarking, and recommendations. In this essay, we describe why article-level metrics are an important extension of traditional citation-based journal metrics and provide a number of example from ALM data collected for PLOS Biology.
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Essay
What Can Article-Level Metrics Do for You?
Martin Fenner*
Article-Level Metrics Project, Public Library of Science, San Francisco, California, United States of America
Abstract: Article-level metrics
(ALMs) provide a wide range of
metrics about the uptake of an
individual journal article by the
scientific community after publi-
cation. They include citations,
usage statistics, discussions in on-
line comments and social media,
social bookmarking, and recom-
mendations. In this essay, we
describe why article-level metrics
are an important extension of
traditional citation-based journal
metrics a nd provide a number of
example from ALM data collec ted
for PLOS Biology.
The scientific impact of a particular
piece of research is reflected in how this
work is taken up by the scientific commu-
nity. The first systematic approach that
was used to assess impact, based on the
technology available at the time, was to
track citations and aggregate them by
journal. This strategy is not only no longer
necessary—since now we can easily track
citations for individual articles—but also,
and more importantly, journal-based met-
rics are now considered a poor perfor-
mance measure for individual articles
[1,2]. One major problem with journal-
based metrics is the variation in citations
per article, which means that a small
percentage of articles can skew, and are
responsible for, the majority of the jour-
nal-based citation impact factor, as shown
by Campbell [1] for the 2004 Nature
Journal Impact Factor. Figure 1 further
illustrates this point, showing the wide
distribution of citation counts between
PLOS Biology research articles published
in 2010. PLOS Biology research articles
published in 2010 have been cited a
median 19 times to date in Scopus, but
10% of them have been cited 50 or more
times, and two articles [3,4] more than
300 times. PLOS Biology metrics are used as
examples throughout this essay, and the
dataset is available in the supporting
information (Data S1). Similar data are
available for an increasing number of
other publications and organizations.
Scientific impact is a multi-dimensional
construct that can not be adequately
measured by any single indicator [2,5,6].
To this end, PLOS has collected and
displayed a variety of metrics for all its
articles since 2009. The array of different
categorised article-level metrics (ALMs)
used and provided by PLOS as of August
2013 are shown in Figure 2. In addition to
citations and usage statistics, i.e., how
often an article has been viewed and
downloaded, PLOS also collects metrics
about: how often an article has been saved
in online reference managers, such as
Mendeley; how often an article has been
discussed in its comments section online,
and also in science blogs or in social
media; and how often an article has been
recommended by other scientists. These
additional metrics provide valuable infor-
mation that we would miss if we only
consider citations. Two important short-
comings of citation-based metrics are that
(1) they take years to accumulate and (2)
citation analysis is not always the best
indicator of impact in more practical
fields, such as clinical medicine [7]. Usage
statistics often better reflect the impact of
work in more practical fields, and they also
sometimes better highlight articles of
general interest (for example, the 2006
PLOS Biology article on the citation advan-
tage of Open Access articles [8], one of the
10 most-viewed articles published in PLOS
Biology).
A bubble chart showing all 2010 PLOS
Biology articles (Figure 3) gives a good
overview of the year’s views and citations,
plus it shows the influence that the article
type (as indicated by dot color) has on an
article’s performance as measured by these
metrics. The weekly PLOS Biology publica-
tion schedule is reflected in this figure,
with articles published on the same day
present in a vertical line. Figure 3 also
shows that the two most highly cited
2010 PLOS Biology research articles are
also among the most viewed (indicated
by the red arrows), but overall there isn’t
astrongcorrelationbetween citations
and views. The most-viewed a rticle
published in 2010 in PLOS Biology is an
essay on Darwinian selection in robots
[9]. De tailed usage statistics also allo w
speculatul ation about the differen t ways
that readers access and make use of
published literature; some articles are
browsed or read online due to general
interest while othersthataredownloaded
(and perhaps also printed) may reflect
the reader’s intention to look at the data
and results in detail and to return to the
articlemorethanonce.
When readers first see an interesting
article, their response is often to view or
download it. By contrast, a citation may be
one of the last outcomes of their interest,
occuring only about 1 in 300 times a
PLOS paper is viewed online. A lot of
things happen in between these potential
responses, ranging from discussions in
comments, social media, and blogs, to
bookmarking, to linking from websites.
These activities are usually subsumed
under the term ‘‘altmetrics,’’ and their
variety can be overwhelming. Therefore, it
helps to group them together into catego-
ries, and several organizations, including
PLOS, are using the category labels of
Viewed, Cited, Saved, Discussed, and
Recommended (Figures 2 and 4, see also
[10]).
Essays articulate a specific perspective on a topic of
broad interest to scientists.
Citation: Fenner M (2013) What Can Article-Level Metrics Do for You? PLoS Biol 11(10): e1001687. doi:10.1371/
journal.pbio.1001687
Published October 22, 2013
Copyright: ß 2013 Martin Fenner. This is an open-access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original author and source are credited.
Funding: The author received no specific funding for this work.
Competing Interests: Martin Fenner is the technical lead for the PLOS Article-Level Metrics project.
* E-mail: mfenner@plos.org
PLOS Biology | www.plosbiology.org 1 October 2013 | Volume 11 | Issue 10 | e1001687
All PLOS Biology articles are viewed and
downloaded, and almost all of them (all
research articles and nearly all front
matter) will be cited sooner or later.
Almost all of them will also be book-
marked in online reference managers,
such as Mendeley, but the percentage of
articles that are discussed online is much
smaller. Some of these percentages are
time dependent; the use of social media
discussion platforms, such as Twitter and
Facebook for example, has increased in
recent years (93% of PLOS Biology research
articles published since June 2012 have
been discussed on Twitter, and 63%
mentioned on Facebook). These are the
locations where most of the online discus-
sion around published articles currently
seems to take place; the percentage of
papers with comments on the PLOS
website or that have science blog posts
written about them is much smaller. Not
all of this online discussion is about
research articles, and perhaps, not surpris-
ingly, the most-tweeted PLOS article
overall (with more than 1,100 tweets) is a
PLOS Biology perspective on the use of
social media for scientists [11].
Some metrics are not so much indica-
tors of a broad online discussion, but
rather focus on highlighting articles of
particular interest. For example, science
blogs allow a more detailed discussion of
an article as compared to comments or
tweets, and journals themselves sometimes
choose to highlight a paper on their own
blogs, allowing for a more digestible
explanation of the science for the non-
expert reader [12]. Coverage by other
bloggers also serves the same purpose; a
good example of this is one recent post on
the OpenHelix Blog [13] that contains
video footage of the second author of a
2010 PLOS Biology article [14] discussing
the turkey genome.
F1000Prime, a commercial service of
recommendations by expert scientists, was
added to the PLOS Article-Level Metrics
in August 2013. We now highlight on the
PLOS website when any articles have
received at least one recommendation
within F1000Prime. We also monitor
when an article has been cited within the
widely used modern-day online encyclo-
pedia, Wikipedia. A good example of the
latter is the Tasmanian devil Wikipedia
page [15] that links to a PLOS Biology
research article published in 2010 [16].
While a F1000Prime recommendation is a
strong endorsement from peer(s) in the
scientific community, being included in a
Wikipedia page is akin to making it into a
textbook about the subject area and being
read by a much wider audience that goes
beyond the scientific community.
PLOS Biology is the PLOS journal with
the highest percentage of articles recom-
mended in F1000Prime and mentioned in
Wikipedia, but there is only partial overlap
between the two groups of articles because
they focus on different audiences (Figure 5).
These recommendations and mentions in
Figure 2. Article-level metrics used by PLOS in August 2013 and their categories. Taken from [10] with permission by the authors.
doi:10.1371/journal.pbio.1001687.g002
Figure 1. Citation counts for
PLOS Biology
articles published in 2010. Scopus citation
counts plotted as a probability distribution for all 197 PLOS Biology research articles published in
2010. Data collected May 20, 2013. Median 19 citations; 10% of papers have at least 50 citations.
doi:10.1371/journal.pbio.1001687.g001
PLOS Biology | www.plosbiology.org 2 October 2013 | Volume 11 | Issue 10 | e1001687
turn show correlations with other metrics,
but not simple ones; you can’t assume, for
example, that highly cited articles are
more likely to be recommended by
F1000Prime, so it will be interesting to
monitor these trends now that we include
this information.
With the increasing availability of ALM
data, there comes a growing need to
provide tools that will allow the commu-
nity to interrogate them. A good first step
for researchers, research administrators,
and others interested in looking at the
metrics of a larger set of PLOS articles is
the recently launched ALM Reports tool
[17]. There are also a growing number of
service providers, including Altmetric.com
[18], ImpactStory [19], and Plum Analyt-
ics [20] that provide similar services for
articles from other publishers.
As article-level metrics become increas-
ingly used by publishers, funders, univer-
sities, and researchers, one of the major
challenges to overcome is ensuring that
standards and best practices are widely
adopted and understood. The National
Information Standards Organization
(NISO) was recently awarded a grant by
the Alfred P. Sloan Foundation to work on
this [21], and PLOS is actively involved in
this project. We look forward to further
developing our article-level metrics and to
having them adopted by other publishers,
which hopefully will pave the way to their
wide incorporation into research and
researcher assessments.
Supporting Information
Data S1 Dataset of ALM for PLOS
Biology articles used in the text, and
R scripts that were used to produce
figures. The data were collected on May
Figure 3. Views vs. citations for
PLOS Biology
articles published in 2010. All 304 PLOS Biology articles published in 2010. Bubble size
correlates with number of Scopus citations. Research articles are labeled green; all other articles are grey. Red arrows indicate the two most highly
cited papers. Data collected May 20, 2013.
doi:10.1371/journal.pbio.1001687.g003
Figure 4. Article-level metrics for
PLOS Biology
. Proportion of all 1,706 PLOS Biology research
articles published up to May 20, 2013 mentioned by particular article-level metrics source. Colors
indicate categories (Viewed, Cited, Saved, Discussed, Recommended), as used on the PLOS
website.
doi:10.1371/journal.pbio.1001687.g004
PLOS Biology | www.plosbiology.org 3 October 2013 | Volume 11 | Issue 10 | e1001687
20, 2013 and include all PLOS Biology
articles published up to that day. Data for
F1000Prime were collected on August 15,
2013. All charts were produced with R
version 3.0.0.
(ZIP)
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doi:10.1371/journal.pbio.1001687.g005
PLOS Biology | www.plosbiology.org 4 October 2013 | Volume 11 | Issue 10 | e1001687
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