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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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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
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
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
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/
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:
PLOS Biology | 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.
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.
PLOS Biology | 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
[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.
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
PLOS Biology | 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.
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2013 that have been recommended by F1000Prime (red) or mentioned in Wikipedia (blue).
PLOS Biology | 4 October 2013 | Volume 11 | Issue 10 | e1001687
... In addition, this situation is linked to the open international debate on the need for a change in the evaluation of research and the excessive weight given to the impact factors of continents (journals), instead of taking into account the quality of the published product, using article-level metrics (Fenner, 2013;Gasparyan et al., 2021). ...
... Además, esta situación enlaza con el debate abierto internacionalmente sobre la necesidad de un cambio en la evaluación de la investigación y el excesivo peso que toman los factores de impacto de los continentes (las revistas), en lugar de tener en cuenta la calidad del producto publicado, utilizando métricas a nivel de artículo (Fenner, 2013;Gasparyan et al., 2021). ...
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A synergistic combination of two next-generation sequencing platforms with a detailed comparative BAC physical contig map provided a cost-effective assembly of the genome sequence of the domestic turkey (Meleagris gallopavo). Heterozygosity of the sequenced source genome allowed discovery of more than 600,000 high quality single nucleotide variants. Despite this heterozygosity, the current genome assembly (∼1.1 Gb) includes 917 Mb of sequence assigned to specific turkey chromosomes. Annotation identified nearly 16,000 genes, with 15,093 recognized as protein coding and 611 as non-coding RNA genes. Comparative analysis of the turkey, chicken, and zebra finch genomes, and comparing avian to mammalian species, supports the characteristic stability of avian genomes and identifies genes unique to the avian lineage. Clear differences are seen in number and variety of genes of the avian immune system where expansions and novel genes are less frequent than examples of gene loss. The turkey genome sequence provides resources to further understand the evolution of vertebrate genomes and genetic variation underlying economically important quantitative traits in poultry. This integrated approach may be a model for providing both gene and chromosome level assemblies of other species with agricultural, ecological, and evolutionary interest.
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The Australasian and South American marsupial mammals, such as kangaroos and opossums, are the closest living relatives to placental mammals, having shared a common ancestor around 130 million years ago. The evolutionary relationships among the seven marsupial orders have, however, so far eluded resolution. In particular, the relationships between the four Australasian and three South American marsupial orders have been intensively debated since the South American order Microbiotheria was taxonomically moved into the group Australidelphia. Australidelphia is significantly supported by both molecular and morphological data and comprises the four Australasian marsupial orders and the South American order Microbiotheria, indicating a complex, ancient, biogeographic history of marsupials. However, the exact phylogenetic position of Microbiotheria within Australidelphia has yet to be resolved using either sequence or morphological data analysis. Here, we provide evidence from newly established and virtually homoplasy-free retroposon insertion markers for the basal relationships among marsupial orders. Fifty-three phylogenetically informative markers were retrieved after in silico and experimental screening of approximately 217,000 retroposon-containing loci from opossum and kangaroo. The four Australasian orders share a single origin with Microbiotheria as their closest sister group, supporting a clear divergence between South American and Australasian marsupials. In addition, the new data place the South American opossums (Didelphimorphia) as the first branch of the marsupial tree. The exhaustive computational and experimental evidence provides important insight into the evolution of retroposable elements in the marsupial genome. Placing the retroposon insertion pattern in a paleobiogeographic context indicates a single marsupial migration from South America to Australia. The now firmly established phylogeny can be used to determine the direction of genomic changes and morphological transitions within marsupials.
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Loss-of-function mutations in the PINK1 or parkin genes result in recessive heritable forms of parkinsonism. Genetic studies of Drosophila orthologs of PINK1 and parkin indicate that PINK1, a mitochondrially targeted serine/threonine kinase, acts upstream of Parkin, a cytosolic ubiquitin-protein ligase, to promote mitochondrial fragmentation, although the molecular mechanisms by which the PINK1/Parkin pathway promotes mitochondrial fragmentation are unknown. We tested the hypothesis that PINK1 and Parkin promote mitochondrial fragmentation by targeting core components of the mitochondrial morphogenesis machinery for ubiquitination. We report that the steady-state abundance of the mitochondrial fusion-promoting factor Mitofusin (dMfn) is inversely correlated with the activity of PINK1 and Parkin in Drosophila. We further report that dMfn is ubiquitinated in a PINK1- and Parkin-dependent fashion and that dMfn co-immunoprecipitates with Parkin. By contrast, perturbations of PINK1 or Parkin did not influence the steady-state abundance of the mitochondrial fission-promoting factor Drp1 or the mitochondrial fusion-promoting factor Opa1, or the subcellular distribution of Drp1. Our findings suggest that dMfn is a direct substrate of the PINK1/Parkin pathway and that the mitochondrial morphological alterations and tissue degeneration phenotypes that derive from mutations in PINK1 and parkin result at least in part from reduced ubiquitin-mediated turnover of dMfn.
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Author Summary Mutations in the PINK1 or Parkin genes lead to an inherited form of Parkinson disease. Understanding how the products of these genes work may give us insights into what goes wrong in these patients and in Parkinson disease more generally. Previous studies in flies and mice, and in human cells suggest that PINK1 and Parkin are part of a common pathway that protects against damaged mitochondria; these organelles power the cell when healthy but can produce harmful reactive oxygen species when damaged. Exactly how PINK1 and Parkin work together to protect against damaged mitochondria is unclear. The findings we report in this paper suggest a new model in which PINK1 and Parkin together sense mitochondria in distress and selectively target them for degradation. In this pathway, PINK1 acts as a flag that accumulates on dysfunctional mitochondria and then signals to Parkin, which tags these mitochondria for destruction. Since disease-causing mutations in PINK1 or Parkin disrupt this pathway, patients with these mutations may not be able to clean up their damaged mitochondria, leading to the neuronal damage typical of parkinsonism.
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Genome-wide association studies (GWAS) have now identified at least 2,000 common variants that appear associated with common diseases or related traits (, hundreds of which have been convincingly replicated. It is generally thought that the associated markers reflect the effect of a nearby common (minor allele frequency >0.05) causal site, which is associated with the marker, leading to extensive resequencing efforts to find causal sites. We propose as an alternative explanation that variants much less common than the associated one may create "synthetic associations" by occurring, stochastically, more often in association with one of the alleles at the common site versus the other allele. Although synthetic associations are an obvious theoretical possibility, they have never been systematically explored as a possible explanation for GWAS findings. Here, we use simple computer simulations to show the conditions under which such synthetic associations will arise and how they may be recognized. We show that they are not only possible, but inevitable, and that under simple but reasonable genetic models, they are likely to account for or contribute to many of the recently identified signals reported in genome-wide association studies. We also illustrate the behavior of synthetic associations in real datasets by showing that rare causal mutations responsible for both hearing loss and sickle cell anemia create genome-wide significant synthetic associations, in the latter case extending over a 2.5-Mb interval encompassing scores of "blocks" of associated variants. In conclusion, uncommon or rare genetic variants can easily create synthetic associations that are credited to common variants, and this possibility requires careful consideration in the interpretation and follow up of GWAS signals.
As Editor-in-Chief of the journal Nature, I am concerned by the tendency within acade- mic administrations to focus on a journal's impact factor when judging the worth of scientific contri- butions by researchers, affecting promotions, recruitment and, in some countries, financial bonuses for each paper. Our own internal research demonstrates how a high journal impact factor can be the skewed result of many citations of a few papers rather than the average level of the majority, reduc- ing its value as an objective measure of an individual paper. Proposed alternative indices have their own drawbacks. Many researchers say that their important work has been published in low-impact journals. Focusing on the citations of individual papers is a more reliable indicator of an individual's impact. A positive development is the increasing ability to track the contributions of individuals by means of author-contribution statements and perhaps, in the future, citability of components of papers rather than the whole. There are attempts to escape the hierarchy of high-impact-factor jour- nals by means of undifferentiated databases of peer-reviewed papers such as PLoS One. It remains to be seen whether that model will help outstanding work to rise to due recognition regardless of editorial selectivity. Although the current system may be effective at measuring merit on national and institutional scales, the most effective and fair analysis of a person's contribution derives from a direct assessment of individual papers, regardless of where they were published.