What paper should I read next? Who should I talk to at a conference? Which research
group should get this grant? Researchers and funders alike must make daily judgments
on how to best spend their limited time and money–judgments that are becoming increasingly
difficult as the volume of scholarly communication increases. Not only does the number
of scholarly papers continue to grow, it is joined by new forms of communication from
data publications to microblog posts.
To deal with incoming information, scholars have always relied upon filters. At first
these filters were manually compiled compendia and corpora of the literature. But
by the mid-20th century, filters built on manual indexing began to break under the
weight of booming postwar science production. Garfield  and others pioneered a
solution: automated filters that leveraged scientists own impact judgments, aggregating
citations as “pellets of peer recognition.” .
These citation-based filters have dramatically grown in importance and have become
the tenet of how research impact is measured. But, like manual indexing 60 years ago,
they may today be failing to keep up with the literature’s growing volume, velocity,
and diversity .
Citations are heavily gamed – and are painfully slow to accumulate , and
overlook increasingly important societal and clinical impacts . Most importantly,
they overlook new scholarly forms like datasets, software, and research blogs that
fall outside of the scope of citable research objects. In sum, citations only reflect
formal acknowledgment and thus they provide only a partial picture of the science
system . Scholars may discuss, annotate, recommend, refute, comment, read, and
teach a new finding before it ever appears in the formal citation registry. We need
new mechanisms to create a subtler, higher-resolution picture of the science system.
The Quest for Better Filters
The scientometrics community has not been blind to the limitations of citation measures,
and has collectively proposed methods to gather evidence of broader impacts and provide
more detail about the science system: tracking acknowledgements , patents ,
mentorships , news articles , usage in syllabuses , and many others, separately
and in various combinations . The emergence of the Web, a “nutrient-rich space
for scholars” , has held particular promise for new filters and lenses on scholarly
output. Webometrics researchers have uncovered evidence of informal impact by examining
networks of hyperlinks and mentions on the broader Web –. An important strand
of webometrics has also examined the properties of article download data , ,
The last several years, however, have presented a promising new approach to gathering
fine-grained impact data: tracking large-scale activity around scholarly products
in online tools and environments. These tools and environments include, among others:
social media like Twitter and Facebook
online reference managers like CiteULike, Zotero, and Mendeley
collaborative encyclopedias like Wikipedia
blogs, both scholarly and general-audience
scholarly social networks, like ResearchGate or Academia.edu
conference organization sites like Lanyrd.com
Growing numbers of scholars are using these and similar tools to mediate their interaction
with the literature. In doing so, they are leaving valuable tracks behind them–tracks
with potential to show informal paths of influence with unprecedented speed and resolution.
Many of these tools offer open APIs, supporting large-scale, automated mining of online
activities and conversations around research objects .
Altmetrics ,  is the study and use of scholarly impact measures based on activity
in online tools and environments. The term has also been used to describe the metrics
themselves–one could propose in plural a “set of new altmetrics.” Altmetrics is in
most cases a subset of both scientometrics and webometrics; it is a subset of the
latter in that it focuses more narrowly on scholarly influence as measured in online
tools and environments, rather than on the Web more generally.
Altmetrics may support finer-grained maps of science, broader and more equitable evaluations,
and improvements to the peer-review system . On the other hand, the use and development
of altmetrics should be pursued with appropriate scientific caution. Altmetrics may
face attempts at manipulation similar to what Google must deal with in web search
ranking. Addressing such manipulation may, in-turn, impact the transparency of altmetrics.
New and complex measures may distort our picture of the science system if not rigorously
assessed and correctly understood. Finally, altmetrics may promote an evaluation system
for scholarship that many argue has become overly focused on metrics.
Scope of this Collection
The goal of this collection is to gather an emerging body of research for the further
study and use of altmetrics. We believe it is greatly needed, as important questions
regarding altmetrics’ prevalence, validity, distribution, and reliability remain incompletely
answered. Importantly, the present collection, which has the virtue of being online
and open access, allows altmetrics researchers to experiment on themselves.
The collection’s scope includes:
Statistical analysis of altmetrics data sources, and comparisons to established sources
Metric validation, and identification of biases in measurements
Validation of models of scientific discovery/recommendation based on altmetrics
Qualitative research describing the scholarly use of online tools and environments
Empirically-supported theory guiding altmetrics’ use
Other research relating to scholarly impact in online tools and environments.
The current collection includes articles that address many of these areas. It will
publish new research on an ongoing basis, and we hope to see additional contributions
appear in the coming months. We look forward to building a foundation of early research
to support this new field.