  Citation metrics are analytic measures used to evaluate the usage, impact and
dissemination of scientific research. Traditionally, citation metrics have been
independently measured at each level of the publication pyramid, namely at the
article-level, at the author-level, and at the journal-level. The most commonly
used metrics have been focused on journal-level measurements, such as the
Impact Factor and the Eigenfactor, as well as on researcher-level metrics like
the Hirsch index (h-index) and i10 index. On the other hand, reliable
article-level metrics are less widespread, and are often reserved to
non-standardized and non-scientific characteristics of individual articles,
such as views, citations, downloads, and mentions in social and news media.
These characteristics are known as 'altmetrics'. However, when the number of
views and citations are similar between two articles, no discriminating measure
currently exists with which to assess and compare each articles' individual
impact. Given the modern, exponentially growing scientific literature,
scientists and readers of Science need optimized, reliable, objective methods
for managing, measuring and comparing research outputs and individual
publications. To this end, I hereby describe and propose a new standardized
article-level metric henceforth known as the 'Individual Impact Index
Statistic', or $i^3$ for short. The $i^3$ is a weighted algorithm that takes
advantage of the peer-review process, and considers a number of characteristics
of individual scientific publications in order to yield a standardized and
readily comparable measure of impact and dissemination. The strengths,
limitations, and potential uses of this novel metric are also discussed.
