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Citation metrics are analytic measures used to evaluate the usage, impact anddissemination of scientific research. Traditionally, citation metrics have beenindependently measured at each level of the publication pyramid, namely at thearticle-level, at the author-level, and at the journal-level. The most commonlyused metrics have been focused on journal-level measurements, such as theImpact Factor and the Eigenfactor, as well as on researcher-level metrics likethe Hirsch index (h-index) and i10 index. On the other hand, reliablearticle-level metrics are less widespread, and are often reserved tonon-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 ofviews and citations are similar between two articles, no discriminating measurecurrently exists with which to assess and compare each articles' individualimpact. Given the modern, exponentially growing scientific literature,scientists and readers of Science need optimized, reliable, objective methodsfor managing, measuring and comparing research outputs and individualpublications. To this end, I hereby describe and propose a new standardizedarticle-level metric henceforth known as the 'Individual Impact IndexStatistic', or $i^3$ for short. The $i^3$ is a weighted algorithm that takesadvantage of the peer-review process, and considers a number of characteristicsof individual scientific publications in order to yield a standardized andreadily comparable measure of impact and dissemination. The strengths,limitations, and potential uses of this novel metric are also discussed.

The Individual Impact Index ($i^3$) Statistic: A Novel Article-Level Citation Metric