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Article-level metrics are citation metrics which measure the usage and impact of individual scholarly articles. The most common article-level citation metric is the number of citations. [ 1 ] Field-weighted Citation Impact (FWCI) by Scopus divides the total citations by the average number of citations for an article in the scientific field .
In any given year, the CiteScore of a journal is the number of citations, received in that year and in previous three years, for documents published in the journal during the total period (four years), divided by the total number of published documents (articles, reviews, conference papers, book chapters, and data papers) in the journal during the same four-year period: [3]
The simplest journal-level metric is the journal impact factor, the average number of citations that articles published by a journal in the previous two years have received in the current year, as calculated by Clarivate. Other companies report similar metrics, such as the CiteScore, based on Scopus.
Author-level metrics are citation metrics that measure the bibliometric impact of individual authors, researchers, academics, and scholars. Many metrics have been developed that take into account varying numbers of factors (from only considering the total number of citations, to looking at their distribution across papers or journals using statistical or graph-theoretic principles).
The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. [1] Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. [2]
An additional example is provided by patents which contain prior art, citation of earlier patents relevant to the current claim. The digitization of patent data and increasing computing power have led to a community of practice that uses these citation data to measure innovation attributes, trace knowledge flows, and map innovation networks. [3]
Bibliometrics is the application of statistical methods to the study of bibliographic data, especially in scientific and library and information science contexts, and is closely associated with scientometrics (the analysis of scientific metrics and indicators) to the point that both fields largely overlap.
The g-index is an author-level metric suggested in 2006 by Leo Egghe. [1] The index is calculated based on the distribution of citations received by a given researcher's publications, such that given a set of articles ranked in decreasing order of the number of citations that they received, the g-index is the unique largest number such that the top g articles received together at least g 2 ...