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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).
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 .
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.
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]
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 h-index is an author-level metric that measures both the productivity and citation impact of the publications, initially used for an individual scientist or scholar. The h-index correlates with success indicators such as winning the Nobel Prize, being accepted for research fellowships and holding positions at top universities. [1]
Altmetrics can be gamed: for example, likes and mentions can be bought. [56] Altmetrics can be more difficult to standardize than citations. One example is the number of tweets linking to a paper where the number can vary widely depending on how the tweets are collected. [57] Besides, online popularity may not equal to scientific values.
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 ...