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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]
The GAISE document provides a two-dimensional framework, [11] specifying four components used in statistical problem solving (formulating questions, collecting data, analyzing data, and interpreting results) and three levels of conceptual understanding through which a student should progress (Levels A, B, and C). [12]
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]
A sample implementation of an ACS unit. It is possible to monitor the noise level on the incoming bit stream by monitoring the rate of growth of the "best" path metric. A simpler way to do this is to monitor a single location or "state" and watch it pass "upward" through say four discrete levels within the range of the accumulator.
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 .
Examples of commonly used bibliometrics for science, or scientometrics, are the h-index, impact factor, and websites displaying indicators such as Altmetrics. According to Hicks et al., these metrics often pervasively misguide evaluations of scientific material.
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.