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An overview of research metrics: author level, journal level, and article level

An overview of research metrics: author level, journal level, and article level

Research metrics are the measures that are used to evaluate the significance or impact of peer-reviewed research work. There are different types of metrics that can be generated at various levels. For instance, how frequently is a research article or a group of research articles cited by other researchers to demonstrate the significance of a journal or a field of study. Here we will discuss the three types of metrics: author level, journal level, and article level. Learning about the classifications, applications, and restrictions of these metrics will assist you in making future decisions on where to publish as well as improve your understanding of research.

Metrics at the article level

Article level metrics are measures of the reach and impact of published articles. It is an attempt to combine the data from new sources (social media mentions) and traditional sources (citations) to represent a broader picture of individual works and how they are being shared, applied, and talked about. Citation count, altmetric, and outputs in the top percentiles are used to quantify the article level metrics.

Citation count

Citations of any article can be counted and tracked by multidisciplinary databases such as Google Scholar, Scopus, and the Web of Science. Because each of these databases contains published articles, the citation count of each article is likely to differ across the three databases.


It’s defined as a collection of scholarly action-related digital information gathered from numerous social media sources. These metrics can be collected more quickly than traditional methods (citation). It includes usage data, social media mentions, and reference management software. It can give a wider impact to research studies by monitoring their use in policy documents, news articles, and so on.

Outputs in the top percentiles indicate how frequently your articles appear in the most-cited thresholds of a data source (e.g., Scopus) per publication year. It is used for a collection of journal articles and is normalised for the field in which you write.

Metrics at the journal level

Journal level metrics are used to quantify the impact or influence of a journal by considering the number of citations received by the articles in that particular journal. These metrics reflect the position of the journal in its related field, the prestige associated with it, and the difficult criteria for being published in that journal. There are several journal metrics with different types of calculation criteria and data sets. Here we look at important metrics such as journal impact factor, Scientific Journal Ranking (SJR), SNIP, cite score, and Eigenfactor.

Impact factor

There are well-known metrics for journals which show the popularity of journals in their field. It is calculated by dividing the total number of citations in the previous two years’ publications by the total number of citable research or review articles published in the last two years.

SCImago Journal Rank (SJR)

Scientific journal ranking involves two factors: the first is the total number of citations received by the journal; and the second is the prestige of the source of the journal citation. Details of the SJR calculation are available here. Because of the iterative computation procedure, calculating the SJR of a journal is not possible by yourself. SJR uses data from the Scopus database.

Source normalized impact per paper (SNIP)

SNIP scores are calculated as the ratio of a source’s average citation count to its citation potential. The quantity of citations that a journal is predicted to receive for its subject field is referred to as its citation potential. SNIP is calculated using the scopus data and CWTS Journal Indicators.

Cite score

Cite score is another metric to calculate the impact of a journal.It computes the average number of citations to documents by a journal during a four-year period, divided by the number of the same document categories indexed in Scopus and published over the same period.The cite score is easy to understand and can be calculated by yourself. The drawback of these metrics is that they can be easily manipulated by the editors by publishing review articles that are read more than research articles.


The Eigenfactor score was intended to measure the significance of journals in the scientific community. Carl and Theodore Bergstrom of the University of Washington developed eigenfactor metrics. Basically, the eigenfactor of a journal shows how many people frequently read the journal and find its contents worthwhile. For instance, Nature has eigenfactor value because it is a large journal that publishes research articles and reviews in every discipline of science with reliable and valuable content, which increases its citation. A detailed method for the calculation of the Eigenfactor is given here.

Author-level metrics

Author level metrics are intended to measure the impact of individual authors in the scientific community.

H index is calculated by counting the number of publications for which the author has been cited by other authors at least that same number of times. For example, if an author has an H index 5 it means the author has 5 publications which have been cited by other authors at least 5 times. The criticism of h index is that it does not count the highly cited papers and career span of the author. It is simply based on productivity and impact. Authors with a longer career span have more publications and will have a higher h index score.

G index

G index calculated by the distribution of citations revived by a given researcher’s publication. [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 g2 citations. ” (from Harzig’s Publish or Perish Manual). For example, if an author has a 10 G index, it indicates that at least his 10 publications have been cited at least 100 times (g2). In contrast to the h index, these scores can be produced from a small number of article citations. For example, if a professor has 15 papers, 10 of which have no citations, and the other five 5 have 55, 30, 10, 5, and 5 have a g index of 10, but a h index of 5, (5 articles have at least 5 citations). The limitation of the G index is that it always favours academicians who have published more papers.

I10 index

This index was created by Google Scholar and counts the number of publications that have at least 10 citations. For additional details about these metrics, visit Publish or Perish and the Metrics Toolkit.

Each type of research metrics have their own limitations and it does not tell you about the quality of research. We should always consider the other factors also to assess the quality of research work.

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