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Publishing Your Research: Author-level Metrics

Guide to Getting Published

Author-level Metrics

Author-level metrics are citation metrics that measure the bibliometric impact of individual authors. H-index is the best-known author-level metric.  Since it was proposed by JE Hirsch in 2005 it has gained a lot of popularity amongst researchers while bibliometics scholars proposed a few variants to account for its weaknesses (g-index, m-index are good examples).

Unique Researcher IDs

Due to common researcher names, name changes, cultural differences in name order, and inconsistent use of middle initials, it can be difficult to accurately calculate measures of personal impact. Numeric codes can help identify individual researchers. 

ORCID (Open Researcher and Contributor ID)

ResearcherID (Clarivate Analytics)

By registering for a unique identifier, you can potentially connect a diverse array of your scholarly output, including journal articles, datasets, patents, and online comments.

Scopus Author Profile is a unique record of that researcher’s publication activity. The details come from peer-reviewed articles and other publications that are indexed in Scopus. Create your free account with Elsevier (vendor for Scopus, Embase, Clinical Key) to edit your profile.

Google Scholar

Google Scholar calculates not only h-index, but the i10-index for authors with a public profile which must created by the author.

The i10-index is simply the number of articles with at least 10 citations.

In Google Scholar, searching for an author will pull up the profile as the first result if they have a Google Scholar profile. Clicking on the profile will bring you to their profile page, which includes both metrics, as seen below.



"For the few scientists who earn a Nobel Prize, the impact...of their research is unquestionable. For the rest of us, how does one quantify the cumulative impact of an individual's scientific research output?" --  J.E. Hirsch 2005

Your productivity as a researcher can be measured by your total number of articles, and the impact of your research can be measured by the total number of times your articles have been cited. The h-index (AKA Hirsch index) is a combined measure of both productivity and impact. An index of h means that your h most highly-cited articles have at least h citations each. 

The h-index is more informative than total number of articles (which ignores how well those articles have been received by other researchers) or total number of citations (which can be inordinately influenced by a small number of highly-cited articles and therefore not an accurate reflection of productivity). 

One caveat about the h-index is that it correlates with the length of a researcher's career (i.e., researchers who have been publishing for longer tend to have higher h-indices). It can also be inflated by self-citation. In addition, the h-index ignores the order of authorship, which is very important in some disciplines. Additionally, because different disciplines have different publishing practices, the h-index should not be used to compare researchers across different disciplines. Average impact scores vary widely from discipline to discipline. 

iCite for Author Metrics

iCite provides bibliometric information for journal articles that have been included in the PubMed database.   Citation data are drawn from: PubMed Central, European PubMed Central, CrossRef, and Web of Science. At present, only PubMed citations are included, so citations appearing from journals outside PubMed are not counted.  

For an analysis by author, the following data will be produced: 

  • Total number of articles within PubMed (Total Pubs)
  • Mean number of articles published per year (Pubs/Year)
  • Number of citations for the articles (Cites/Year): maximum, mean, standard error of the mean, and median
  • Relative Citation Ratio (RCR): maximum, mean, standard error of the mean, and median
  • Weighted RCR: the sum of the RCRs for the articles within the analysis group

    The Relative Citation Ratio (RCR) is a NIH metric representing a citation-based measure of scientific influence. It is calculated as the cites/year of each paper, normalized to the citations per year received by NIH-funded papers in the same field and year. A paper with an RCR of 1.0 has received the same number of cites/year as the median NIH-funded paper in its field, while a paper with an RCR of 2.0 has received twice as many cites/year as the median NIH-funded paper in its field. 

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