For example, the 2016 impact factor of a journal would be calculated as A/B, where:
A = the number of times that all items published in that journal in 2015 and 2016 were cited by indexed publications during 2017.
B = the total number of 'citable items'* published by that journal in 2015 and 2016.
Despite attempts to make journal impact factors more sophisticated critics argue that are still a crude numeric metric. Caution should be exercised in comparing journals across disciplines. It is also worth noting that however the journal impact is measured it does not necessarily reflect the impact that the research has had in the 'real world'.
Find further resources about the new Journal Citation Reports interface here.
You may like to read this short document, 'using Journal Citation Reports wisely', which puts forward some of the reasons why you should exercise caution when using citation data as a means of evaluating journals.
* Note that Journal Citation Reports does show the scores for both the impact factor and the impact factor without self-cites.
** e.g., the journal with the highest impact factor in the category Medicine (2013) was New England Journal of Medicine with an impact factor of 54.420; the journal with the highest impact factor in the category Nursing (2013) was Oncology Nursing Forum with an impact factor of 2.830.
Another key indicator used in Journal Citation Reports (JCR) is the Eigenfactor. The Eigenfactor Project is an academic research project developed by the DataLab at the University of Washington. The aim of the project is to 'provide the scientific community with what we believe to be a better method of evaluating the influence of scholarly journals', through the use of recent advances in network analysis.
The raw citation data used to compute Eigenfactor metrics derives from JCR. Meanwhile, Eigenfactor scores are shown alongside other metrics, including impact factors, in JCR; see this example for Nature:
The Eigenfactor algorithm is based on an iterative voting scheme, or a “random walk,” around the citation network; Eigenfactor scores are based on the amount of time a user is likely to spend in a given journal in the network.
Eigenfactor scores are scaled so that the scores of all journals indexed in JCR add up to 100. So if a journal has an Eigenfactor score of 1.0, it has 1% of the total influence of all indexed publications. In 2013, the journal Nature had the highest Eigenfactor score, with a value of 1.603.
The project website identifies a number of what it considers are the advantages of Eigenfactor metrics, including:
CiteScore metrics measures journal citation impacts and are produced by Scopus. It uses eight indicators to analyze publication influence. Information about the metrics features are available here.
The calculation of CiteScore for the current year is based on the number of citations received by a journal in that year for the documents published in the journal in the past three years, divided by the documents indexed in Scopus published in those three years. The calculation of CiteScore for the current year is based on the number of citations received by a journal in that year for the documents published in the journal in the past three years, divided by the documents indexed in Scopus published in those three years. If you wish to know more about how the scores are calculated click here.
There is a quick reference for research impact metrics available.
SNIP (Source Normalized Impact per Paper):
SJR (SCImago Journal Rank):
"altmetrics is the creation and study of new metrics based on the Social Web for analyzing, and informing scholarship." (altmetrics.org)
How are altmetrics calculated?
Different sources go into altmetric calculations, depending on the provider and the information that they are using. Generally, a high altmetric score indicates that an item has received a lot of attention and it has also received what that provider has decided is "quality" attention (i.e. a news post might be more valuable than a twitter mention).
Altmetrics can help researchers understand how their outputs are being shared and discussed via social media and online, and may supplement the information gained from traditional indicators.
The Altmetric badge is often used by publishers on their websites and resources to display the Altmetric data. The tutorial below shows you how it works.
Dimensions Plus is a modern and innovative, linked research data infrastructure and tool, reimagining discovery and access to research: grants, publications, citations, clinical trials, patents and policy documents in one place. The Guide to the Dimensions Data Approach provides an overview of the Dimensions database content
For comprehensive information and support, use the Dimensions support site.
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