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Scholarly Publishing: Traditional Metrics

A guide to scholarly publishing and scholarly communication activities at Himmelfarb Library.

What are Bibliometrics?

Bibliometrics refers to measuring impact by counting citations, specifically by counting how many times a particular work has been cited by other works. 

Video: "How to Use Bibliometrics Effectively," from Thomson Reuters Web of Science

Help With Citation Analysis

The Himmelfarb Health Sciences Library provides access to a number of resources to help you analyze citations:

  • Scopus
    • Click "Analytics" on the blue ribbon above the search bar
    • You can get SJR and SNIP values (see "Journal Impact" tab) for journals and create charts comparing different titles
  • Web of Science
    • Click "Additional Resources" tab at the top of the page
    • Select "Journal Citation Reports" to get access to citation data
  • Quantifying Scholarly Research Impact guide
    • Reference & Instruction Librarian Tom Harrod's guide contains tips on calculating an author- and journal-level metrics (H-index, G-index, Eigenfactor, Scimago Journal Rank, and others) and a case study of two authors.

Pros and Cons of Bibliometrics

Pros

  • Citation = Impact: If an article is cited, that typically indicates that the work has had some impact on others in the field.
  • Objectivity: Metrics are calculated using equations, reducing the chance of bias that might occur through human judgment.
  • Statistical Analysis: There are many tools available to provide authors with citation data and to perform advanced statistical analyses of that data. 
  • Time-Saving: Relatively easy to implement, methods are straightforward. 

Cons

  • Time: Citations take 1-2 years on average to accumulate (Priem, Piwowar & Hemminger, 2012), so newer works may be left out.
  • Impact Level: Citation analysis only works at the author or journal level; cannot measure impact at the article level.
  • Narrow Focus: Focuses on peer-reviewed articles only, leaves out other types of research such as datasets that comprise a large part of the global literature.
  • Language Bias: Article authors are more likely to cite works written in the same language as the article.

Priem, J., Piwowar, H.A., & Hemminger, B.M. (2013). Altmetrics in the wild: Using social media to explore scholarly impact. Pre-print. Retrieved from http://arxiv.org/abs/1203.4745v1.

Measuring Scholarly Research Impact - Books in the Himmelfarb Collection

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