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Bibliometrics & Measuring Research Output: Individual data

Bibliometric analysis at the individual level

The University's White Paper, "Measuring Research Output Through Bibliometrics", identifies how bibliometric data can be used to support a bibliometric analysis at the individual level, and offers context for:


Possible objectives of an individual-level bibliometric analysis include:

  1. Understanding which of your articles have been cited, and how many times they have been cited within specific citation-tracking databases.
  2. Gaining insight into citing works and authors to identify possibilities for collaboration.

It is also important to be mindful of how the level of detail influences an individual-level bibliometric analysis. For example, small sample sizes which may undermine the possibility for robust comparisons.

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Appropriate uses

Appropriate uses of an individual-level bibliometric analysis include:

  • Person-level analysis, intended for an individual researcher to gain a personal understanding of their research output and impact examples.
  • Highlighting highly cited documents in advancement packages, funding competitions, or awards applications.

Possible Measures

  • Publication counts, either total or broken down by year.
  • Citation counts, either total or broken down by publication, as well as highly cited publications.
  • H-index or a variant.
  • For more information, see Measures.


  • When doing comparisons be sure to use the same data source (citation-tracking database) for all data collection (e.g., only Web of Science or only Scopus). Never compare metrics from two different data sources.

Inappropriate uses

Inappropriate uses of an individual-level bibliometric analysis include:

  • Comparing individual researchers solely based on h-index data.

  • Performance measurement to compare between researchers for personnel decisions, including hiring, merit review, and tenure.

  • Comparing research output among individual researchers, such as those from different disciplines or fields, at different stages of career, or with different research foci. 

  • Comparing women researchers’ cited works with men as there is a known bias.

  • Using measures to compare areas that are not robust or well captured in citation-tracking databases. For example, regional and interdisciplinary disciplines, or fields where books or conference proceedings are the primary forms of research output.