H-index is a widely used metric that attempts to measure both your productivity and your citation impact. It is a measure of the number of citations and the number of outputs.
How is it calculated
The h-index is the number (h) of your papers that have received (h) citations each. For example, if you have 26 published outputs that have each been cited at least 26 times, your h-index is 26. This index demonstrates that you have a range of papers with high citation levels rather than one or two outliers with very high citations.
Source: Hirsch, J. E. (2005), An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569-16572. https://doi.org/10.1073/pnas.0507655102
Similar indices
- i10-index is the number of publications with at least 10 citations.
- h5-index is your h-index relating to outputs published within the last five years only. Check this webpage for more information on h-indices (by Elsevier).
Considerations
- Your h-index will vary depending on where you source it from, as each platform calculates it in relation to other content on that platform. For example, your Scopus h-index will only count citations you have received in other Scopus content; the same is true in Web of Science. Your h-index is likely to be highest in Google Scholar as they harvest content from various sources rather than just one database.
- The h-index doesn't take into account the length of your career or the differences in publishing speed and quantity between disciplines. It is, therefore, biased against early career researchers, people who publish infrequently and people who are selective or niche with their publishing platform.
- A h-index number on its own is not helpful without any context. You need to know what the benchmark h-index is for a researcher at the same career level as you in your field and then look at how you compare to that. Understanding this means you can be strategic about whether or not you are going to use this number. As always, this one metric should not be used in isolation to measure scholarly impact or research quality.
