The h-index definition and its known limitations are covered on this site’s h-index dictionary entry. This guide is the practical companion to that entry: it walks through the calculation mechanics in more depth, works a second numeric example that includes a tie in citation counts, and focuses specifically on why the same researcher can get three different h-index numbers from Google Scholar, Scopus, and Web of Science — and what to do about that when reporting the figure.
Hirsch’s original definition
The h-index was proposed by physicist Jorge E. Hirsch in a 2005 paper, “An index to quantify an individual’s scientific research output,” published in Proceedings of the National Academy of Sciences (PNAS 102(46):16569–16572). Hirsch’s definition: a researcher has an h-index of h if h of their papers have each been cited at least h times, while the remaining papers have each been cited fewer than h times. The index was designed as a single number balancing productivity (how many papers) against impact (how often they’re cited), specifically to resist being distorted either by a large volume of rarely-cited papers or by one or two extremely highly cited outliers.
How to calculate an h-index manually
- Pick one citation database and stay inside it. Google Scholar, Scopus, and Web of Science each index a different set of documents and count citations differently (see below), so mixing citation counts pulled from different sources into one list will produce a number that doesn’t correspond to any real database’s h-index.
- List every paper with its total citation count from that database, for the researcher (or, for a journal or research group, the same method applies to that entity’s output).
- Sort the list by citation count, highest to lowest, and assign each paper a rank starting at 1.
- Compare each paper’s citation count to its rank, moving down the sorted list.
- Find the last rank position where the citation count is still greater than or equal to the rank number. That rank number is the h-index. As soon as you reach a rank where the citation count drops below the rank, the h-index has already been passed — it’s the rank immediately before that point.
Most researchers never do this by hand for a large publication list — Google Scholar Citations profiles compute and display it automatically, and Scopus/Web of Science author profiles do the same for their own indexed content (see the “finding it automatically” section below). The manual method matters for understanding what the displayed number actually represents, and for computing it yourself from a citation export when a profile isn’t available or needs checking.
Worked example
Take a researcher with ten papers, with these total citation counts already sorted highest to lowest: 55, 30, 30, 18, 12, 7, 7, 3, 1, 0.
| Rank | Citations | Citations ≥ rank? |
|---|---|---|
| 1 | 55 | Yes |
| 2 | 30 | Yes |
| 3 | 30 | Yes |
| 4 | 18 | Yes |
| 5 | 12 | Yes |
| 6 | 7 | Yes (7 ≥ 6) |
| 7 | 7 | Yes (7 ≥ 7) |
| 8 | 3 | No (3 < 8) |
| 9 | 1 | No |
| 10 | 0 | No |
Rank 7 is the last position where the condition still holds (7 citations at rank 7), and rank 8 fails it (3 citations is less than 8). This researcher’s h-index is 7. Note the tied citation count at ranks 2 and 3 (30 citations each) doesn’t need special handling — ties simply occupy adjacent ranks in whichever order, and the count-versus-rank comparison works the same way regardless of which tied paper is listed first. This example uses illustrative numbers only, constructed to show the mechanics, not a real researcher’s publication record.
Why Google Scholar, Scopus, and Web of Science give different h-index values for the same person
It’s normal, not an error, for the same researcher to have three different h-index numbers depending on which database computed it. The underlying cause in all three cases is coverage scope — which documents each database indexes, and which of those documents’ citations it counts:
- Google Scholar is an automated web crawl with no published inclusion criteria: it indexes journal articles, conference papers, theses, preprints, books and book chapters, patents, and other grey literature, and counts citations from any of that indexed material, including non-peer-reviewed sources. This breadth means Google Scholar h-index values are typically the highest of the three for the same person — comparative bibliometric studies have found Google Scholar h-index figures running roughly 1.3–1.4x higher than the equivalent Web of Science figure on average, though the gap varies substantially by field and career stage.
- Scopus (Elsevier) is curated against published selection criteria by its Content Selection and Advisory Board (CSAB) and counts citations only from Scopus-indexed source documents — a narrower, vetted set than Google Scholar’s crawl but broader in journal count than Web of Science’s Core Collection in most subject areas. Scopus launched in November 2004, and its citation counting is naturally shallower for citations that predate its own indexed backfile in a given subject area, which can undercount total citations (and therefore h-index) for researchers with much older highly-cited work. Scopus h-index values tend to fall between Web of Science and Google Scholar, typically modestly above Web of Science for the same person.
- Web of Science (Clarivate) draws on the curated Core Collection — Science Citation Index Expanded, Social Sciences Citation Index, Arts & Humanities Citation Index, Emerging Sources Citation Index, plus the Conference Proceedings and Book Citation Indexes — each with its own editorial selection criteria and, in several of those indexes, a much longer backfile (the Science Citation Index’s coverage reaches back to 1945, Social Sciences Citation Index to 1956). Its citation counts only include citations made by other Web of Science–indexed documents, which is generally the narrowest of the three counting scopes for current researchers, and typically produces the lowest h-index of the three for the same person.
None of the three numbers is “wrong” — they’re each an accurate count within that database’s own coverage. This is exactly why responsible-assessment frameworks such as DORA and CoARA caution against citing an h-index without naming the source database and pull date, and against comparing h-index figures pulled from different databases as if they were the same measurement. See the h-index inflation entry for a related distortion — how large-team, hyperauthorship papers can inflate an h-index within any of the three databases.
Finding your h-index automatically instead of calculating it by hand
- Google Scholar Citations profile computes and displays h-index (and i10-index) automatically once a profile is set up and publications are claimed — see this site’s guide to creating and optimizing a Google Scholar profile.
- Scopus Author Details pages display an author-level h-index computed from Scopus-indexed documents, reachable once an author has a disambiguated Scopus Author ID — see how to create a Scopus Author ID.
- Web of Science author/Researcher Profile pages display the same figure computed from Core Collection content — see setting up a Web of Science researcher profile.
- Third-party tools such as Harzing’s Publish or Perish retrieve citation data (commonly from Google Scholar) and compute h-index and related variants without requiring a saved profile; useful for a one-off check or for a database that doesn’t offer a persistent author profile.
Calculation edge cases worth knowing about
- Self-citations are included by default. None of the three databases excludes a researcher’s citations of their own prior work from the base h-index calculation shown on a profile; some platforms offer a separate “excluding self-citations” view as an optional filter rather than changing the default number.
- Co-authored papers count in full toward every co-author’s individual h-index — the h-index does not divide credit by author count or authorship position, which is one reason it can be a poor comparator across fields with very different typical team sizes.
- Name and profile disambiguation matters more than the arithmetic. An incomplete or duplicate author profile (common with common surnames, name changes, or institutional-affiliation variants) will undercount publications before the h-index calculation even starts. A persistent identifier such as an ORCID iD, where linked into a database profile, reduces this risk.
- The h-index can only stay the same or increase over time within a fixed database and pull date — new citations can only add support to already-qualifying papers or push a paper past the next rank threshold, never remove a citation already counted. A displayed h-index can appear to drop only if the underlying database changes its indexed content (e.g., a paper or citing source is delisted) or if it’s compared across different pull dates using different coverage.
Frequently asked questions
Which database’s h-index should I report?
Report the number together with the source database and the date it was pulled (for example, “h-index of 14, Scopus, as of March 2026”) rather than a bare number. Institutions and funders that request an h-index in a CV or application will often specify which database they expect; when they don’t, naming the source avoids the figure being misread as directly comparable to a colleague’s number from a different database.
Is a higher h-index always better?
Not straightforwardly. The h-index is field-dependent (citation density varies enormously by discipline), career-stage-dependent (it can only grow, so it favors longer careers), and blind to authorship order or contribution. See what counts as a “good” h-index for how field and career stage change what a given number means, and DORA/CoARA guidance on why it should not be used as a standalone decision criterion.
Does the h-index count preprints?
Only if the database computing it indexes preprints. Google Scholar generally does; Scopus and Web of Science’s core indexing has historically focused on peer-reviewed content, though both have expanded preprint and early-access coverage over time — check the specific database’s current scope rather than assuming.
How is h-index different from i10-index?
The i10-index is a simpler count — the number of a researcher’s papers with at least 10 citations each — and is a Google Scholar–specific metric that Scopus and Web of Science don’t compute. See the h-index vs. i10-index comparison for the full breakdown, and impact factor vs. h-index for how an author-level metric differs from a journal-level one.
This guide covers calculation mechanics and database differences. For what the number means once you have it — typical ranges by field and career stage, and why assessment frameworks caution against using it as a standalone metric — see the h-index and good h-index dictionary entries.
