You’ve looked up your h-index and now you’re staring at a number with no idea whether it’s good, bad, or average for someone at your career stage. This page gives you the benchmarks, by career stage and by field, plus the context you need to interpret what your score actually means.
The h-index is one of the most widely used single-number measures of a researcher’s publication impact. It combines how many papers you’ve published with how often those papers get cited. If you have an h-index of 20, you have 20 papers that have each been cited at least 20 times.
Simple metric, but the number means nothing on its own. A molecular biologist with an h-index of 25 and a mathematician with an h-index of 10 may be performing at the same level — citation rates in the life sciences are typically much higher than in pure mathematics, often by several times over. And a postdoc with an h-index of 6 might be outperforming a full professor with an h-index of 15 when you factor in career length. Everything here is built around helping you read your number in context, not just know what it is.
An h-index is only meaningful when stated with three things: the platform it came from, your career stage, and your field. Strip any one of those and the number becomes uninterpretable. That’s the lens this article is built around.
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How the H-Index Works
Jorge E. Hirsch of UCSD proposed the h-index in a 2005 PNAS paper as a way to measure and compare the overall scientific productivity of individual researchers. [1]
The calculation is straightforward. Rank your publications by citation count, highest first. Your h-index is the largest number h such that h of your papers have each been cited at least h times — the point where the citation count still meets or exceeds the rank. If you have an h-index of 10, you have 10 papers each cited at least 10 times, and the paper ranked 11th has fewer than 11 citations.
H-Index Calculator
Enter your papers’ citation counts to calculate your h-index — plus i10, g-index and total citations — then see how it measures up for your career stage.
- It doesn’t account for the number of authors on a paper (the h-frac index addresses this).
- It penalises early-career researchers (the m-quotient = h ÷ years since first publication adjusts for this).
- It can’t fairly compare researchers across disciplines.
- Google Scholar, Scopus and Web of Science give different values — this tool works off whatever data you enter.
One thing worth knowing: each increment gets harder. Going from h=10 to h=11 means you need 11 papers with at least 11 citations each. The higher your h-index, the more publications and citations you need to push it up by one more point.
What is a Good H-Index?
Hirsch himself offered benchmarks: after 20 years of active research, an h-index of 20 is good, 40 is outstanding, and 60 is truly exceptional. [1]
Those numbers are a useful starting point, but they are field-agnostic and assume two decades of publishing. If you are five years post-PhD or you work in a low-citation discipline, those thresholds will mislead you. The tables below break it down by career stage and by field.
H-Index Benchmarks by Career Stage
These are indicative ranges, not precise thresholds. Your field will shift them substantially — see the next section.
| Career stage | Typical h-index range | Context |
|---|---|---|
| PhD student (end of programme) | 1–5 | Publication output varies considerably by country, discipline, and lab type; citations take time to accumulate after publication |
| Postdoc (2–4 years post-PhD) | 3–8 | 5–8 is strong in high-citation fields like biomedicine; 3–5 is normal elsewhere |
| Assistant professor / Lecturer | 5–15 | 10–15 is solid in biochemistry or clinical medicine; 5–8 can be excellent in humanities |
| Associate professor | 10–25 | Wide range; field and institution type matter more than the raw number here |
| Full professor | 15–60+ | 15–20 is distinguished in mathematics; 30–60 is typical in biomedical sciences |
Orientation ranges only. These are editorial estimates based on patterns observed across the bibliometric literature. No single peer-reviewed study covers all career stages and disciplines in this form — these ranges reflect general tendencies, not authoritative thresholds. Your field will shift them significantly.
The spread at senior levels is enormous. It reflects genuine differences in citation culture between disciplines, not differences in research quality. A full professor in a low-citation discipline and one in biomedical sciences can have very different h-indexes and both be performing strongly relative to their peers.
H-Index Benchmarks by Field
Approximate ranges for full professors with 15 or more years of active publishing. Exact figures vary by country, institution, and subfield — use as orientation, not a pass/fail line.
| Field | Approximate h-index range (full professor) | Why the range differs |
|---|---|---|
| Biomedical sciences | 30–60 | Large research communities, high citation rates, multi-author papers common |
| Clinical medicine | 25–55 | Clinical trials generate high citation volumes; collaborative authorship standard |
| Physics and chemistry | 20–45 | Established citation culture; large collaborations in some subfields |
| Engineering and computer science | 20–40 | Conference papers important in CS but poorly indexed by some databases |
| Social sciences and economics | 10–25 | Smaller citation pools; single-author and dual-author papers more common |
| Mathematics | 8–20 | Small community size, slow citation accumulation, single-author tradition |
| Humanities | 5–15 | Books matter more than articles; citation databases capture a fraction of impact |
Orientation ranges only. Editorial estimates synthesised from the bibliometric literature. No single peer-reviewed source provides cross-field h-index benchmarks in this form — figures vary considerably by country, institution, subfield, and platform. Use as a rough orientation only.
Always state which platform your h-index comes from. Older database-comparison studies — particularly in library and information science — found Google Scholar and Scopus often produce higher h-index values than Web of Science for the same researcher; the size of the gap varies by field, profile hygiene, and database coverage. [2] Comparing numbers from different platforms without flagging this is a common source of misunderstanding.
Putting the H-Index in Scale
Hirsch’s original 2005 paper documented that 84% of Nobel Prize winners in physics had an h-index of at least 30. [1] Among the top 10 most-cited scientists in life sciences over the period 1983–2002, h-indexes ranged from 120 to 191. Among new inductees to the US National Academy of Sciences in biological and biomedical sciences in 2005, the median h-index was 57. [1]
These numbers are not benchmarks for most researchers — they represent decades of highly-cited output at the very top of some of the most productive fields in science. The useful comparison is within your field and career stage, which the tables above are built to support.
The counterpoint is equally instructive: in his original paper, Hirsch estimated that Albert Einstein would have had an h-index of only 4 or 5 based on his publications up to early 1906, despite already being widely recognised as one of the most original thinkers in physics. [1] The h-index rewards sustained volume over time. A handful of revolutionary papers will not register.
Where to Check Your H-Index
You will get a different h-index depending on which platform you use. This is not an error. Each database indexes a different slice of the literature.
| Platform | Access | What it indexes | Relative h-index |
|---|---|---|---|
| Google Scholar | Free | Journals, conferences, preprints, books, theses, patents — broadest coverage | Typically highest |
| Scopus | Institutional subscription | Journals, conferences, and books across physical, health, life, and social sciences; coverage changes over time — check Elsevier’s current figures | Mid-range |
| Web of Science | Institutional subscription | Journals, conferences, and books; strongest historical depth (from 1900); selective indexing criteria; coverage changes — check Clarivate’s current figures | Typically lowest |
| ResearchGate | Free (with profile) | Publications on your profile; useful for tracking your own output but not a primary platform for formally reporting your h-index | Varies; depends on profile completeness |
If you work in a conference-heavy field like computer science, your Web of Science h-index may significantly understate your impact. Top-tier CS conference papers (NeurIPS, ICML, CVPR) carry substantial weight in the field. Both Web of Science and Scopus index some conference material, but coverage varies by database, venue, and proceedings type — neither captures all conferences comprehensively. Google Scholar’s broader indexing typically picks up more.
How to Find Your H-Index on Google Scholar (Free)
Google Scholar is the fastest free option. If you already have a Google Scholar profile, your h-index is displayed on your profile page alongside your i-10 index and total citations. If you don’t have a profile yet:
- Go to scholar.google.com and click “My profile”. Sign in with your Google account and add your name, affiliation, and research keywords.
- Verify your publications. Google Scholar will auto-suggest papers it thinks are yours. Add the correct ones and remove any that belong to someone else with your name.
- Read your metrics. Your h-index, i-10 index, and total citations appear in the right-hand panel. Google Scholar also shows “since [year]” versions of your citations, h-index, and i10-index on author profiles, letting you see your more recent impact separately.
On Scopus, search for your name under “Author search”, select your profile, and click the “Metrics” tab. On Web of Science, run an author search, select your publications, and click “Create Citation Report” to see your h-index calculated from their database only.
For a quick check without setting up a profile, Publish or Perish uses Google Scholar data and calculates h-index plus several alternative metrics. No institutional subscription required.
What the H-Index Doesn’t Tell You
It structurally penalises researchers who haven’t been publishing for long. The h-index can only grow as papers accumulate citations over years. A brilliant postdoc with three groundbreaking papers cannot have an h-index above 3, no matter how many times those papers are cited. Career breaks — parental leave, illness, career transitions — compound this: the metric does not distinguish between “stopped publishing” and “took time out for valid reasons.” This is not a flaw you can work around; it’s built into how the index is defined.
Early-career h-indexes often say more about the lab than the individual. A postdoc’s citation count is heavily shaped by their PI’s reputation, the institution’s visibility, and the size of the research group. Two researchers with identical ability can have very different h-indexes based purely on where they trained.
It no longer tracks scientific awards the way it used to. A 2021 study in PLoS One found that the h-index no longer correlates well with the number of awards a researcher receives, partly because the average number of authors per paper has increased. [3]
Self-citation can inflate the number. Some researchers strategically cite their own previous work to push borderline papers above the h-threshold. Some platforms and analysis workflows let you exclude self-citations — check whether your reported h-index includes or excludes them, and report consistently.
Beyond the H-Index: Alternative Metrics
No single number captures a researcher’s full contribution. Several alternatives address specific weaknesses of the h-index. Here’s when each one is worth looking at.
| Metric | What it does | When to use it | Limitation |
|---|---|---|---|
| i-10 index | Number of publications with at least 10 citations | Quick productivity check; available on Google Scholar | Low threshold; less useful for senior researchers |
| M-quotient (m-value) | H-index divided by years since first publication | Comparing researchers at different career stages; corrects for career length | Penalises career breaks; rewards early starters |
| h-frac index | Citation counts divided by number of authors before calculating h | When author count matters — fields with hyperauthorship | Could theoretically incentivise leaving junior authors off papers [3] |
| G-index | Largest number n of highly cited articles for which the average citations is at least n | When a few highly cited papers should count more | Dominated by outlier papers; harder to interpret [4] |
In many research institutions, particularly in STEM fields, the h-index is a key bibliometric figure in hiring, promotion, and grant decisions. [3] Whether or not that’s the right approach, it remains difficult to ignore. If you want a fairer picture alongside it — especially as an early-career researcher — report the m-quotient too. It takes ten seconds: divide your h-index by the number of years since your first publication.
Common Mistakes When Interpreting the H-Index
| Mistake | Why it’s wrong | What to do instead |
|---|---|---|
| Comparing h-indexes from different platforms | Database comparison studies consistently find Google Scholar produces higher h-indexes than Web of Science for the same researcher, often by a substantial margin [2] | Always compare like with like — same platform, same date |
| Comparing across disciplines without adjusting | Citation rates vary enormously between fields — what reads as a low h-index in biology may represent strong performance in mathematics or the humanities | Use field-specific benchmarks, not a universal “good” number |
| Judging early-career researchers by h-index alone | The metric structurally favours long careers; it says nothing about quality of recent work | Use the m-quotient (h-index ÷ years active) for fairer comparison |
| Ignoring self-citation | Strategic self-citation can push borderline papers above the h-threshold; the effect compounds over time | Check whether your platform’s h-index includes or excludes self-citations; some platforms offer both views |
| Treating h-index as the whole picture | It misses breakthrough papers (one paper cited 5,000 times gives you h=1 on its own), mentoring impact, and non-journal contributions | Use h-index as one data point alongside the i-10, total citations, and qualitative assessment |
How to Report Your H-Index Properly
Whenever you report your h-index — on a CV, in a grant, or in a hiring conversation — state three things alongside the number: the platform you used, your career stage, and your field. Without those three pieces of context, the number alone tells your reader very little, and invites exactly the kind of cross-platform and cross-discipline comparison that makes the h-index misleading.
For example: “h-index of 14 on Google Scholar, 5 years post-PhD, computational biology” is a meaningful statement. “h-index of 14” is not.
The h-index is not going away. Flawed as it is, it remains widely used in hiring, promotion, and funding decisions. [3] Know yours, understand its limits, and give it enough context to be read honestly.
References
- Hirsch JE. (2005) An index to quantify an individual’s scientific research output. PNAS 102(46):16569–72
- Meho LI, Yang K. (2007) Impact of data sources on citation counts and rankings of LIS faculty: Web of Science versus Scopus and Google Scholar. JASIST 58(13):2105–25
- Koltun V, Hafner D. (2021) The h-index is no longer an effective correlate of scientific reputation. PLoS One. 16(6):e0253397
- Egghe L. (2006) Theory and practise of the g-index. Scientometrics 69(1):131–152
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