Good H-Index

There is no single number that counts as a “good” h-index across the board. The question is only answerable once two things are fixed: field (citation density and publishing pace vary enormously by discipline) and career stage (h-index can only grow, so it mechanically rewards seniority over quality). An h-index that is unremarkable in cell biology can be exceptional in pure mathematics; an h-index that looks modest for a full professor can be outstanding for a fourth-year PhD student. Any claim that gives a bare number as “good” without naming both a field and a career stage is not measuring what it appears to measure.

Why the raw number can’t be judged on its own

The h-index is defined as the largest number h such that a researcher has h papers each cited at least h times. That construction bakes in three sources of variation that make cross-context comparison unsound:

• Field-dependence. Citation practices differ by discipline — average reference-list length, typical time-to-citation, and the size of the citing community are not comparable between, say, biomedicine and mathematics. Jorge Hirsch acknowledged this in the paper that introduced the metric, noting there will be “differences in typical h values in different fields, determined in part by the average number of references in a paper in the field,” and that h values in the life sciences run much higher than in physics (Hirsch, PNAS 102(46):16569–16572, 2005).
• Career-stage (time) dependence. Because h-index can only increase or stay flat as a career progresses, it partly measures how long someone has been publishing, not just how good the work is. Two researchers with identical h-index values 10 years apart in career length are not equivalently accomplished.
• Database-dependence. Google Scholar, Scopus and Web of Science index different content and different citation windows, so the same researcher can show three different h-index values depending on which database produced it. A number is not comparable to a “benchmark” figure computed from a different source.

These are also exactly the three limitations already documented on CASRAI’s h-index entry, which this page builds on rather than repeats.

Hirsch’s own field-specific benchmark — and why it doesn’t generalize

The closest thing to an authoritative “good h-index” number comes from Hirsch’s original 2005 paper itself, and it is narrower than it is usually quoted as being. Working from typical values he observed among physicists, Hirsch proposed a rough scale: an h-index of about 20 after 20 years of active research characterizes a “successful scientist”; about 40 after 20 years characterizes “outstanding scientists, likely to be found only at the top universities”; and about 60 after 20 years (or 90 after 30 years) characterizes “truly unique individuals” (Hirsch, PNAS 2005).

Two caveats matter more than the numbers themselves:

• Hirsch derived these figures specifically from physics publication and citation patterns and said so explicitly — he did not offer them as a cross-field standard, and noted that further research would be needed to understand how h-index distributions differ across other fields.
• The figures are two decades old. Publication volume, co-authorship norms, and citation databases have all changed since 2005; treat the specific numbers as a historical illustration of how field-specific a “good” h-index claim has to be, not as a current threshold to apply to anyone’s CV.

Reproducing a “good h-index by field” table extrapolated from this one dated, single-field data point — something a number of researcher-facing blogs do — overstates what the original source actually supports. CASRAI is not publishing one here for that reason.

The m-quotient: Hirsch’s own attempt at career-stage normalization

In the same paper, Hirsch proposed a second metric specifically to correct for the time-dependence problem: the m-quotient, defined as the h-index divided by the number of years since a researcher’s first published paper (m = h / n). Because raw h-index only grows, Hirsch intended m as “a useful yardstick to compare scientists of different seniority” — an early-career researcher with h = 10 after 5 years (m = 2) and a senior researcher with h = 40 after 20 years (m = 2) are, on this measure, progressing at a comparable rate, despite a fourfold difference in raw h-index.

The m-quotient inherits h-index’s field-dependence (it normalizes for time, not discipline) and is unstable early in a career, where small changes in h produce large swings in m. It is a partial fix for one of the three problems above, not a solution to all of them, and it is far less commonly reported than the raw h-index itself.

What responsible-assessment frameworks actually say

Several widely adopted frameworks address single-number research metrics generally, and h-index specifically falls under their scope even where it isn’t named:

• The Leiden Manifesto (Hicks et al., Nature 520:429–431, 2015) states as one of its ten principles that assessment should “account for variation by field in publication and citation practices,” and separately warns against “misplaced concreteness and false precision” — directly applicable to treating an h-index difference of one or two points as meaningful.
• DORA (the San Francisco Declaration on Research Assessment) is framed principally around journal-level metrics such as the Journal Impact Factor; its core recommendation not to use a journal-based metric “as a surrogate measure of the quality of individual research articles, to assess an individual scientist’s contributions, or in hiring, promotion, or funding decisions” mentions h-index only once, in passing, as one of several alternative journal-level indicators. DORA’s general caution against reducing research quality to a single number is nonetheless the principle that subsequent responsible-metrics guidance (below) extends explicitly to researcher-level indices like h-index.
• The CoARA Agreement and the Hong Kong Principles both commit signing institutions to basing assessment on qualitative expert judgement of a researcher’s actual contributions rather than on standalone bibliometric thresholds.

None of these frameworks publish a numeric “good h-index” figure, by field or otherwise — that omission is itself part of their point: a defensible research-assessment process does not reduce to a lookup table.

What to do instead of looking for a number

• Compare within field and career stage, not across them. If a comparison is needed at all, benchmark against researchers in the same or an adjacent subfield with a similar number of years since their first publication or since PhD completion — not against a discipline-wide or cross-field figure.
• Hold the database constant. Don’t compare an h-index pulled from Google Scholar against one pulled from Scopus or Web of Science; state which database and date the figure came from.
• Use it as one input among several, not a threshold. Per the Leiden Manifesto and CoARA, quantitative indicators should support, not replace, qualitative review of the actual body of work — the papers themselves, their contribution, and context such as career breaks, part-time research roles, or a switch between subfields.
• Be skeptical of any “good h-index by field/career stage” table you encounter, including informal ones from other websites, unless it discloses its data source, database, sample and date. Absent that disclosure, the number is not verifiable and is not more reliable than an average of anecdote.

References

• Hirsch JE. “An index to quantify an individual’s scientific research output.” PNAS 102(46):16569–16572, 2005.
• Hicks D, Wouters P, Waltman L, de Rijcke S, Rafols I. “Bibliometrics: The Leiden Manifesto for research metrics.” Nature 520:429–431, 2015 (doi:10.1038/520429a).
• San Francisco Declaration on Research Assessment (DORA), sfdora.org.
• Coalition for Advancing Research Assessment (CoARA) Agreement, coara.eu.
• Hong Kong Principles for Assessing Researchers, 2020.