Σύγκριση μεθόδων

	i10-Index	e-Index (Excess Citations)	g-Index (Egghe)	m-Quotient (Hirsch m)
Πεδίο	Βιβλιομετρία	Βιβλιομετρία	Βιβλιομετρία	Βιβλιομετρία
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	2011	2009	2006	2005
Δημιουργός	Google Scholar (Google Scholar Citations / My Citations)	Chun-Ting Zhang	Leo Egghe	Jorge E. Hirsch
Τύπος	Author-level productivity count of well-cited papers	Author-level excess-citation impact index	Author-level cumulative-citation impact index	Career-length-normalized author impact rate
Θεμελιώδης πηγή	Cornell University Library. Measuring Your Research Impact: i10-Index. Defines the i10-index as the number of publications with at least 10 citations, created and used by Google Scholar.	Zhang, C.-T. (2009). The e-index, complementing the h-index for excess citations. PLoS ONE, 4(5), e5429.	Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152.	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.
Εναλλακτικές ονομασίες	Google Scholar i10-index, i10 index, ten-citation index	Zhang e-index, excess citation index, e index	Egghe g-index, Egghe index, g index	Hirsch m-quotient, m-parameter, h-index per year
Συναφείς	3	3	3	3
Σύνοψη	The i10-index is a deliberately simple author-level metric introduced by Google Scholar in 2011 for its Scholar Citations profiles. It counts the number of a researcher's publications that have each accumulated at least ten citations. Unlike the h-index, whose threshold depends on the rank of the paper, the i10-index applies a single fixed cutoff, making it transparent and trivial to compute. Its appeal lies in this simplicity and in its native availability on every Google Scholar profile, though it is used almost exclusively within the Google Scholar ecosystem and offers less discriminating power than rank-based indices.	The e-index, proposed by Chun-Ting Zhang in 2009, isolates the citations that the h-index throws away. Inside the h-core of an author's h most-cited papers, the h-index implicitly credits each paper with only h citations and discards everything above that, even though top papers may have far more. The e-index recovers exactly this surplus: it is the square root of the difference between the total citations of the h-core and the h-squared citations that the h-index already accounts for. Zhang designed it as a complement rather than a replacement for the h-index, so that the pair (h, e) together describe both the size of an author's productive core and the concentration of excess impact within it.	The g-index, introduced by Leo Egghe in 2006, is an author-level bibliometric indicator designed to repair a structural weakness of Hirsch's h-index: its insensitivity to the size of the most-cited papers. Where the h-index caps the credit any single paper can earn at h, the g-index lets exceptionally cited articles raise an author's score. It is defined as the largest number g such that the g most-cited papers together accumulate at least g-squared citations. Because it rests on cumulative rather than per-paper citation counts, the g-index always equals or exceeds the h-index and rewards researchers whose impact is concentrated in a few landmark works as well as those with broad, steady output.	The m-quotient, defined by Jorge Hirsch in the same 2005 paper that introduced the h-index, normalizes an author's h-index by the length of their scientific career. Because the h-index can only grow over time and never decreases, raw h-values systematically favor senior researchers and make it unfair to compare early-career scientists with established ones. The m-quotient divides the h-index by the number of years since the researcher's first publication, yielding a rate of impact accumulation per year. Hirsch proposed rough benchmarks on this scale, suggesting that a sustained value near 1 characterizes a successful scientist, near 2 an outstanding one, and near 3 a truly exceptional figure, making the m-quotient a tool for comparing researchers at different career stages.