Compara mètodes

	e-Index (Excess Citations)	i10-Index
Camp	Bibliometria	Bibliometria
Família	Process / pipeline	Process / pipeline
Any d'origen	2009	2011
Autor original	Chun-Ting Zhang	Google Scholar (Google Scholar Citations / My Citations)
Tipus	Author-level excess-citation impact index	Author-level productivity count of well-cited papers
Font seminal	Zhang, C.-T. (2009). The e-index, complementing the h-index for excess citations. PLoS ONE, 4(5), e5429.	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.
Àlies	Zhang e-index, excess citation index, e index	Google Scholar i10-index, i10 index, ten-citation index
Relacionats	3	3
Resum	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 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.