Author-level metrics

Formally, if f is the function that corresponds to the number of citations for each publication, the h-index is computed as follows. First, we order the values of f from the largest to the lowest value. Then, we look for the last position in which f is greater than or equal to the position (we call h this position). For example, if we have a researcher with 5 publications A, B, C, D, and E with 10, 8, 5, 4, and 3 citations, respectively, the h-index is equal to 4 because the 4th publication has 4 citations and the 5th has only 3. In contrast, if the same publications have 25, 8, 5, 3, and 3 citations, then the index is 3 because the fourth paper has only 3 citations.[1]

Author-level Eigenfactor is a version of Eigenfactor for single authors.[6] Eigenfactor regards authors as nodes in a network of citations. The score of an author according to this metric is his or her eigenvector centrality in the network.

It has been argued that "For an individual researcher, a measure such as Erdős number captures the structural properties of the network whereas the h-index captures the citation impact of the publications. One can be easily convinced that ranking in coauthorship networks should take into account both measures to generate a realistic and acceptable ranking." Several author ranking systems have been proposed already, for instance the Phys Author Rank Algorithm.[7]

The i-10 index indicates the number of academic publications an author has written that have been cited by at least 10 sources. It was introduced in July 2011 by Google as part of their work on Google Scholar.[8]

ResearchGate Score or RG Score is an author-level metric introduced by ResearchGate in 2012.[9] According to ResearchGate's CEO Dr. Ijad Madisch, “[t]he RG Score allows real-time feedback from the people who matter: the scientists themselves.”[10] RG Score has been reported to be correlated with existing author-level metrics and has an undisclosed calculation methodology.[11][12][13][14] Two studies reported that RG Score seems to incorporate the journal impact factors into the calculation.[13][14] The RG Score was found to be negatively correlated with network centrality – users that are the most active on ResearchGate usually do not have high RG scores.[15] It was also found to be strongly positively correlated with Quacquarelli Symonds university rankings at the institutional level, but only weakly with Elsevier SciVal rankings of individual authors.[16] While it was found to be correlated with different university rankings, the correlation in between these rankings themselves was higher.[11]

Field-weighted Citation Impact (FWCI) is an author-level metric introduced and applied by Scopus SciVal.[17] FWCI equals to the total citations actually received divided by the total citations that would be expected based on the average of the considered field. FWCI of 1 means that the output performs just as expected for the global average. More than 1 means that the author outperforms the average, and less than 1 means that the author underperforms. For instance, ${\displaystyle 1.55}$ means ${\displaystyle 55}$% more likely to be cited.[18][19]

The m-index is defined as h/n, where h is the h-index and n is the number of years since the first published paper of the scientist;[1] also called m-quotient.[20][21]

An individual h-index normalized by the number of authors has been proposed: ${\displaystyle h_{I}=h^{2}/N_{a}^{(T)}}$, with ${\displaystyle N_{a}^{(T)}}$ being the number of authors considered in the ${\displaystyle h}$ papers.[22] It was found that the distribution of the h-index, although it depends on the field, can be normalized by a simple rescaling factor. For example, assuming as standard the hs for biology, the distribution of h for mathematics collapse with it if this h is multiplied by three, that is, a mathematician with h = 3 is equivalent to a biologist with h = 9. This method has not been readily adopted, perhaps because of its complexity. It might be simpler to divide citation counts by the number of authors before ordering the papers and obtaining the h-index, as originally suggested by Hirsch.

Three additional metrics have been proposed: h² lower, h² center, and h² upper, to give a more accurate representation of the distribution shape. The three h² metrics measure the relative area within a scientist's citation distribution in the low impact area, h² lower, the area captured by the h-index, h² center, and the area from publications with the highest visibility, h² upper. Scientists with high h² upper percentages are perfectionists, whereas scientists with high h² lower percentages are mass producers. As these metrics are percentages, they are intended to give a qualitative description to supplement the quantitative h-index.[23]

For g-index is introduced in 2006 as largest number of top ${\displaystyle g}$ articles, which have received together at least ${\displaystyle g^{2}}$ citations.[24]

The e-index, the square root of surplus citations for the h-set beyond h², complements the h-index for ignored citations, and therefore is especially useful for highly cited scientists and for comparing those with the same h-index (iso-h-index group).[25][26]

The c-index accounts not only for the citations but for the quality of the citations in terms of the collaboration distance between citing and cited authors. A scientist has c-index n if n of [his/her] N citations are from authors which are at collaboration distance at least n, and the other (N − n) citations are from authors which are at collaboration distance at most n.[27]

The o-index corresponds to the geometric mean of the h-index and the most cited paper of a researcher.[28]

The h-index has been shown to have a strong discipline bias. However, a simple normalization ${\displaystyle h/\langle h\rangle _{d}}$ by the average h of scholars in a discipline d is an effective way to mitigate this bias, obtaining a universal impact metric that allows comparison of scholars across different disciplines.[29]

The RA-index accommodates improving the sensitivity of the h-index on the number of highly cited papers and has many cited paper and uncited paper under the h-core. This improvement can enhance the measurement sensitivity of the h-index. [30]

L-index combines the number of citations, the number of coauthors, the age of publications into a single value, which is independent of the number of publications and conveniently ranges from 0.0 to 9.9.[31] With c as number of citations, a as number of authors and y as number of years, L-index is defined by the formula:

${\displaystyle L=\ln \left({\sum _{i}{\frac {c_{i}}{a_{i}y_{i}}}}\right)+1}$

L-index is automatically calculated by the Exaly database.[32]

An s-index, accounting for the non-entropic distribution of citations, has been proposed and it has been shown to be in a very good correlation with h.[33]

w-index is defined as follow: if w of a researcher's papers have at least ${\displaystyle 10w}$ citations each and the other papers have fewer than ${\displaystyle 10(w+1)}$ citations, that researcher's w‐index is w.[34]

Author Impact Factor (AIF) is the Impact Factor applied to authors.[5] The AIF of an author ${\displaystyle X}$ in year ${\displaystyle y}$ is the mean number of citations given by papers published in year ${\displaystyle y}$ to papers published by ${\displaystyle X}$ in a period of ${\displaystyle \Delta y}$ years before year ${\displaystyle y}$. Unlike the h-index, AIF is able to capture trends and variations of the impact of the scientific output of scientists over time, which is a growing measure taking into account the whole career path.

There are a number of models proposed to incorporate the relative contribution of each author to a paper, for instance by accounting for the rank in the sequence of authors.[35] A generalization of the h-index and some other indices that gives additional information about the shape of the author's citation function (heavy-tailed, flat/peaked, etc.) has been proposed.[36] Because the h-index was never meant to measure future publication success, recently, a group of researchers has investigated the features that are most predictive of future h-index. It is possible to try the predictions using an online tool.[37] However, later work has shown that since h-index is a cumulative measure, it contains intrinsic auto-correlation that led to significant overestimation of its predictability. Thus, the true predictability of future h-index is much lower compared to what has been claimed before.[38] The h-index can be timed to analyze its evolution during one's career, employing different time windows.[39]