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'''Author-level metrics''' are [[citation metrics]] that measure the [[bibliometrics|bibliometric impact]] of individual authors, researchers, academics, and scholars. A prime example is the [[h-index|''h''-index]]. Other metrics originally developed for [[academic journal]]s can be reported at researcher level, such as the author-level [[eigenfactor]]<ref name="wiley">{{cite journal |title=Author-level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community |first1=Jevin D. |last1=West |first2=Michael C. |last2=Jensen |first3=Ralph J. |last3=Dandrea |first4=Gregory J. |last4=Gordon |first5=Carl T. |last5=Bergstrom |journal=[[Journal of the American Society for Information Science and Technology]] |year=2013 |doi=10.1002/asi.22790 |volume=64 |issue=4 |pages=787–801}}</ref> and the author [[impact factor]].<ref name="nature">{{cite journal |title=Author Impact Factor: Tracking the dynamics of individual scientific impact |journal=[[Scientific Reports]] |doi=10.1038/srep04880 |first1=Raj Kumar |last1=Pan |first2=Santo |last2=Fortunato |year=2014 |volume=4 |page=4880|arxiv=1312.2650 }}</ref> [[Jorge E. Hirsch]] invented and suggested h-index as a ''"useful yardstick with which to compare, in an unbiased way, different individuals competing for the same resource when an important evaluation criterion is scientific achievement."''<ref>{{cite journal |last1=Hirsch |first1=J. E. |title=An index to quantify an individual's scientific research output |journal=Proceedings of the National Academy of Sciences |date=7 November 2005 |volume=102 |issue=46 |pages=16569–16572 |doi=10.1073/pnas.0507655102}}</ref>
'''Author-level metrics''' are [[citation metrics]] that measure the [[bibliometrics|bibliometric impact]] of individual authors, researchers, academics, and scholars. A prime example is the [[h-index|''h''-index]]. Other metrics originally developed for [[academic journal]]s can be reported at researcher level, such as the author-level [[eigenfactor]]<ref name="wiley">{{cite journal |title=Author-level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community |first1=Jevin D. |last1=West |first2=Michael C. |last2=Jensen |first3=Ralph J. |last3=Dandrea |first4=Gregory J. |last4=Gordon |first5=Carl T. |last5=Bergstrom |journal=[[Journal of the American Society for Information Science and Technology]] |year=2013 |doi=10.1002/asi.22790 |volume=64 |issue=4 |pages=787–801}}</ref> and the author [[impact factor]].<ref name="nature">{{cite journal |title=Author Impact Factor: Tracking the dynamics of individual scientific impact |journal=[[Scientific Reports]] |doi=10.1038/srep04880 |first1=Raj Kumar |last1=Pan |first2=Santo |last2=Fortunato |year=2014 |volume=4 |page=4880|arxiv=1312.2650 }}</ref> [[Jorge E. Hirsch]] invented and suggested h-index as a ''"useful yardstick with which to compare, in an unbiased way, different individuals competing for the same resource when an important evaluation criterion is scientific achievement."''<ref name="hirsch">{{cite journal |last1=Hirsch |first1=J. E. |title=An index to quantify an individual's scientific research output |journal=Proceedings of the National Academy of Sciences |date=7 November 2005 |volume=102 |issue=46 |pages=16569–16572 |doi=10.1073/pnas.0507655102}}</ref>

==List of metrics==

* Formally, if ''f'' is the function that corresponds to the number of citations for each publication, we compute the ''h''-index 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.<ref name="hirsch"/>
* An individual ''h''-index normalized by the number of authors has been proposed: <math>h_I = h^2/N_a^{(T)}</math>, with <math>N_a^{(T)}</math> being the number of authors considered in the <math>h</math> papers.<ref name=BatistaEtal2006>{{Cite journal |author=Batista P. D. |title=Is it possible to compare researchers with different scientific interests? |journal=[[Scientometrics (journal)|Scientometrics]] |volume=68 |issue=1 |year=2006 |pages=179–89 |doi=10.1007/s11192-006-0090-4 |display-authors=1 |last2=Campiteli |first2=Mônica G. |last3=Kinouchi |first3=Osame }}</ref> 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 ''h''s for biology, the distribution of ''h'' for mathematics collapse with it if this ''h'' is multiplied by three, that is, a mathematician with ''h''&nbsp;=&nbsp;3 is equivalent to a biologist with ''h''&nbsp;=&nbsp;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.
* The ''m''-index is defined as ''h''/''n'', where ''n'' is the number of years since the first published paper of the scientist;<ref name="hirsch"/> also called ''m''-quotient.<ref>{{cite web |url=http://www.harzing.com/pop_hindex.htm |title=Reflections on the ''h''-index |author=Anne-Wil Harzing |date=2008-04-23 |website= |accessdate=2013-07-18}}</ref><ref>{{cite journal |pmid=21507617 |year=2011 |author1=von Bohlen und Halbach O |title=How to judge a book by its cover? How useful are bibliometric indices for the evaluation of "scientific quality" or "scientific productivity"? |volume=193 |issue=3 |pages=191–96 |doi=10.1016/j.aanat.2011.03.011 |journal=[[Annals of Anatomy]]}}</ref>
* 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.<ref>{{cite journal | last1 = Tscharntke | first1 = T. | last2 = Hochberg | first2 = M. E. | last3 = Rand | first3 = T. A. | last4 = Resh | first4 = V. H. | last5 = Krauss | first5 = J. | year = 2007 | title = Author Sequence and Credit for Contributions in Multiauthored Publications | url = | journal = PLoS Biology | volume = 5 | issue = 1| page = e18 | doi = 10.1371/journal.pbio.0050018 |pmc=1769438 | pmid=17227141}}</ref>
* 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.<ref>{{cite journal |last=Gągolewski |first=M. |author2=Grzegorzewski, P. |year=2009 |title=A geometric approach to the construction of scientific impact indices |journal=Scientometrics |volume=81 |issue=3 |pages=617–34 |doi=10.1007/s11192-008-2253-y }}</ref>
* Three additional metrics have been proposed: ''h''<sup>2</sup> lower, ''h''<sup>2</sup> center, and ''h''<sup>2</sup> upper, to give a more accurate representation of the distribution shape. The three ''h''<sup>2</sup> metrics measure the relative area within a scientist's citation distribution in the low impact area, ''h''<sup>2</sup> lower, the area captured by the ''h''-index, ''h''<sup>2</sup> center, and the area from publications with the highest visibility, ''h''<sup>2</sup> upper. Scientists with high ''h''<sup>2</sup> upper percentages are perfectionists, whereas scientists with high ''h''<sup>2</sup> lower percentages are mass producers. As these metrics are percentages, they are intended to give a qualitative description to supplement the quantitative ''h''-index.<ref>{{cite journal |doi=10.1016/j.joi.2010.03.005 |title=The ''h'' index research output measurement: Two approaches to enhance its accuracy |year=2010 |last1=Bornmann |first1=Lutz |last2=Mutz |first2=Rüdiger |last3=Daniel |first3=Hans-Dieter |journal=Journal of Informetrics |volume=4 |issue=3 |pages=407–14 }}</ref>
* The [[g-index|''g''-index]] can be seen as the ''h''-index for an averaged citations count.<ref>{{cite journal |doi=10.1007/s11192-006-0144-7 |title=Theory and practise of the ''g''-index |year=2013 |last1=Egghe |first1=Leo |journal=Scientometrics |volume=69 |pages=131–52|hdl=1942/981 |url=https://uhdspace.uhasselt.be/dspace/bitstream/1942/981/1/theory%20%26%20practice.pdf }}</ref>
* It has been argued that "For an individual researcher, a measure such as [[Erdős number]] captures the structural properties of 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 such as [[eigenfactor]] (based on [[eigenvector centrality]]) have been proposed already, for instance the Phys Author Rank Algorithm.<ref>{{cite web |author1=Kashyap Dixit |author2=S Kameshwaran |author3=Sameep Mehta |author4=Vinayaka Pandit |author5=N Viswanadham |url=http://domino.research.ibm.com/library/cyberdig.nsf/papers/2B600A90C54E51B18525755800283D37/$File/RR_ranking.pdf |title=Towards simultaneously exploiting structure and outcomes in interaction networks for node ranking |website=IBM Research Report R109002 |date=February 2009}}; see also {{cite conference |doi=10.1145/1871437.1871470 |title=Outcome aware ranking in interaction networks |booktitle=Proceedings of the 19th ACM international conference on Information and knowledge management – CIKM '10 |year=2010 |last1=Kameshwaran |first1=Sampath |last2=Pandit |first2=Vinayaka |last3=Mehta |first3=Sameep |last4=Viswanadham |first4=Nukala |last5=Dixit |first5=Kashyap |isbn=9781450300995 |page=229}}</ref>
* 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''.<ref>{{cite journal |author1=Bras-Amorós, M. |author2=Domingo-Ferrer, J. |author3=Torra, V | year = 2011 | title = A bibliometric index based on the collaboration distance between cited and citing authors | journal = Journal of Informetrics | volume = 5 | issue = 2 | pages = 248–64 | doi = 10.1016/j.joi.2010.11.001 |hdl=10261/138172 }}</ref>
* 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''.<ref>{{cite journal |last1=Silagadze |first1=Z. K. |title=Citation entropy and research impact estimation |year= 2010|pages=2325–33 |volume=41 |issue=2010 |journal=Acta Phys. Pol. B |arxiv=0905.1039 |bibcode=2009arXiv0905.1039S}}</ref>
* The ''e''-index, the square root of surplus citations for the ''h''-set beyond ''h''<sup>2</sup>, 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).<ref>{{cite journal |doi=10.1371/journal.pone.0005429 |title=The e-Index, Complementing the ''h''-Index for Excess Citations |year=2009 |editor1-last=Joly |editor1-first=Etienne |last1=Zhang |first1=Chun-Ting |journal=PLoS ONE |volume=4 |issue=5 |page=e5429 |pmid=19415119 |pmc=2673580|bibcode=2009PLoSO...4.5429Z }}</ref><ref>{{cite journal |doi=10.1016/j.bbrc.2009.07.091 |title=Citation analysis: Maintenance of ''h''-index and use of e-index |year=2009 |last1=Dodson |first1=M.V. |journal=Biochemical and Biophysical Research Communications |volume=387 |issue=4 |pages=625–26 |pmid=19632203}}</ref>
* 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.<ref>{{cite journal |doi=10.1038/489201a |title=Future impact: Predicting scientific success |year=2012 |last1=Acuna |first1=Daniel E. |last2=Allesina |first2=Stefano |last3=Kording |first3=Konrad P. |authorlink3=Konrad Kording|journal=Nature |volume=489 |issue=7415 |pages=201–02 |pmid=22972278 |pmc=3770471|bibcode=2012Natur.489..201A }}</ref> 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.<ref>{{cite journal |doi=10.1038/srep03052 |title=On the Predictability of Future Impact in Science|year=2013 |last1=Penner |first1=Orion |last2=Pan |first2=Raj K. |last3=Petersen | first3=Alexander M. |last4=Kaski | first4=Kimmo | last5= Fortunato | first5=Santo |journal=Scientific Reports |volume=3 |issue=3052|page=3052 |pmid=24165898 |pmc=3810665|bibcode=2013NatSR...3E3052P|arxiv=1306.0114 }}</ref>
*{{anchor|i10-index}}The ''i''10-index indicates the number of academic publications an author has written that have been cited by at least ten sources. It was introduced in July 2011 by [[Google]] as part of their work on [[Google Scholar]].<ref>Connor, James; Google Scholar Blog. [http://googlescholar.blogspot.com/2011/11/google-scholar-citations-open-to-all.html "Google Scholar Citations Open To All"], Google, 16 November 2011, retrieved 24 November 2011</ref>
* The ''h''-index has been shown to have a strong discipline bias. However, a simple normalization <math>h/\langle h \rangle_d</math> 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.<ref>{{cite journal |doi= 10.1016/j.joi.2013.09.002 |arxiv=1305.6339 |title= Universality of scholarly impact metrics |year=2013 |last1=Kaur |first1=Jasleen |last2=Radicchi |first2=Filippo |last3=Menczer |first3=Filippo |journal=Journal of Informetrics |volume= 7|issue= 4|pages= 924–32 }}</ref> Of course this method does not deal with academic age bias.
* The ''h''-index can be timed to analyze its evolution during one's career, employing different time windows.<ref>{{cite journal|last=Schreiber|first=Michael|date=2015|title=Restricting the ''h''-index to a publication and citation time window: A case study of a timed Hirsch index|url=|journal=Journal of Informetrics|volume=9|pages=150–55|doi=10.1016/j.joi.2014.12.005|arxiv=1412.5050}}</ref>
* The ''o''-index corresponds to the [[geometric mean]] of the ''h''-index and the most cited paper of a researcher.<ref name="DM2015">{{Cite journal |last = Dorogovtsev |first = S.N. |author2 = Mendes, J.F.F. |authorlink = Jose Fernando Ferreira Mendes |title = Ranking Scientists |journal = [[Nature Physics]] |volume = 11 |issue = 11 |pages = 882–84 |year=2015 |doi = 10.1038/nphys3533 |arxiv = 1511.01545|bibcode = 2015NatPh..11..882D }}</ref>
*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. <ref>{{Cite journal|last=Fatchur Rochim|first=Adian|date=November 2018|title=Improving fairness of ''h''-index: RA-index|journal=DESIDOC Journal of Library and Information Technology|volume=38|issue=6|pages=378–386|doi=10.14429/djlit.38.6.12937}}</ref>


==Criticism==
==Criticism==

Revision as of 20:01, 10 March 2020

Author-level metrics are citation metrics that measure the bibliometric impact of individual authors, researchers, academics, and scholars. A prime example is the h-index. Other metrics originally developed for academic journals can be reported at researcher level, such as the author-level eigenfactor[1] and the author impact factor.[2] Jorge E. Hirsch invented and suggested h-index as a "useful yardstick with which to compare, in an unbiased way, different individuals competing for the same resource when an important evaluation criterion is scientific achievement."[3]

List of metrics

  • Formally, if f is the function that corresponds to the number of citations for each publication, we compute the h-index 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.[3]
  • An individual h-index normalized by the number of authors has been proposed: , with being the number of authors considered in the papers.[4] 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.
  • The m-index is defined as h/n, where n is the number of years since the first published paper of the scientist;[3] also called m-quotient.[5][6]
  • 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.[7]
  • 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.[8]
  • 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.[9]
  • The g-index can be seen as the h-index for an averaged citations count.[10]
  • It has been argued that "For an individual researcher, a measure such as Erdős number captures the structural properties of 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 such as eigenfactor (based on eigenvector centrality) have been proposed already, for instance the Phys Author Rank Algorithm.[11]
  • 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.[12]
  • 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.[13]
  • 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).[14][15]
  • 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.[16] 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.[17]
  • The i10-index indicates the number of academic publications an author has written that have been cited by at least ten sources. It was introduced in July 2011 by Google as part of their work on Google Scholar.[18]
  • The h-index has been shown to have a strong discipline bias. However, a simple normalization 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.[19] Of course this method does not deal with academic age bias.
  • The h-index can be timed to analyze its evolution during one's career, employing different time windows.[20]
  • The o-index corresponds to the geometric mean of the h-index and the most cited paper of a researcher.[21]
  • 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. [22]

Criticism

Leo Szilard, the inventor nuclear chain reaction, expressed an early criticism of author-level metrics as a decision criterion for scientific research funding in his book "The Voice of the Dolphins and other Stories".[23] Senator J. Lister Hill read this criticism aloud in the senate hearing in 1962 as means to explain the retardation of science in the field of cancer research.[24] Senator Hill said that it was written as early as in 1948. In the book, the narrative of this criticism develops as a part of a dialog, where a way of preventing scientific progress is discussed:

"As a matter of fact, I think it would be quite easy. You could set up a foundation, with an annual endowment of thirty million dollars. Research workers in need of funds could apply for grants, if they could mail out a convincing case. Have ten committees, each committee, each composed of twelve scientists, appointed to pass on these applications. Take the most active scientists out of the laboratory and make them members of these committees. And the very best men in the field should be appointed as chairman at salaries of fifty thousand dollars each. Also have about twenty prizes of one hundred thousand dollars each for the best scientific papers of the year. This is just about all you would have to do. Your lawyers could easily prepare a charter for the foundation. As a matter of fact, any of the National Science Foundation bills which were introduced in the Seventy-ninth and Eightieth Congress could perfectly well serve as a model."

"First of all, the best scientists would be removed from their laboratories and kept busy on committees passing on applications for funds. Secondly the scientific workers in need of funds would concentrate on problems which were considered promising and were pretty certain to lead to publishable results. For a few years there might be a great increase in scientific output; but by going after the obvious, pretty soon science would dry out. Science would become something like a parlor game. Somethings would be considered interesting, others not. There would be fashions. Those who followed the fashions would get grants. Those who wouldn’t wouldnot, and pretty soon they would learn to follow the fashion, too."[23]

See also

Further reading

  • Opening Science: The Evolving Guide on How the Internet is Changing Research ... 2013-12-16. ISBN 978-3319000251. Retrieved 2015-08-16.
  • Sally Morris; Ed Barnas; Douglas LaFrenier; Margaret Reich (2013-02-21). The Handbook of Journal Publishing. ISBN 978-1107653603. Retrieved 2015-08-16.
  • Incentives and Performance: Governance of Research Organizations. ISBN 978-3319097848. Retrieved 2015-08-16.
  • Measuring Scholarly Impact: Methods and Practice. ISBN 978-3319103761. Retrieved 2015-08-16.

References

  1. ^ West, Jevin D.; Jensen, Michael C.; Dandrea, Ralph J.; Gordon, Gregory J.; Bergstrom, Carl T. (2013). "Author-level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community". Journal of the American Society for Information Science and Technology. 64 (4): 787–801. doi:10.1002/asi.22790.
  2. ^ Pan, Raj Kumar; Fortunato, Santo (2014). "Author Impact Factor: Tracking the dynamics of individual scientific impact". Scientific Reports. 4: 4880. arXiv:1312.2650. doi:10.1038/srep04880.
  3. ^ a b c Hirsch, J. E. (7 November 2005). "An index to quantify an individual's scientific research output". Proceedings of the National Academy of Sciences. 102 (46): 16569–16572. doi:10.1073/pnas.0507655102.
  4. ^ Batista P. D.; et al. (2006). "Is it possible to compare researchers with different scientific interests?". Scientometrics. 68 (1): 179–89. doi:10.1007/s11192-006-0090-4.
  5. ^ Anne-Wil Harzing (2008-04-23). "Reflections on the h-index". Retrieved 2013-07-18.
  6. ^ von Bohlen und Halbach O (2011). "How to judge a book by its cover? How useful are bibliometric indices for the evaluation of "scientific quality" or "scientific productivity"?". Annals of Anatomy. 193 (3): 191–96. doi:10.1016/j.aanat.2011.03.011. PMID 21507617.
  7. ^ Tscharntke, T.; Hochberg, M. E.; Rand, T. A.; Resh, V. H.; Krauss, J. (2007). "Author Sequence and Credit for Contributions in Multiauthored Publications". PLoS Biology. 5 (1): e18. doi:10.1371/journal.pbio.0050018. PMC 1769438. PMID 17227141.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  8. ^ Gągolewski, M.; Grzegorzewski, P. (2009). "A geometric approach to the construction of scientific impact indices". Scientometrics. 81 (3): 617–34. doi:10.1007/s11192-008-2253-y.
  9. ^ Bornmann, Lutz; Mutz, Rüdiger; Daniel, Hans-Dieter (2010). "The h index research output measurement: Two approaches to enhance its accuracy". Journal of Informetrics. 4 (3): 407–14. doi:10.1016/j.joi.2010.03.005.
  10. ^ Egghe, Leo (2013). "Theory and practise of the g-index" (PDF). Scientometrics. 69: 131–52. doi:10.1007/s11192-006-0144-7. hdl:1942/981.
  11. ^ Kashyap Dixit; S Kameshwaran; Sameep Mehta; Vinayaka Pandit; N Viswanadham (February 2009). "Towards simultaneously exploiting structure and outcomes in interaction networks for node ranking" (PDF). IBM Research Report R109002.; see also Kameshwaran, Sampath; Pandit, Vinayaka; Mehta, Sameep; Viswanadham, Nukala; Dixit, Kashyap (2010). "Outcome aware ranking in interaction networks". Proceedings of the 19th ACM international conference on Information and knowledge management – CIKM '10. p. 229. doi:10.1145/1871437.1871470. ISBN 9781450300995. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)
  12. ^ Bras-Amorós, M.; Domingo-Ferrer, J.; Torra, V (2011). "A bibliometric index based on the collaboration distance between cited and citing authors". Journal of Informetrics. 5 (2): 248–64. doi:10.1016/j.joi.2010.11.001. hdl:10261/138172.
  13. ^ Silagadze, Z. K. (2010). "Citation entropy and research impact estimation". Acta Phys. Pol. B. 41 (2010): 2325–33. arXiv:0905.1039. Bibcode:2009arXiv0905.1039S.
  14. ^ Zhang, Chun-Ting (2009). Joly, Etienne (ed.). "The e-Index, Complementing the h-Index for Excess Citations". PLoS ONE. 4 (5): e5429. Bibcode:2009PLoSO...4.5429Z. doi:10.1371/journal.pone.0005429. PMC 2673580. PMID 19415119.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  15. ^ Dodson, M.V. (2009). "Citation analysis: Maintenance of h-index and use of e-index". Biochemical and Biophysical Research Communications. 387 (4): 625–26. doi:10.1016/j.bbrc.2009.07.091. PMID 19632203.
  16. ^ Acuna, Daniel E.; Allesina, Stefano; Kording, Konrad P. (2012). "Future impact: Predicting scientific success". Nature. 489 (7415): 201–02. Bibcode:2012Natur.489..201A. doi:10.1038/489201a. PMC 3770471. PMID 22972278.
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