8,635 research outputs found
Inter-field nonlinear transformation of journal impact indicators: The case of the h-index
[EN] Impact indices used for joint evaluation of research items coming from different scientific fields must be comparable. Often a linear transformation -a normalization or another basic operation-is considered to be enough for providing the correct translation to a unified setting in which all the fields are adequately treated. In this paper it is shown that this is not always true. The attention is centered in the case of the h-index. It is proved that it that cannot be translated by means of direct normalization preserving its genuine meaning. According to the universality of citation distribution, it is shown that a slight variant of the h-index is necessary for this notion to produce comparable values when applied to different scientific fields. A complete example concerning a group of top scientists is shown.The first author was supported by Ministerio de Economia, Industria y Competitividad under Research Grant CSO2015-65594-C2-1R Y 2R (MINECO/FEDER, UE). The second author was suported by Ministerio de Economia, Industria y Competitividad and FEDER under Research Grant MTM2016-77054-C2-1-PFerrer Sapena, A.; Sánchez Pérez, EA. (2019). Inter-field nonlinear transformation of journal impact indicators: The case of the h-index. Journal of Interdisciplinary Mathematics. 22(2):177-199. https://doi.org/10.1080/09720502.2019.1616913S177199222Geuna, A., & Piolatto, M. (2016). Research assessment in the UK and Italy: Costly and difficult, but probably worth it (at least for a while). Research Policy, 45(1), 260-271. doi:10.1016/j.respol.2015.09.004Hicks, D. (2012). Performance-based university research funding systems. Research Policy, 41(2), 251-261. doi:10.1016/j.respol.2011.09.007Hirsch, J. E. (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.0507655102Egghe, L. (2010). The Hirsch index and related impact measures. Annual Review of Information Science and Technology, 44(1), 65-114. doi:10.1002/aris.2010.1440440109Van Leeuwen, T. (2008). Testing the validity of the Hirsch-index for research assessment purposes. Research Evaluation, 17(2), 157-160. doi:10.3152/095820208x319175Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., & Herrera, F. (2009). h-Index: A review focused in its variants, computation and standardization for different scientific fields. Journal of Informetrics, 3(4), 273-289. doi:10.1016/j.joi.2009.04.001Imperial, J., & Rodríguez-Navarro, A. (2007). Usefulness of Hirsch’s h-index to evaluate scientific research in Spain. Scientometrics, 71(2), 271-282. doi:10.1007/s11192-007-1665-4Aoun, S. G., Bendok, B. R., Rahme, R. J., Dacey, R. G., & Batjer, H. H. (2013). Standardizing the Evaluation of Scientific and Academic Performance in Neurosurgery—Critical Review of the «h» Index and its Variants. World Neurosurgery, 80(5), e85-e90. doi:10.1016/j.wneu.2012.01.052Waltman, L., & van Eck, N. J. (2011). The inconsistency of the h-index. Journal of the American Society for Information Science and Technology, 63(2), 406-415. doi:10.1002/asi.21678Rousseau, R., García-Zorita, C., & Sanz-Casado, E. (2013). The h-bubble. Journal of Informetrics, 7(2), 294-300. doi:10.1016/j.joi.2012.11.012Burrell, Q. L. (2013). The h-index: A case of the tail wagging the dog? Journal of Informetrics, 7(4), 774-783. doi:10.1016/j.joi.2013.06.004Schreiber, M. (2013). How relevant is the predictive power of the h-index? A case study of the time-dependent Hirsch index. Journal of Informetrics, 7(2), 325-329. doi:10.1016/j.joi.2013.01.001Khan, N. R., Thompson, C. J., Taylor, D. R., Gabrick, K. S., Choudhri, A. F., Boop, F. R., & Klimo, P. (2013). Part II: Should the h-Index Be Modified? An Analysis of the m-Quotient, Contemporary h-Index, Authorship Value, and Impact Factor. World Neurosurgery, 80(6), 766-774. doi:10.1016/j.wneu.2013.07.011Schreiber, M. (2013). A case study of the arbitrariness of the h-index and the highly-cited-publications indicator. Journal of Informetrics, 7(2), 379-387. doi:10.1016/j.joi.2012.12.006Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548), 429-431. doi:10.1038/520429aDienes, K. R. (2015). Completing h. Journal of Informetrics, 9(2), 385-397. doi:10.1016/j.joi.2015.01.003Ayaz, S., & Afzal, M. T. (2016). Identification of conversion factor for completing-h index for the field of mathematics. Scientometrics, 109(3), 1511-1524. doi:10.1007/s11192-016-2122-zWaltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365-391. doi:10.1016/j.joi.2016.02.007Van Eck, N. J., & Waltman, L. (2008). Generalizing the h- and g-indices. Journal of Informetrics, 2(4), 263-271. doi:10.1016/j.joi.2008.09.004Egghe, L., & Rousseau, R. (2008). An h-index weighted by citation impact. Information Processing & Management, 44(2), 770-780. doi:10.1016/j.ipm.2007.05.003Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152. doi:10.1007/s11192-006-0144-7Iglesias, J. E., & Pecharromán, C. (2007). Scaling the h-index for different scientific ISI fields. Scientometrics, 73(3), 303-320. doi:10.1007/s11192-007-1805-xEgghe, L. (2008). Examples of simple transformations of the h-index: Qualitative and quantitative conclusions and consequences for other indices. Journal of Informetrics, 2(2), 136-148. doi:10.1016/j.joi.2007.12.003Schreiber, M. (2015). Restricting the h-index to a publication and citation time window: A case study of a timed Hirsch index. Journal of Informetrics, 9(1), 150-155. doi:10.1016/j.joi.2014.12.00
Methods for measuring the citations and productivity of scientists across time and discipline
Publication statistics are ubiquitous in the ratings of scientific
achievement, with citation counts and paper tallies factoring into an
individual's consideration for postdoctoral positions, junior faculty, tenure,
and even visa status for international scientists. Citation statistics are
designed to quantify individual career achievement, both at the level of a
single publication, and over an individual's entire career. While some academic
careers are defined by a few significant papers (possibly out of many), other
academic careers are defined by the cumulative contribution made by the
author's publications to the body of science. Several metrics have been
formulated to quantify an individual's publication career, yet none of these
metrics account for the dependence of citation counts and journal size on time.
In this paper, we normalize publication metrics across both time and discipline
in order to achieve a universal framework for analyzing and comparing
scientific achievement. We study the publication careers of individual authors
over the 50-year period 1958-2008 within six high-impact journals: CELL, the
New England Journal of Medicine (NEJM), Nature, the Proceedings of the National
Academy of Science (PNAS), Physical Review Letters (PRL), and Science. In
comparing the achievement of authors within each journal, we uncover
quantifiable statistical regularity in the probability density function (pdf)
of scientific achievement across both time and discipline. The universal
distribution of career success within these arenas for publication raises the
possibility that a fundamental driving force underlying scientific achievement
is the competitive nature of scientific advancement.Comment: 25 pages in 1 Column Preprint format, 7 Figures, 4 Tables. Version
II: changes made in response to referee comments. Note: change in definition
of "Paper shares.
Universality of citation distributions: towards an objective measure of scientific impact
We study the distributions of citations received by a single publication
within several disciplines, spanning broad areas of science. We show that the
probability that an article is cited times has large variations between
different disciplines, but all distributions are rescaled on a universal curve
when the relative indicator is considered, where is the
average number of citations per article for the discipline. In addition we show
that the same universal behavior occurs when citation distributions of articles
published in the same field, but in different years, are compared. These
findings provide a strong validation of as an unbiased indicator for
citation performance across disciplines and years. Based on this indicator, we
introduce a generalization of the h-index suitable for comparing scientists
working in different fields.Comment: 7 pages, 5 figures. accepted for publication in Proc. Natl Acad. Sci.
US
The structure of the Arts & Humanities Citation Index: A mapping on the basis of aggregated citations among 1,157 journals
Using the Arts & Humanities Citation Index (A&HCI) 2008, we apply mapping
techniques previously developed for mapping journal structures in the Science
and Social Science Citation Indices. Citation relations among the 110,718
records were aggregated at the level of 1,157 journals specific to the A&HCI,
and the journal structures are questioned on whether a cognitive structure can
be reconstructed and visualized. Both cosine-normalization (bottom up) and
factor analysis (top down) suggest a division into approximately twelve
subsets. The relations among these subsets are explored using various
visualization techniques. However, we were not able to retrieve this structure
using the ISI Subject Categories, including the 25 categories which are
specific to the A&HCI. We discuss options for validation such as against the
categories of the Humanities Indicators of the American Academy of Arts and
Sciences, the panel structure of the European Reference Index for the
Humanities (ERIH), and compare our results with the curriculum organization of
the Humanities Section of the College of Letters and Sciences of UCLA as an
example of institutional organization
Scientific impact evaluation and the effect of self-citations: mitigating the bias by discounting h-index
In this paper, we propose a measure to assess scientific impact that
discounts self-citations and does not require any prior knowledge on the their
distribution among publications. This index can be applied to both researchers
and journals. In particular, we show that it fills the gap of h-index and
similar measures that do not take into account the effect of self-citations for
authors or journals impact evaluation. The paper provides with two real-world
examples: in the former, we evaluate the research impact of the most productive
scholars in Computer Science (according to DBLP); in the latter, we revisit the
impact of the journals ranked in the 'Computer Science Applications' section of
SCImago. We observe how self-citations, in many cases, affect the rankings
obtained according to different measures (including h-index and ch-index), and
show how the proposed measure mitigates this effect
A review of the characteristics of 108 author-level bibliometric indicators
An increasing demand for bibliometric assessment of individuals has led to a
growth of new bibliometric indicators as well as new variants or combinations
of established ones. The aim of this review is to contribute with objective
facts about the usefulness of bibliometric indicators of the effects of
publication activity at the individual level. This paper reviews 108 indicators
that can potentially be used to measure performance on the individual author
level, and examines the complexity of their calculations in relation to what
they are supposed to reflect and ease of end-user application.Comment: to be published in Scientometrics, 201
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