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Tobias Opthof

Publications and source records attributed to Tobias Opthof.

12 recordsLinked to original sources

hα: The Scientist as Chimpanzee or Bonobo

In a recent paper, Hirsch (2018) proposes to attribute the credit for a co-authored paper to the α-author--the author with the highest h-index--regardless of his or her actual contribution, effectively reducing the role of the other co-authors to zero. The indicator hα inherits most of the disadvantages of the h-index from which it is derived, but adds the normative element of reinforcing the Matthew effect in science. Using an example, we show that hα can be extremely unstable. The empirical attribution of credit among co-authors is not captured by abstract models such as h, h_bar , or hα.

cs.DL

Revisiting Relative Indicators and Provisional Truths

Following discussions in 2010 and 2011, scientometric evaluators have increasingly abandoned relative indicators in favor of comparing observed with expected citation ratios. The latter method provides parameters with error values allowing for the statistical testing of differences in citation scores. A further step would be to proceed to non-parametric statistics (e.g., the top-10%) given the extreme skewness (non-normality) of the citation distributions. In response to a plea for returning to relative indicators in the previous issue of this newsletter, we argue in favor of further progress in the development of citation impact indicators.

cs.DL

Citation Analysis with Medical Subject Headings (MeSH) using the Web of Knowledge: A new routine

Citation analysis of documents retrieved from the Medline database (at the Web of Knowledge) has been possible only on a case-by-case basis. A technique is here developed for citation analysis in batch mode using both Medical Subject Headings (MeSH) at the Web of Knowledge and the Science Citation Index at the Web of Science. This freeware routine is applied to the case of "Brugada Syndrome," a specific disease and field of research (since 1992). The journals containing these publications, for example, are attributed to Web-of-Science Categories other than "Cardiac and Cardiovascular Systems"), perhaps because of the possibility of genetic testing for this syndrome in the clinic. With this routine, all the instruments available for citation analysis can now be used on the basis of MeSH terms. Other options for crossing between Medline, WoS, and Scopus are also reviewed.

cs.DL

A Rejoinder on Energy versus Impact Indicators

Citation distributions are so skewed that using the mean or any other central tendency measure is ill-advised. Unlike G. Prathap's scalar measures (Energy, Exergy, and Entropy or EEE), the Integrated Impact Indicator (I3) is based on non-parametric statistics using the (100) percentiles of the distribution. Observed values can be tested against expected ones; impact can be qualified at the article level and then aggregated.

cs.DL

Turning the tables in citation analysis one more time: Principles for comparing sets of documents

We submit newly developed citation impact indicators based not on arithmetic averages of citations but on percentile ranks. Citation distributions are-as a rule-highly skewed and should not be arithmetically averaged. With percentile ranks, the citation of each paper is rated in terms of its percentile in the citation distribution. The percentile ranks approach allows for the formulation of a more abstract indicator scheme that can be used to organize and/or schematize different impact indicators according to three degrees of freedom: the selection of the reference sets, the evaluation criteria, and the choice of whether or not to define the publication sets as independent. Bibliometric data of seven principal investigators (PIs) of the Academic Medical Center of the University of Amsterdam is used as an exemplary data set. We demonstrate that the proposed indicators [R(6), R(100), R(6,k), R(100,k)] are an improvement of averages-based indicators because one can account for the shape of the distributions of citations over papers.

cs.DL

Citation analysis cannot legitimate the strategic selection of excellence

In reaction to a previous critique(Opthof & Leydesdorff, 2010), the Center for Science and Technology Studies (CWTS) in Leiden proposed to change their old "crown" indicator in citation analysis into a new one. Waltman et al. (2011)argue that this change does not affect rankings at various aggregated levels. However, CWTS data is not publicly available for testing and criticism. In this correspondence, we use previously published data of Van Raan (2006) to address the pivotal issue of how the results of citation analysis correlate with the results of peer review. A quality parameter based on peer review was neither significantly correlated with the two parameters developed by the CWTS in the past (CPP/JCSm or CPP/FCSm) nor with the more recently proposed h-index (Hirsch, 2005). Given the high correlations between the old and new "crown" indicators, one can expect that the lack of correlation with the peer-review based quality indicator applies equally to the newly developed ones.

cs.DL

Remaining problems with the "New Crown Indicator" (MNCS) of the CWTS

In their article, entitled "Towards a new crown indicator: some theoretical considerations," Waltman et al. (2010; at arXiv:1003.2167) show that the "old crown indicator" of CWTS in Leiden was mathematically inconsistent and that one should move to the normalization as applied in the "new crown indicator." Although we now agree about the statistical normalization, the "new crown indicator" inherits the scientometric problems of the "old" one in treating subject categories of journals as a standard for normalizing differences in citation behavior among fields of science. We further note that the "mean" is not a proper statistics for measuring differences among skewed distributions. Without changing the acronym of "MNCS," one could define the "Median Normalized Citation Score." This would relate the new crown indicator directly to the percentile approach that is, for example, used in the Science and Engineering Indicators of US National Science Board (2010). The median is by definition equal to the 50th percentile. The indicator can thus easily be extended with the 1% (= 99th percentile) most highly-cited papers (Bornmann et al., in press). The seeming disadvantage of having to use non-parametric statistics is more than compensated by possible gains in the precision.

cs.DL

Scopus' SNIP Indicator

Rejoinder to Moed [arXiv:1005.4906]: Our main objection is against developing new indicators which, like some of the older ones (for example, the "crown indicator" of CWTS), do not allow for indicating error because they do not provide a statistics, but are based, in our opinion, on a violation of the order of operations. The claim of validity for the SNIP indicator is hollow because the normalizations are based on field classifications which are not valid. Both problems can perhaps be solved by using fractional counting.

cs.DL

Normalization at the field level: fractional counting of citations

Van Raan et al. (2010; arXiv:1003.2113) have proposed a new indicator (MNCS) for field normalization. Since field normalization is also used in the Leiden Rankings of universities, we elaborate our critique of journal normalization in Opthof & Leydesdorff (2010; arXiv:1002.2769) in this rejoinder concerning field normalization. Fractional citation counting thoroughly solves the issue of normalization for differences in citation behavior among fields. This indicator can also be used to obtain a normalized impact factor.

cs.DL

Normalization, CWTS indicators, and the Leiden Rankings: Differences in citation behavior at the level of fields

Van Raan et al. (2010; arXiv:1003.2113) have proposed a new indicator (MNCS) for field normalization. Since field normalization is also used in the Leiden Rankings of universities, we elaborate our critique of journal normalization in Opthof & Leydesdorff (2010; arXiv:1002.2769) in this rejoinder concerning field normalization. Fractional citation counting thoroughly solves the issue of normalization for differences in citation behavior among fields. This indicator can also be used to obtain a normalized impact factor.

physics.soc-ph

Scopus's Source Normalized Impact per Paper (SNIP) versus a Journal Impact Factor based on Fractional Counting of Citations

Impact factors (and similar measures such as the Scimago Journal Rankings) suffer from two problems: (i) citation behavior varies among fields of science and therefore leads to systematic differences, and (ii) there are no statistics to inform us whether differences are significant. The recently introduced SNIP indicator of Scopus tries to remedy the first of these two problems, but a number of normalization decisions are involved which makes it impossible to test for significance. Using fractional counting of citations-based on the assumption that impact is proportionate to the number of references in the citing documents-citations can be contextualized at the paper level and aggregated impacts of sets can be tested for their significance. It can be shown that the weighted impact of Annals of Mathematics (0.247) is not so much lower than that of Molecular Cell (0.386) despite a five-fold difference between their impact factors (2.793 and 13.156, respectively).

cs.DL

Caveats for the journal and field normalizations in the CWTS ("Leiden") evaluations of research performance

The Center for Science and Technology Studies at Leiden University advocates the use of specific normalizations for assessing research performance with reference to a world average. The Journal Citation Score (JCS) and Field Citation Score (FCS) are averaged for the research group or individual researcher under study, and then these values are used as denominators of the (mean) Citations per publication (CPP). Thus, this normalization is based on dividing two averages. This procedure only generates a legitimate indicator in the case of underlying normal distributions. Given the skewed distributions under study, one should average the observed versus expected values which are to be divided first for each publication. We show the effects of the Leiden normalization for a recent evaluation where we happened to have access to the underlying data.

cs.DL