Completing h

Dienes, Keith R.

doi:10.1016/j.joi.2015.01.003

Cited by 24 publications

(17 citation statements)

References 62 publications

(91 reference statements)

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“…It prejudices young researchers and individuals who publish in categories with low citation rates. Modifications to improve the h -index include fractional counting (Leydesdorff and Opthof, 2010), normalizing citations, correcting for the dimensionality of the h -index with a conversion factor (Dienes, 2015). An

h_{normalcat}

minimizes the pitfalls associated with the individual h -index; it is an aggregate value that applies to all researchers for the same 5-year period.…”

Section: Resultsmentioning

confidence: 99%

Citation analysis of scientific categories

Patience

Blais

et al. 2017

Heliyon

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Databases catalogue the corpus of research literature into scientific categories and report classes of bibliometric data such as the number of citations to articles, the number of authors, journals, funding agencies, institutes, references, etc. The number of articles and citations in a category are gauges of productivity and scientific impact but a quantitative basis to compare researchers between categories is limited. Here, we compile a list of bibliometric indicators for 236 science categories and citation rates of the 500 most cited articles of each category. The number of citations per paper vary by several orders of magnitude and are highest in multidisciplinary sciences, general internal medicine, and biochemistry and lowest in literature, poetry, and dance. A regression model demonstrates that citation rates to the top articles in each category increase with the square root of the number of articles in a category and decrease proportionately with the age of the references: articles in categories that cite recent research are also cited more frequently. The citation rate correlates positively with the number of funding agencies that finance the research. The category h-index correlates with the average number of cites to the top 500 ranked articles of each category (R2=0.997). Furthermore, only a few journals publish the top 500 cited articles in each category: four journals publish 60% (σ=±20%) of these and ten publish 81% (σ=±15%).

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h_{normalcat}

minimizes the pitfalls associated with the individual h -index; it is an aggregate value that applies to all researchers for the same 5-year period.…”

Section: Resultsmentioning

confidence: 99%

Citation analysis of scientific categories

Patience

Blais

et al. 2017

Heliyon

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show abstract

“…Indeed, departments are usually ranked according to publications of its members in few and specialized journals. The main strengths of the new approach are that:  All main proposals for modifying the original H index have been included (Dienes, 2015), including the elimination of self-and cross-citations, an increased weighting to highly cited articles, a focus on peer-reviewed scientific journals, the use of fractional citations to account for the number of authors (i.e., awarding authors a fraction of a point instead of a full point for multi-author articles), an increased sensitivity to variability of the overall citation profile, and the consideration of the life cycle of an article.  Discrimination against inter-disciplinary and heterodox CVs can be reduced by mitigating the bias created by conventional rankings, without relying on the application of advanced methodologies to complex datasets, as in the case of applying empirically based scaling factors to different disciplines (Ruocco & Daraio, 2013), comparisons with the performance of other researchers in the same field (Nair, 2015), or comparison with the average number of citations per paper in a given discipline (Radicchi & Castellano, 2012)  All main questions left open by the original description of the H index have been tackled (Gagolewski, 2013), including the attribution of an article to a given discipline, since this is done by the author.…”

Section: Discussionmentioning

confidence: 99%

“…Here, I will refer to the most popular H index Bertoli-Barsotti & Lando, 2015). In particular, I will calculate alternative nested (as clarified in Section 2) versions of the H index based on common information available in the Scopus dataset, by referring to insights suggested by the authors of other indices and by attempting to solve the empirical shortcomings (e.g., sensitivity to citations, the "fashion" effect, attribution to disciplines, life cycle of articles; Dienes, 2015) as well as the open questions (e.g., the attribution of an article to a given discipline; Gagolewski, 2013) of the original H index. Note that the focus on articles reduces the dependence of my results on the dataset used because most databases include all full-length journal articles by an author.…”

Section: Introductionmentioning

confidence: 99%

Scientific Production and Productivity in Curriculum Vitae Characterisation: Simple and Nested H Indices that Support Cross-Disciplinary Comparisons

Zagonari

2017

SSRN Journal

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In this study, I developed operational versions of Hirsch's H index that can be applied to each researcher's curriculum vitae (CV) to allow cross-disciplinary comparisons. The revised indices account for anomalies that potentially arise from tactical or opportunistic citation and publication behaviours by authors and editors, and can be calculated from readily available information. I split the original H index into nested indices to isolate networking activity, distinguish scientific production and productivity, and used nested Gini indices to identify intentional and successful inter-topical and inter-disciplinary research. I applied the most popular normalisations (i.e., per author and per year) using simple methodologies (i.e., least-squares linear and cubic interpolation fitting, whole-career vs. sub-periods, two-dimensional graphs) to solve empirical problems (e.g., sensitivity to citations, the "fashion" effect, attribution to disciplines, life cycle of articles) as well as open questions (e.g., the attribution of an article to a given discipline) associated with the original H index. I provided three numerical examples based on a representative heterodox, a representative orthodox multi-disciplinary, and a representative orthodox uni-disciplinary CV: the first CV includes 17 Scopus publications, and shows a highly heterodox (i.e., 5.8%), but no interdisciplinary research career, with a tiny networking component (i.e., 0.9%); the second CV includes 24 Scopus publications, and shows a slightly heterodox (i.e., 0.3%), but highly interdisciplinary (i.e., 53.9%) research career, with a small networking component (i.e., 14.3%); the third CV includes 16 Scopus publications, and shows slightly heterodox (i.e., 0.1%) and no interdisciplinary research career, with no networking component.

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“…Bibliometric indicators such as the impact factor and the h ‐index measure the impact of journals, articles, and researchers . Google Scholar (GS) classifies journals by the h ‐5, which equals the number of articles in the previous 5 years, h , with at least h citations.…”

Section: Scientific Category Rank: H‐5 and φτξ Factormentioning

confidence: 99%