Benchmarking research performance at the university level with information theoretic measures

García, J. A.; Rodrı́guez-Sánchez, Rosa; Fdez-Valdivia, J.; Robinson-García, Nicolás; Torres‐Salinas, Daniel

doi:10.1007/s11192-012-0854-y

Cited by 6 publications

(4 citation statements)

References 25 publications

(23 reference statements)

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“…In this section we give the key points for understanding and interpreting the application of the information gain methodology in this study. For a more exhaustive presentation of this methodology the reader is referred to Appendix A and to García et al (2012) where this methodology is applied in a bibliometric context for benchmarking academic institutions.…”

Section: Methodology For Mapping Academic Publishers: Information Gainmentioning

confidence: 99%

“…We believe that this study offers a first approach to the BKCI database as the application of information theoretic measures allows us to identify the main publishers and their main characteristics by area, an important issue when studying and validating a new information resource. This methodology has already been successfully applied for benchmarking academic institutions (García et al, 2012). Therefore, our aim is to develop what we have named 'heliocentric clockwise maps' as a means to describe the structure of the BKCI through book chapters' citation patterns.…”

Section: Introductionmentioning

confidence: 99%

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Mapping citation patterns of book chapters in the Book Citation Index

Torres‐Salinas

Rodrı́guez-Sánchez

Robinson-García

et al. 2013

Journal of Informetrics

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In this paper we provide the reader with a visual representation of relationships among the impact of book chapters indexed in the Book Citation Index using information gain values and published by different academic publishers in specific disciplines. The impact of book chapters can be characterized statistically by citations histograms. For instance, we can compute the probability of occurrence of book chapters with a number of citations in different intervals for each academic publisher. We predict the similarity between two citation histograms based on the amount of relative information between such characterizations. We observe that the citation patterns of book chapters follow a Lotkaian distribution. This paper describes the structure of the Book Citation Index using 'heliocentric clockwise maps' which allow the reader not only to determine the grade of similarity of a given academic publisher indexed in the Book Citation Index with a specific discipline according to their citation distribution, but also to easily observe the general structure of a discipline, identifying the publishers with higher impact and output.

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Section: Methodology For Mapping Academic Publishers: Information Gainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping citation patterns of book chapters in the Book Citation Index

Torres‐Salinas

Rodrı́guez-Sánchez

Robinson-García

et al. 2013

Journal of Informetrics

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“…This made it imperative to investigate possible gaps in coverage of the current esteem-factor approach. Furthermore, the inclusion of publication and citation data would give a better multidimensional picture of the University's research strengths and could back-up the existing esteem indicators (García et al 2013;Rafols et al 2010). A comprehensive method to identify research specialisations is of increasing importance since in addition to their role in the public image of the University of Vienna, they are envisaged as potential basis of strategic resource decisions ("strengthening the strengths").…”

Section: Background and Introductionmentioning

confidence: 99%

Research strengths identified by esteem and bibliometric indicators: a case study at the University of Vienna

et al. 2020

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The identification of one's own research strengths is of crucial importance for research administration at universities. In this case study, two different approaches were applied to the University of Vienna. The first relies on funding and rankings information as well as on other esteem indicators. The second is based on a bibliometric analysis of the publication output. We used two alternative clusterings for publications for the bibliometric analysis: Web of Science subject categories and lists of researchers associated with esteem-indicators. Both, esteem-indicators and bibliometric analysis proved to be useful in identifying research strengths, lead to similar results and are meant to be used together to complement each other. We found that the greatest hindrance in the bibliometric approach lies in the inherent limitations of journals-assignment-based classification systems and the considerable time and efforts for more accurate researcher-based publication analyses. Further investigation on this subject, including new and alternative metrics, is needed and will be conducted in the future. However, the preliminary results already demonstrate the benefits of using esteem-factors together with bibliometric analyses for research strengths definition of universities.

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“…This methodology is based on information gain or Kullback-Leibler divergence (Kullback & Leibler, 1951), in which distributions of a given indicator are compared meaning that the more similar they are, the lesser information gain there is between them. This methodology has already been successfully tested for comparing academic institutions (García et al, 2012c) and citation patterns of book chapters (Torres-Salinas et al, 2013). In this approach, the prestige of academic journals in each category is represented statistically by an impact-factor histogram, and thus, we compute the probability of occurrence of scholarly journals with impact factor in different intervals.…”

Section: Introductionmentioning

confidence: 99%

Best-in-class and strategic benchmarking of scientific subject categories of Web of Science in 2010

et al. 2013

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Here we show a novel technique for comparing subject categories, where the prestige of academic journals in each category is represented statistically by an impact-factor histogram. For each subject category we compute the probability of occurrence of scholarly journals with impact factor in different intervals. Here impact factor is measured with Thomson Reuters Impact Factor, Eigenfactor Score, and Immediacy Index. Assuming the probabilities associated with a pair of subject categories our objective is to measure the degree of dissimilarity between them. To do so, we use an axiomatic characterization for predicting dissimilarity between subject categories. The scientific subject categories of Web of Science in 2010 were used to test the proposed approach for benchmarking Cell Biology and Computer Science Information Systems with the rest as two case studies. The former is best-in-class benchmarking that involves studying the leading competitor category; the latter is strategic benchmarking that involves observing how other scientific subject categories compete.

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Benchmarking research performance at the university level with information theoretic measures

Cited by 6 publications

References 25 publications

Mapping citation patterns of book chapters in the Book Citation Index

Mapping citation patterns of book chapters in the Book Citation Index

Research strengths identified by esteem and bibliometric indicators: a case study at the University of Vienna

Best-in-class and strategic benchmarking of scientific subject categories of Web of Science in 2010

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