Search (11 results, page 1 of 1)

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Abstract

The h-index is a popular bibliometric indicator for assessing individual scientists. We criticize the h-index from a theoretical point of view. We argue that for the purpose of measuring the overall scientific impact of a scientist (or some other unit of analysis), the h-index behaves in a counterintuitive way. In certain cases, the mechanism used by the h-index to aggregate publication and citation statistics into a single number leads to inconsistencies in the way in which scientists are ranked. Our conclusion is that the h-index cannot be considered an appropriate indicator of a scientist's overall scientific impact. Based on recent theoretical insights, we discuss what kind of indicators can be used as an alternative to the h-index. We pay special attention to the highly cited publications indicator. This indicator has a lot in common with the h-index, but unlike the h-index it does not produce inconsistent rankings.

Type

a

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Abstract

F1000 is a postpublication peer review service for biological and medical research. F1000 recommends important publications in the biomedical literature, and from this perspective F1000 could be an interesting tool for research evaluation. By linking the complete database of F1000 recommendations to the Web of Science bibliographic database, we are able to make a comprehensive comparison between F1000 recommendations and citations. We find that about 2% of the publications in the biomedical literature receive at least one F1000 recommendation. Recommended publications on average receive 1.30 recommendations, and more than 90% of the recommendations are given within half a year after a publication has appeared. There turns out to be a clear correlation between F1000 recommendations and citations. However, the correlation is relatively weak, at least weaker than the correlation between journal impact and citations. More research is needed to identify the main reasons for differences between recommendations and citations in assessing the impact of publications.

Type

a

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Abstract

We present a theoretical and empirical analysis of a number of bibliometric indicators of journal performance. We focus on three indicators in particular: the Eigenfactor indicator, the audience factor, and the influence weight indicator. Our main finding is that the last two indicators can be regarded as a kind of special case of the first indicator. We also find that the three indicators can be nicely characterized in terms of two properties. We refer to these properties as the property of insensitivity to field differences and the property of insensitivity to insignificant journals. The empirical results that we present illustrate our theoretical findings. We also show empirically that the differences between various indicators of journal performance are quite substantial.

Type

a

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Abstract

VOS is a new mapping technique that can serve as an alternative to the well-known technique of multidimensional scaling (MDS). We present an extensive comparison between the use of MDS and the use of VOS for constructing bibliometric maps. In our theoretical analysis, we show the mathematical relation between the two techniques. In our empirical analysis, we use the techniques for constructing maps of authors, journals, and keywords. Two commonly used approaches to bibliometric mapping, both based on MDS, turn out to produce maps that suffer from artifacts. Maps constructed using VOS turn out not to have this problem. We conclude that in general maps constructed using VOS provide a more satisfactory representation of a dataset than maps constructed using well-known MDS approaches.

Type

a

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Abstract

A percentile-based bibliometric indicator is an indicator that values publications based on their position within the citation distribution of their field. The most straightforward percentile-based indicator is the proportion of frequently cited publications, for instance, the proportion of publications that belong to the top 10% most frequently cited of their field. Recently, more complex percentile-based indicators have been proposed. A difficulty in the calculation of percentile-based indicators is caused by the discrete nature of citation distributions combined with the presence of many publications with the same number of citations. We introduce an approach to calculating percentile-based indicators that deals with this difficulty in a more satisfactory way than earlier approaches suggested in the literature. We show in a formal mathematical framework that our approach leads to indicators that do not suffer from biases in favor of or against particular fields of science.

Type

a

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Abstract

Classifying journals or publications into research areas is an essential element of many bibliometric analyses. Classification usually takes place at the level of journals, where the Web of Science subject categories are the most popular classification system. However, journal-level classification systems have two important limitations: They offer only a limited amount of detail, and they have difficulties with multidisciplinary journals. To avoid these limitations, we introduce a new methodology for constructing classification systems at the level of individual publications. In the proposed methodology, publications are clustered into research areas based on citation relations. The methodology is able to deal with very large numbers of publications. We present an application in which a classification system is produced that includes almost 10 million publications. Based on an extensive analysis of this classification system, we discuss the strengths and the limitations of the proposed methodology. Important strengths are the transparency and relative simplicity of the methodology and its fairly modest computing and memory requirements. The main limitation of the methodology is its exclusive reliance on direct citation relations between publications. The accuracy of the methodology can probably be increased by also taking into account other types of relations-for instance, based on bibliographic coupling.

Type

a

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Abstract

Research evaluation has become routine and often relies on metrics. But it is increasingly driven by data and not by expert judgement. As a result, the procedures that were designed to increase the quality of research are now threatening to damage the scientific system. To support researchers and managers, five experts led by Diana Hicks, professor in the School of Public Policy at Georgia Institute of Technology, and Paul Wouters, director of CWTS at Leiden University, have proposed ten principles for the measurement of research performance: the Leiden Manifesto for Research Metrics published as a comment in Nature.

Type

a

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Abstract

We provide in this article a number of new insights into the methodological discussion about author co-citation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors' co-citation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. We show by means of an example that the choice of an appropriate similarity measure has a high practical relevance. Finally, we discuss the use of similarity measures for statistical inference.

Type

a

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Abstract

Radicchi, Fortunato, and Castellano (2008) claim that, apart from a scaling factor, all fields of science are characterized by the same citation distribution. We present a large-scale validation study of this universality-of-citation-distributions claim. Our analysis shows that claiming citation distributions to be universal for all fields of science is not warranted. Although many fields indeed seem to have fairly similar citation distributions, there are exceptions as well. We also briefly discuss the consequences of our findings for the measurement of scientific impact using citation-based bibliometric indicators.

Type

a

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Abstract

The Leiden Ranking 2011/2012 is a ranking of universities based on bibliometric indicators of publication output, citation impact, and scientific collaboration. The ranking includes 500 major universities from 41 different countries. This paper provides an extensive discussion of the Leiden Ranking 2011/2012. The ranking is compared with other global university rankings, in particular the Academic Ranking of World Universities (commonly known as the Shanghai Ranking) and the Times Higher Education World University Rankings. The comparison focuses on the methodological choices underlying the different rankings. Also, a detailed description is offered of the data collection methodology of the Leiden Ranking 2011/2012 and of the indicators used in the ranking. Various innovations in the Leiden Ranking 2011/2012 are presented. These innovations include (1) an indicator based on counting a university's highly cited publications, (2) indicators based on fractional rather than full counting of collaborative publications, (3) the possibility of excluding non-English language publications, and (4) the use of stability intervals. Finally, some comments are made on the interpretation of the ranking and a number of limitations of the ranking are pointed out.

Type

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Type

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