Gazing at the skyline for star scientists

Sidiropoulos, Antonis; Gogoglou, Antonia; Katsaros, Dimitrios; Manolopoulos, Yannis

doi:10.1016/j.joi.2016.04.009

Cited by 27 publications

(10 citation statements)

References 51 publications

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“…Since Spearman coefficient (ρ) is independent of the normality of the data distribution and moreover it can handle ties, in this study has been used Spearman ρ coefficient for the construction of the correlation matrix. 41,9 The correlation matrix (12×12) was formulated and most indicators were found to be significantly correlated (Spearman ρ > 0.8) with each other at a statistical significance level of 0.05, but there were also a few indicators that showed no strong correlation (ρ < 0.5) with the majority of other indicators. Table 5 shows that, the Spearman's ρ correlation coefficient between JCRIF and AIF (after removing self-citations) reaches a statistical significance level with a high correlation, ρ = 0.96.…”

Section: Discussionmentioning

confidence: 99%

Modified Impact Factors

Alguliyev¹,

Alıguliyev²

2017

J. Scientomet Res.

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Section: Discussionmentioning

confidence: 99%

Modified Impact Factors

Alguliyev¹,

Alıguliyev²

2017

J. Scientomet Res.

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“…Methods for determining the characteristics of top performers proliferate, and they are studied as individual scientists or scientists embedded in organizational contexts, with reciprocal relationships: how they influence and how they are influenced by their organizations or collaborative networks. The skyline for star scientists (Sidiropoulos et al 2016 ) is being sought: stars are those scientists whose performance cannot be surpassed by others with respect to all scientometric indexes selected. Apart from stars, the relevant studies focus on the scientific elite or the most highly cited scientists (Parker et al 2010 , 2013 ), top researchers (Abramo et al 2013 ; Cortés et al 2016 ), the academic elite (Yin and Zhi 2017 ), or prolific professors (Piro et al 2016 ).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

High research productivity in vertically undifferentiated higher education systems: Who are the top performers?

Kwiek¹

2018

Scientometrics

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The growing scholarly interest in research top performers comes from the growing policy interest in research top performance itself. A question emerges: what makes someone a top performer? In this paper, the upper 10% of Polish academics in terms of research productivity are studied, and predictors of entering this class are sought. In the science system (and Poland follows global patterns), a small number of scholars produce most of the works and attract huge numbers of citations. Performance determines rewards, and small differences in talent translate into a disproportionate level of success, leading to inequalities in resources, research outcomes, and rewards. Top performers are studied here through a bivariate analysis of their working time distribution and their academic role orientation, as well as through a model approach. Odds ratio estimates with logistic regression of being highly productive Polish academics are presented. Consistently across major clusters of academic disciplines, the tiny minority of 10% of academics produces about half (44.7%) of all Polish publications (48.0% of publications in English and 57.2% of internationally co-authored publications). The mean research productivity of top performers across major clusters is on average 7.3 times higher than that of the other academics, and in terms of internationally co-authored publications, 12.07 times higher. High inequality was observed: the average research productivity distribution is highly skewed with a long tail on the right not only for all Polish academics but also for top performers. The class of top performers is as internally stratified as that of their lower-performing colleagues. Separate regression models for all academics, science, technology, engineering and mathematics academics, and social sciences and humanities academics are built based on a large national sample (2525 usable observations), and implications are discussed.

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“…Given the high correlation amongst bibliometric performance metrics and based on proposed categorizations of those metrics in [10], [36], we opted for three straightforward "raw" metrics to represent both impact and productivity (feature set F 1): citation count (c) to represent cumulative impact, h-index (h) for ranked output, and citation rate (c/p, where p is the number of publications) to account for impact normalized over productivity. Figure 2 depicts the coefficient of variation (βCV ) [37] for various contemplated combinations of features and number of clusters k. When βCV curve stabilizes, it is expected that the variabilities in the intra and inter-cluster distances remain stable, implying that adding more clusters should be of little help to understand the dataset variability.…”

Section: Defining Clusters Of Peersmentioning

confidence: 99%

A Data-Driven Unified Framework for Predicting Citation Dynamics

Gogoglou

Manolopoulos

2020

IEEE Trans. Big Data

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With the rising interest in predicting the scientific output, various efforts have been made to predict a scientist's h-index or the citation trajectory of a publication. In this work, we employ a dynamic categorization for scientists to ensure at each stage of their careers a comparison amongst their peers and combine this grouping with predictive models to estimate a scientist's future impact, as expressed by citation counts. Moreover, we investigate a wide range of factors identifying their importance in determining the future of science for different performance and academic levels with particular emphasis on features describing a scholar's position in multi-layered collaboration and citation networks. The robustness of the approach is examined on a longitudinal dataset centered around 700,302 data points representing Computer Scientists in various time periods with their complete networks of over 18 million collaboration links and 36 million citations. Our results indicate up to 30% improvement in prediction performance compared to baseline methods along with an average R 2 =0.96 for short term and R 2 =0.91 for long term predictions.

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Gazing at the skyline for star scientists

Cited by 27 publications

References 51 publications

Modified Impact Factors

Modified Impact Factors

High research productivity in vertically undifferentiated higher education systems: Who are the top performers?

A Data-Driven Unified Framework for Predicting Citation Dynamics

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