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Loet Leydesdorff
University of Amsterdam
Data miningData scienceCitationComputer scienceScientometrics
956Publications
86H-index
30.1kCitations
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#1Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
#1Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 3
Last. Lutz Bornmann (MPG: Max Planck Society)H-Index: 51
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Both “interdisciplinarity” and “synergy” are desirable features from a policy perspective: can surplus be found in the interactions among (disciplinary) bodies of knowledge? We have recently developed measures for “interdisciplinarity” and distinguished these measurements from those of “synergy.” In this study, we analyze three review papers by Judit Bar-Ilan (2001, 2004, and 2008a) in terms of whether they rank high on interdisciplinarity and synergy values among the 130 papers of her œuvre. Re...
1 CitationsSource
#1Iina Hellsten (UvA: University of Amsterdam)H-Index: 17
#2Tobias Opthof (UvA: University of Amsterdam)H-Index: 44
Last. Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
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Abstract The sciences develop as conglomerates of ideas, texts, and agents. In this study, we propose a n-mode network approach to integrate the network matrices containing social, semantic, and epistemic attributes analytically into a single network and visualization. For example, authors, words (e.g., title words and keywords), and knowledge claims can be attributed to publications as units of analysis. This results in a 2-mode document/attributes matrix which stores both the dimensionality in...
2 CitationsSource
#1Gangan PrathapH-Index: 27
#2Somenath Mukherjee (Central Mechanical Engineering Research Institute)
Last. Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
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Abstract The Journal Impact Factor (JIF) is linearly sensitive to self-citations because each self-citation adds to the numerator, whereas the denominator is not affected. Pinski and Narin (1976) Influence Weights (IW) are not or marginally sensitive to these outliers on the main diagonal of a citation matrix and thus provide an alternative to JIFs. Whereas the JIFs are based on raw citation counts normalized by the number of publications in the previous two years, IWs are based on the eigenvect...
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#1Inga Ivanova (HSE: National Research University – Higher School of Economics)H-Index: 3
#2Nataliya Smorodinskaya (RAS: Russian Academy of Sciences)H-Index: 3
Last. Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
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We propose the Modified Economic Complexity Index (MECI) as a possible refinement to two relevant complexity measures: the Economic Complexity index (ECI) and the Fitness and Complexity index (FCI). Both ECI and FCI are used for the evaluation of competitive advantages and growth potentials of countries. ECI and FCI assume bipartite country-network data, whereas MECI provides an ecosystem-based design using technology as a third dimension. We test the three complexity measures with respect to Ba...
1 CitationsSource
#1Iina Hellsten (UvA: University of Amsterdam)H-Index: 17
#2Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
5 CitationsSource
#1Igone Porto-Gomez (University of Deusto)H-Index: 2
#2Jon Mikel Zabala Iturriagagoitia (University of Deusto)H-Index: 11
Last. Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
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Abstract Innovative economies generate new options from geographical, technological, and organizational synergies. These synergies can be indicated as subsystems of negative entropy. Such a reduction of uncertainty favours the climate for innovation. Using information theory and triple helix model of university-industry-government relations, we analyse the Mexican innovation system at national and regional levels in terms of the mutual information flowing between the geographical, technological,...
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#1Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
#2Caroline S. Wagner (OSU: Ohio State University)H-Index: 24
Last. Fred Phillips (UNM: University of New Mexico)H-Index: 1
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3 CitationsSource
#1Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
#2Caroline S. Wagner (OSU: Ohio State University)H-Index: 24
Last. Lutz Bornmann (MPG: Max Planck Society)H-Index: 51
view all 3 authors...
Source
#1Tobias Hecking (University of Duisburg-Essen)H-Index: 8
#2Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
1 CitationsSource
#1Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 86
#2Lutz Bornmann (MPG: Max Planck Society)H-Index: 51
Last. Jonathan Adams ('KCL': King's College London)H-Index: 8
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We propose the I3* indicator as a non-parametric alternative to the journal impact factor (JIF) and h-index. We apply I3* to more than 10,000 journals. The results can be compared with other journal metrics. I3* is a promising variant within the general scheme of non-parametric I3 indicators introduced previously: I3* provides a single metric which correlates with both impact in terms of citations (c) and output in terms of publications (p). We argue for weighting using four percentile classes: ...
1 CitationsSource
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