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Loet Leydesdorff
University of Amsterdam
946Publications
77H-index
25.6kCitations
Publications 946
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#1Igone Porto-Gomez (University of Deusto)H-Index: 1
#2Jon Mikel Zabala Iturriagagoitia (University of Deusto)H-Index: 8
Last.Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 77
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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,...
#1Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 77
#2Lutz Bornmann (MPG: Max Planck Society)H-Index: 46
Last.Jonathan Adams ('KCL': King's College London)H-Index: 5
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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: ...
#1Robin Haunschild (MPG: Max Planck Society)H-Index: 17
#2Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 77
Last.Werner Marx (MPG: Max Planck Society)H-Index: 23
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Abstract Twitter accounts have already been used in many scientometric studies, but the meaningfulness of the data for societal impact measurements in research evaluation has been questioned. Earlier research focused on social media counts and neglected the interactive nature of the data. We explore a new network approach based on Twitter data in which we compare author keywords to hashtags as indicators of topics. We analyze the topics of tweeted publications and compare them with the topics of...
#1Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 77
#2Lutz Bornmann (MPG: Max Planck Society)H-Index: 46
Last.John Mingers (UKC: University of Kent)H-Index: 51
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The Leiden Rankings can be used for grouping research universities by considering universities which are not significantly different as a homogeneous set. The groups and intergroup relations can be analyzed and visualized using tools from network analysis. Using the so-called “excellence indicator” PPtop-10%—the proportion of the top-10% most-highly-cited papers assigned to a university—we pursue a classification using (i) overlapping stability intervals, (ii) statistical-significance tests, and...
#1Lutz Bornmann (MPG: Max Planck Society)H-Index: 46
#2Alexander Tekles (MPG: Max Planck Society)H-Index: 1
Last.Loet Leydesdorff (UvA: University of Amsterdam)H-Index: 77
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Recently, the integrated impact indicator (I3) was introduced where citations are weighted in accordance with the percentile rank class of each publication in a set of publications. I3 can also be used as a field-normalized indicator. Field-normalization is common practice in bibliometrics, especially when institutions and countries are compared. Publication and citation practices are so different among fields that citation impact is normalized for cross-field comparisons. In this study, we test...
Innovation systems are not bound by administrative or political boundaries. Using information theory, we measure innovation-systemness as synergy among size-classes, postal addresses, and technological classes (NACE-codes) of firm-level data collected by Statistics Italy at different scales. Italy is organized in twenty regions, but there is also a traditional divide between the North and the South of the country. At which levels is how much innovation-systemness indicated? The greatest synergy ...
#1Inga A. IvanovaH-Index: 7
#2Øivind StrandH-Index: 3
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The innovation capacity of a system can be measured as the synergy in interactions among its parts. Synergy can be considered as a consequence of negative entropies among three parts of the system. We analyze the development of synergy value in the Norwegian innovation system in terms of mutual information among geographical, sectorial, and size distributions of firms. We use three different techniques for the evaluation of the evolution of synergy over time: rescaled range analysis, DFT, and ge...
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