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16
A Global Map of Science Based on the ISI Subject Categories
, 2007
"... The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cite ..."
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Cited by 45 (11 self)
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The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cited) which is much more densely populated than the underlying matrix at the journal level. Exploratory factor analysis leads us to opt for a fourteen-factor solution. This solution can easily be interpreted as the disciplinary structure of science. The nested maps of science (corresponding to 14 factors, 172 categories, and 6,164 journals) are brought online at
Betweenness Centrality” as an Indicator of the “Interdisciplinarity” of Scientific Journals
- Journal of the American Society for Information Science and Technology
, 2006
"... In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation ..."
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Cited by 45 (10 self)
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In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004, and then also in relation to local citation environments which can be considered as proxies of specialties and disciplines. Betweenness centrality is shown to be an indicator of the interdisciplinarity of journals, but only in local citation environments and after normalization because otherwise the influence of degree centrality (size) overshadows the betweenness-centrality measure. The indicator is applied to a variety of citation environments, including policy-relevant ones like biotechnology and nanotechnology. The values of the indicator remain sensitive to the delineations of the set because of the indicator’s local character. Maps showing 1 interdisciplinarity of journals in terms of betweenness centrality can be drawn using information about journal citation environments which is available online.
Dynamic animations of journal maps: Indicators of structural change and interdisciplinary developments.
- Journal of the American Society for Information Science and Technology
, 2008
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Can scientific journals be classified in terms of aggregated journal–journal citation relations using the journal citation reports?
- Journal of the American Society for Information Science and Technology,
, 2006
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Mapping the Geography of Science: Distribution Patterns and Networks of Relations among Cities and Institutes Journal of the American Society for Information Science & Technology
"... Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. We discuss the pros and cons of various options, and provide software (freeware) for bridging existing ga ..."
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Cited by 17 (3 self)
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Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. We discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices and Scopus, on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI-databases and with Scopus. Pajek enables us to combine the visualization with statistical analysis, whereas the Google Maps and its derivatives provide superior tools at the Internet.
Visualizing the scientific world and its evolution
- Journal of the American Society for Information Science and Technology
, 2006
"... We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees (MSTs) and a two-dimensional map of scientific journals using the database of the Science Citation Index (SCI) during 1994–2001. The structures of constructed MSTs are consistent with ..."
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Cited by 10 (1 self)
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We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees (MSTs) and a two-dimensional map of scientific journals using the database of the Science Citation Index (SCI) during 1994–2001. The structures of constructed MSTs are consistent with the sorting of SCI categories. The map of science is constructed based on our MST results. Such a map shows the relation among various knowledge clusters and their citation properties. The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences, life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to various levels of knowledge domains.
Animating the Development of Social Networks over Time using a Dynamic Extension of Multidimensional Scaling. El Profesional de la Información
, 2008
"... The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stressminimization with developments over time. This ..."
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Cited by 9 (6 self)
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The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stressminimization with developments over time. This dynamic layouter is no longer based on linear interpolation between independent static visualizations, but change over time is used as a parameter in the optimization. Because of our focus on structural change versus stability the attention is shifted from the relational graph to the latent eigenvectors of matrices. The approach is illustrated with animations for the journal citation environments of Social Networks, the (co-)author networks in the carrying community of this journal, and the topical development using relations among its title words. Our results are also compared with animations based on PajekToSVGAnim and SoNIA.
Alternatives to the journal impact factor: I3 and the top-10% (or top-25%?) of the mosthighly cited papers
- Scientometrics
"... The Author(s) 2012. This article is published with open access at Springerlink.com Abstract Journal impact factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over 2 years. However, it has been recognized that citation distribution ..."
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Cited by 5 (3 self)
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The Author(s) 2012. This article is published with open access at Springerlink.com Abstract Journal impact factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over 2 years. However, it has been recognized that citation distributions vary among fields of science and that one needs to normalize for this. Furthermore, the mean—or any central-tendency statistics—is not a good representation of the citation distribution because these distributions are skewed. Important steps have been taken to solve these two problems during the last few years. First, one can normalize at the article level using the citing audience as the reference set. Second, one can use non-parametric statistics for testing the significance of differences among ratings. A proportion of most-highly cited papers (the top-10 % or top-quartile) on the basis of fractional counting of the citations may provide an alternative to the current IF. This indicator is intuitively simple, allows for statistical testing, and accords with the state of the art.
Classification of the scientific network using aggregated journal-journal citation relations in the Journal Citation Reports: Supporting information. Retrieved July 4, 2008, from http://www.phy.ntnu. edu.tw/∼cchen/paper/cluster.htm
- Journal of the American Society for Information Science
, 1985
"... I propose an approach to classifying scientific networks in terms of aggregated journal-journal citation relations of the ISI Journal Citation Reports using the affinity propagation method. This algorithm is applied to obtain the classification of SCI and SSCI journals by minimiz-ing intracategory j ..."
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Cited by 1 (0 self)
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I propose an approach to classifying scientific networks in terms of aggregated journal-journal citation relations of the ISI Journal Citation Reports using the affinity propagation method. This algorithm is applied to obtain the classification of SCI and SSCI journals by minimiz-ing intracategory journal-journal (J-J) distances in the database, where distance between journals is calculated from the similarity of their annual citation patterns with a cutoff parameter, t, to restrain the maximal J-J distance. As demonstrated in the classification of SCI journals, classification of scientific networks with different reso-lution is possible by choosing proper values of t. Twenty journal categories in SCI are found to be stable despite a difference of an order of magnitude in t. In our clas-sifications, the level of specificity of a category can be found by looking at its value of DRJ (the average distance of members of a category to its representative journal), and relatedness of category members is implied by the value ofDJ-J (the average J-J distance within a category). Our results are consistent with the ISI classification scheme, and the level of relatedness for most categories in our classification is higher than their counterpart in the ISI classification scheme.