Search (18 results, page 1 of 1)

0.015624099 = product of:
  0.10936869 = sum of:
    0.10936869 = weight(_text_:global in 1814) [ClassicSimilarity], result of:
      0.10936869 = score(doc=1814,freq=8.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.552684 = fieldWeight in 1814, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1814)
  0.14285715 = coord(1/7)

Abstract

Using Scopus data, we construct a global map of science based on aggregated journal-journal citations from 1996-2012 (N of journals?=?20,554). This base map enables users to overlay downloads from Scopus interactively. Using a single year (e.g., 2012), results can be compared with mappings based on the Journal Citation Reports at the Web of Science (N?=?10,936). The Scopus maps are more detailed at both the local and global levels because of their greater coverage, including, for example, the arts and humanities. The base maps can be interactively overlaid with journal distributions in sets downloaded from Scopus, for example, for the purpose of portfolio analysis. Rao-Stirling diversity can be used as a measure of interdisciplinarity in the sets under study. Maps at the global and the local level, however, can be very different because of the different levels of aggregation involved. Two journals, for example, can both belong to the humanities in the global map, but participate in different specialty structures locally. The base map and interactive tools are available online (with instructions) at http://www.leydesdorff.net/scopus_ovl.

0.013530868 = product of:
  0.09471607 = sum of:
    0.09471607 = weight(_text_:global in 1131) [ClassicSimilarity], result of:
      0.09471607 = score(doc=1131,freq=6.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.47863844 = fieldWeight in 1131, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1131)
  0.14285715 = coord(1/7)

Abstract

Using the option Analyze Results with the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Sciences Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling's "quadratic entropy" (Izsák & Papp, 1995). Since this indicator of interdisciplinarity is normalized between 0 and 1, interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel, Guillaume, Lambiotte, and Lefebvre's (2008) community-finding algorithms operating on the relations among journals included in the JCR. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started or animated (e.g., using PowerPoint). The "citing" dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal literatures are further discussed: Local and global maps are based on different assumptions and can be expected to serve different purposes for the explanation.

0.011047906 = product of:
  0.07733534 = sum of:
    0.07733534 = weight(_text_:global in 1200) [ClassicSimilarity], result of:
      0.07733534 = score(doc=1200,freq=4.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.39080665 = fieldWeight in 1200, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1200)
  0.14285715 = coord(1/7)

Abstract

Portfolio analysis of the publication profile of a unit of interest, ranging from individuals and organizations to a scientific field or interdisciplinary programs, aims to inform analysts and decision makers about the position of the unit, where it has been, and where it may go in a complex adaptive environment. A portfolio analysis may aim to identify the gap between the current position of an organization and a goal that it intends to achieve or identify competencies of multiple institutions. We introduce a new visual analytic method for analyzing, comparing, and contrasting characteristics of publication portfolios. The new method introduces a novel design of dual-map thematic overlays on global maps of science. Each publication portfolio can be added as one layer of dual-map overlays over 2 related, but distinct, global maps of science: one for citing journals and the other for cited journals. We demonstrate how the new design facilitates a portfolio analysis in terms of patterns emerging from the distributions of citation threads and the dynamics of trajectories as a function of space and time. We first demonstrate the analysis of portfolios defined on a single source article. Then we contrast publication portfolios of multiple comparable units of interest; namely, colleges in universities and corporate research organizations. We also include examples of overlays of scientific fields. We expect that our method will provide new insights to portfolio analysis.

0.009374459 = product of:
  0.06562121 = sum of:
    0.06562121 = weight(_text_:global in 2887) [ClassicSimilarity], result of:
      0.06562121 = score(doc=2887,freq=2.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.3316104 = fieldWeight in 2887, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.046875 = fieldNorm(doc=2887)
  0.14285715 = coord(1/7)

Abstract

Via the Internet, information scientists can obtain costfree access to large databases in the "hidden" or "deep Web." These databases are often structured far more than the Internet domains themselves. The patent database of the U.S. Patent and Trade Office is used in this study to examine the science base of patents in terms of the literature references in these patents. Universitybased patents at the global level are compared with results when using the national economy of the Netherlands as a system of reference. Methods for accessing the online databases and for the visualization of the results are specified. The conclusion is that "biotechnology" has historically generated a model for theorizing about university-industry relations that cannot easily be generalized to other sectors and disciplines.

0.009374459 = product of:
  0.06562121 = sum of:
    0.06562121 = weight(_text_:global in 3704) [ClassicSimilarity], result of:
      0.06562121 = score(doc=3704,freq=2.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.3316104 = fieldWeight in 3704, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.046875 = fieldNorm(doc=3704)
  0.14285715 = coord(1/7)

Abstract

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. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), 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 a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

0.009374459 = product of:
  0.06562121 = sum of:
    0.06562121 = weight(_text_:global in 4445) [ClassicSimilarity], result of:
      0.06562121 = score(doc=4445,freq=2.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.3316104 = fieldWeight in 4445, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.046875 = fieldNorm(doc=4445)
  0.14285715 = coord(1/7)

0.0078120497 = product of:
  0.054684345 = sum of:
    0.054684345 = weight(_text_:global in 6524) [ClassicSimilarity], result of:
      0.054684345 = score(doc=6524,freq=2.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.276342 = fieldWeight in 6524, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6524)
  0.14285715 = coord(1/7)

Abstract

Fields of technoscience like biotechnology develop in a network mode: disciplinary insights from different backgrounds are recombined as competing innovation systems are continuously reshaped. The ongoing process of integration at the European level generates an additional network of transnational collaborations. Using the title words of scientific publications in five core journals of biotechnology, multivariate analysis is used to distinguish between the intellectual organization of the publications in terms of title words and the institutional network in terms of addresses of documents. The interaction among the representation of intellectual space in terms of words and co-words, and the potentially European network system is compared with the document sets with American and Japanese addresses. The European system can also be decomposed in terms of the contributions of member states. Whereas a European vocabulary can be made visible at the global level, this communality disappears by this decomposition. The network effect at the European level can be considered as institutional more than cognitive

0.0078120497 = product of:
  0.054684345 = sum of:
    0.054684345 = weight(_text_:global in 2713) [ClassicSimilarity], result of:
      0.054684345 = score(doc=2713,freq=2.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.276342 = fieldWeight in 2713, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2713)
  0.14285715 = coord(1/7)

0.0078120497 = product of:
  0.054684345 = sum of:
    0.054684345 = weight(_text_:global in 3095) [ClassicSimilarity], result of:
      0.054684345 = score(doc=3095,freq=2.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.276342 = fieldWeight in 3095, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3095)
  0.14285715 = coord(1/7)

Abstract

The aggregated journal-journal citation matrix - based on the Journal Citation Reports (JCR) of the Science Citation Index - can be decomposed by indexers or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glänzel and Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. Algorithmic decompositions, on the other hand, are more heavily skewed towards a relatively small number of categories, while this is deliberately counter-acted upon in the case of content-based classifications. Because of the indexer effects, science policy studies and the sociology of science should be careful when using content-based classifications, which are made for bibliographic disclosure, and not for the purpose of analyzing latent structures in scientific communications. Despite the large differences among them, the four classification schemes enable us to generate surprisingly similar maps of science at the global level. Erroneous classifications are cancelled as noise at the aggregate level, but may disturb the evaluation locally.

0.0078120497 = product of:
  0.054684345 = sum of:
    0.054684345 = weight(_text_:global in 3090) [ClassicSimilarity], result of:
      0.054684345 = score(doc=3090,freq=2.0), product of:
        0.19788647 = queryWeight, product of:
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0395589 = queryNorm
        0.276342 = fieldWeight in 3090, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.002325 = idf(docFreq=807, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3090)
  0.14285715 = coord(1/7)

Abstract

We compare the network of aggregated journal-journal citation relations provided by the Journal Citation Reports (JCR) 2012 of the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) with similar data based on Scopus 2012. First, global and overlay maps were developed for the 2 sets separately. Using fuzzy-string matching and ISSN numbers, we were able to match 10,524 journal names between the 2 sets: 96.4% of the 10,936 journals contained in JCR, or 51.2% of the 20,554 journals covered by Scopus. Network analysis was pursued on the set of journals shared between the 2 databases and the 2 sets of unique journals. Citations among the shared journals are more comprehensively covered in JCR than in Scopus, so the network in JCR is denser and more connected than in Scopus. The ranking of shared journals in terms of indegree (i.e., numbers of citing journals) or total citations is similar in both databases overall (Spearman rank correlation ??>?0.97), but some individual journals rank very differently. Journals that are unique to Scopus seem to be less important-they are citing shared journals rather than being cited by them-but the humanities are covered better in Scopus than in JCR.

0.004737216 = product of:
  0.03316051 = sum of:
    0.03316051 = weight(_text_:ed in 2002) [ClassicSimilarity], result of:
      0.03316051 = score(doc=2002,freq=2.0), product of:
        0.140671 = queryWeight, product of:
          3.5559888 = idf(docFreq=3431, maxDocs=44218)
          0.0395589 = queryNorm
        0.23573098 = fieldWeight in 2002, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5559888 = idf(docFreq=3431, maxDocs=44218)
          0.046875 = fieldNorm(doc=2002)
  0.14285715 = coord(1/7)

Abstract

Using the 138,751 patents filed in 2006 under the Patent Cooperation Treaty, co-classification analysis is pursued on the basis of three- and four-digit codes in the International Patent Classification (IPC, 8th ed.). The co-classifications among the patents enable us to analyze and visualize the relations among technologies at different levels of aggregation. The hypothesis that classifications might be considered as the organizers of patents into classes, and therefore that co-classification patterns - more than co-citation patterns - might be useful for mapping, is not corroborated. The classifications hang weakly together, even at the four-digit level; at the country level, more specificity can be made visible. However, countries are not the appropriate units of analysis because patent portfolios are largely similar in many advanced countries in terms of the classes attributed. Instead of classes, one may wish to explore the mapping of title words as a better approach to visualize the intellectual organization of patents.

0.0022970077 = product of:
  0.016079053 = sum of:
    0.016079053 = product of:
      0.032158107 = sum of:
        0.032158107 = weight(_text_:22 in 1621) [ClassicSimilarity], result of:
          0.032158107 = score(doc=1621,freq=2.0), product of:
            0.13852853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0395589 = queryNorm
            0.23214069 = fieldWeight in 1621, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1621)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

22. 5.2003 19:48:04

0.0022970077 = product of:
  0.016079053 = sum of:
    0.016079053 = product of:
      0.032158107 = sum of:
        0.032158107 = weight(_text_:22 in 4460) [ClassicSimilarity], result of:
          0.032158107 = score(doc=4460,freq=2.0), product of:
            0.13852853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0395589 = queryNorm
            0.23214069 = fieldWeight in 4460, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=4460)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

6.11.2005 19:02:22

0.0022970077 = product of:
  0.016079053 = sum of:
    0.016079053 = product of:
      0.032158107 = sum of:
        0.032158107 = weight(_text_:22 in 2761) [ClassicSimilarity], result of:
          0.032158107 = score(doc=2761,freq=2.0), product of:
            0.13852853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0395589 = queryNorm
            0.23214069 = fieldWeight in 2761, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2761)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

22. 3.2009 19:07:20

0.0022970077 = product of:
  0.016079053 = sum of:
    0.016079053 = product of:
      0.032158107 = sum of:
        0.032158107 = weight(_text_:22 in 4681) [ClassicSimilarity], result of:
          0.032158107 = score(doc=4681,freq=2.0), product of:
            0.13852853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0395589 = queryNorm
            0.23214069 = fieldWeight in 4681, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=4681)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

8. 1.2019 18:22:45

0.0019141734 = product of:
  0.013399213 = sum of:
    0.013399213 = product of:
      0.026798425 = sum of:
        0.026798425 = weight(_text_:22 in 4186) [ClassicSimilarity], result of:
          0.026798425 = score(doc=4186,freq=2.0), product of:
            0.13852853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0395589 = queryNorm
            0.19345059 = fieldWeight in 4186, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4186)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

22. 1.2011 12:51:07

0.0019141734 = product of:
  0.013399213 = sum of:
    0.013399213 = product of:
      0.026798425 = sum of:
        0.026798425 = weight(_text_:22 in 3089) [ClassicSimilarity], result of:
          0.026798425 = score(doc=3089,freq=2.0), product of:
            0.13852853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0395589 = queryNorm
            0.19345059 = fieldWeight in 3089, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3089)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

24. 8.2016 17:53:22

0.0019141734 = product of:
  0.013399213 = sum of:
    0.013399213 = product of:
      0.026798425 = sum of:
        0.026798425 = weight(_text_:22 in 4463) [ClassicSimilarity], result of:
          0.026798425 = score(doc=4463,freq=2.0), product of:
            0.13852853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0395589 = queryNorm
            0.19345059 = fieldWeight in 4463, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4463)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

29. 9.2018 11:22:09