Search (7 results, page 1 of 1)

0.058472283 = product of:
  0.11694457 = sum of:
    0.11694457 = product of:
      0.23388913 = sum of:
        0.23388913 = weight(_text_:maps in 1131) [ClassicSimilarity], result of:
          0.23388913 = score(doc=1131,freq=14.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.8213004 = fieldWeight in 1131, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1131)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.045934916 = product of:
  0.09186983 = sum of:
    0.09186983 = product of:
      0.18373966 = sum of:
        0.18373966 = weight(_text_:maps in 3987) [ClassicSimilarity], result of:
          0.18373966 = score(doc=3987,freq=6.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.6452008 = fieldWeight in 3987, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=3987)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We present a novel approach to visually locate bodies of research within the sciences, both at each moment of time and dynamically. This article describes how this approach fits with other efforts to locally and globally map scientific outputs. We then show how these science overlay maps help benchmarking, explore collaborations, and track temporal changes, using examples of universities, corporations, funding agencies, and research topics. We address their conditions of application and discuss advantages, downsides, and limitations. Overlay maps especially help investigate the increasing number of scientific developments and organizations that do not fit within traditional disciplinary categories. We make these tools available online to enable researchers to explore the ongoing sociocognitive transformations of science and technology systems.

0.038279098 = product of:
  0.076558195 = sum of:
    0.076558195 = product of:
      0.15311639 = sum of:
        0.15311639 = weight(_text_:maps in 1543) [ClassicSimilarity], result of:
          0.15311639 = score(doc=1543,freq=6.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.53766733 = fieldWeight in 1543, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1543)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper presents a new global patent map that represents all technological categories and a method to locate patent data of individual organizations and technological fields on the global map. This overlay map technique may support competitive intelligence and policy decision making. The global patent map is based on similarities in citing-to-cited relationships between categories of the International Patent Classification (IPC) of European Patent Office (EPO) patents from 2000 to 2006. This patent data set, extracted from the PATSTAT database, includes 760,000 patent records in 466 IPC-based categories. We compare the global patent maps derived from this categorization to related efforts of other global patent maps. The paper overlays the nanotechnology-related patenting activities of two companies and two different nanotechnology subfields on the global patent map. The exercise shows the potential of patent overlay maps to visualize technological areas and potentially support decision making. Furthermore, this study shows that IPC categories that are similar to one another based on citing-to-cited patterns (and thus close in the global patent map) are not necessarily in the same hierarchical IPC branch, thereby revealing new relationships between technologies that are classified as pertaining to different (and sometimes distant) subject areas in the IPC scheme.

0.026520537 = product of:
  0.053041074 = sum of:
    0.053041074 = product of:
      0.10608215 = sum of:
        0.10608215 = weight(_text_:maps in 4445) [ClassicSimilarity], result of:
          0.10608215 = score(doc=4445,freq=2.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.37250686 = fieldWeight in 4445, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=4445)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Grasping the fruits of "emerging technologies" is an objective of many government priority programs in a knowledge-based and globalizing economy. We use the publication records (in the Science Citation Index) of two emerging technologies to study the mechanisms of diffusion in the case of two innovation trajectories: small interference RNA (siRNA) and nanocrystalline solar cells (NCSC). Methods for analyzing and visualizing geographical and cognitive diffusion are specified as indicators of different dynamics. Geographical diffusion is illustrated with overlays to Google Maps; cognitive diffusion is mapped using an overlay to a map based on the ISI subject categories. The evolving geographical networks show both preferential attachment and small-world characteristics. The strength of preferential attachment decreases over time while the network evolves into an oligopolistic control structure with small-world characteristics. The transition from disciplinary-oriented ("Mode 1") to transfer-oriented ("Mode 2") research is suggested as the crucial difference in explaining the different rates of diffusion between siRNA and NCSC.

0.026520537 = product of:
  0.053041074 = sum of:
    0.053041074 = product of:
      0.10608215 = sum of:
        0.10608215 = weight(_text_:maps in 494) [ClassicSimilarity], result of:
          0.10608215 = score(doc=494,freq=2.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.37250686 = fieldWeight in 494, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=494)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Multiple perspectives on the nonlinear processes of medical innovations can be distinguished and combined using the Medical Subject Headings (MeSH) of the MEDLINE database. Focusing on three main branches-"diseases," "drugs and chemicals," and "techniques and equipment"-we use base maps and overlay techniques to investigate the translations and interactions and thus to gain a bibliometric perspective on the dynamics of medical innovations. To this end, we first analyze the MEDLINE database, the MeSH index tree, and the various options for a static mapping from different perspectives and at different levels of aggregation. Following a specific innovation (RNA interference) over time, the notion of a trajectory which leaves a signature in the database is elaborated. Can the detailed index terms describing the dynamics of research be used to predict the diffusion dynamics of research results? Possibilities are specified for further integration between the MEDLINE database on one hand, and the Science Citation Index and Scopus (containing citation information) on the other.

0.022100445 = product of:
  0.04420089 = sum of:
    0.04420089 = product of:
      0.08840178 = sum of:
        0.08840178 = weight(_text_:maps in 2713) [ClassicSimilarity], result of:
          0.08840178 = score(doc=2713,freq=2.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.31042236 = fieldWeight in 2713, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2713)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The decomposition of scientific literature into disciplinary and subdisciplinary structures is one of the core goals of scientometrics. How can we achieve a good decomposition? 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 matrix (citing versus cited) that is much more densely populated than the underlying matrix at the journal level. Exploratory factor analysis of the matrix of subject categories suggests a 14-factor solution. This solution could be interpreted as the disciplinary structure of science. The nested maps of science (corresponding to 14 factors, 172 categories, and 6,164 journals) are online at http://www.leydesdorff.net/map06. Presumably, inaccuracies in the attribution of journals to the ISI subject categories average out so that the factor analysis reveals the main structures. The mapping of science could, therefore, be comprehensive and reliable on a large scale albeit imprecise in terms of the attribution of journals to the ISI subject categories.

0.022100445 = product of:
  0.04420089 = sum of:
    0.04420089 = product of:
      0.08840178 = sum of:
        0.08840178 = weight(_text_:maps in 3095) [ClassicSimilarity], result of:
          0.08840178 = score(doc=3095,freq=2.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.31042236 = fieldWeight in 3095, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3095)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.