Search (37 results, page 1 of 2)

0.010663961 = product of:
  0.047987826 = sum of:
    0.019081537 = weight(_text_:of in 5390) [ClassicSimilarity], result of:
      0.019081537 = score(doc=5390,freq=26.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.31146988 = fieldWeight in 5390, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5390)
    0.02890629 = weight(_text_:systems in 5390) [ClassicSimilarity], result of:
      0.02890629 = score(doc=5390,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.24009174 = fieldWeight in 5390, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5390)
  0.22222222 = coord(2/9)

Abstract

Using information theory, we measure innovation systemness as synergy among size-classes, ZIP Codes, and technological classes (NACE-codes) for 8.5 million American companies. The synergy at the national level is decomposed at the level of states, Core-Based Statistical Areas (CBSA), and Combined Statistical Areas (CSA). We zoom in to the state of California and in more detail to Silicon Valley. Our results do not support the assumption of a national system of innovations in the U.S.A. Innovation systems appear to operate at the level of the states; the CBSA are too small, so that systemness spills across their borders. Decomposition of the sample in terms of high-tech manufacturing (HTM), medium-high-tech manufacturing (MHTM), knowledge-intensive services (KIS), and high-tech services (HTKIS) does not change this pattern, but refines it. The East Coast-New Jersey, Boston, and New York-and California are the major players, with Texas a third one in the case of HTKIS. Chicago and industrial centers in the Midwest also contribute synergy. Within California, Los Angeles contributes synergy in the sectors of manufacturing, the San Francisco area in KIS. KIS in Silicon Valley and the Bay Area-a CSA composed of seven CBSA-spill over to other regions and even globally.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.10, S.1108-1123

0.010339408 = product of:
  0.046527337 = sum of:
    0.021999538 = weight(_text_:of in 943) [ClassicSimilarity], result of:
      0.021999538 = score(doc=943,freq=24.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.3591007 = fieldWeight in 943, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=943)
    0.0245278 = weight(_text_:systems in 943) [ClassicSimilarity], result of:
      0.0245278 = score(doc=943,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 943, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=943)
  0.22222222 = coord(2/9)

Abstract

Citation analysis of documents retrieved from the Medline database (at the Web of Knowledge) has been possible only on a case-by-case basis. A technique is presented here for citation analysis in batch mode using both Medical Subject Headings (MeSH) at the Web of Knowledge and the Science Citation Index at the Web of Science (WoS). This freeware routine is applied to the case of "Brugada Syndrome," a specific disease and field of research (since 1992). The journals containing these publications, for example, are attributed to WoS categories other than "cardiac and cardiovascular systems", perhaps because of the possibility of genetic testing for this syndrome in the clinic. With this routine, all the instruments available for citation analysis can now be used on the basis of MeSH terms. Other options for crossing between Medline, WoS, and Scopus are also reviewed.

Object

Web of Knowledge

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.5, S.1076-1080

0.009684435 = product of:
  0.04357996 = sum of:
    0.019052157 = weight(_text_:of in 1110) [ClassicSimilarity], result of:
      0.019052157 = score(doc=1110,freq=18.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.3109903 = fieldWeight in 1110, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1110)
    0.0245278 = weight(_text_:systems in 1110) [ClassicSimilarity], result of:
      0.0245278 = score(doc=1110,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 1110, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=1110)
  0.22222222 = coord(2/9)

Abstract

Using data from the Web of Science (WoS), we analyze the mutual information among university, industry, and government addresses (U-I-G) at the country level for a number of countries. The dynamic evolution of the Triple Helix can thus be compared among developed and developing nations in terms of cross-sectional coauthorship relations. The results show that the Triple Helix interactions among the three subsystems U-I-G become less intensive over time, but unequally for different countries. We suggest that globalization erodes local Triple Helix relations and thus can be expected to have increased differentiation in national systems since the mid-1990s. This effect of globalization is more pronounced in developed countries than in developing ones. In the dynamic analysis, we focus on a more detailed comparison between China and the United States. Specifically, the Chinese Academy of the (Social) Sciences is changing increasingly from a public research institute to an academic one, and this has a measurable effect on China's position in the globalization.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2317-2325

0.008001433 = product of:
  0.036006447 = sum of:
    0.020082738 = weight(_text_:of in 4681) [ClassicSimilarity], result of:
      0.020082738 = score(doc=4681,freq=20.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.32781258 = fieldWeight in 4681, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4681)
    0.015923709 = product of:
      0.031847417 = sum of:
        0.031847417 = weight(_text_:22 in 4681) [ClassicSimilarity], result of:
          0.031847417 = score(doc=4681,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = 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)
  0.22222222 = coord(2/9)

Abstract

A recent publication in Nature reports that public R&D funding is only weakly correlated with the citation impact of a nation's articles as measured by the field-weighted citation index (FWCI; defined by Scopus). On the basis of the supplementary data, we up-scaled the design using Web of Science data for the decade 2003-2013 and OECD funding data for the corresponding decade assuming a 2-year delay (2001-2011). Using negative binomial regression analysis, we found very small coefficients, but the effects of international collaboration are positive and statistically significant, whereas the effects of government funding are negative, an order of magnitude smaller, and statistically nonsignificant (in two of three analyses). In other words, international collaboration improves the impact of research articles, whereas more government funding tends to have a small adverse effect when comparing OECD countries.

Date

8. 1.2019 18:22:45

Source

Journal of the Association for Information Science and Technology. 70(2019) no.2, S.198-201

0.007797851 = product of:
  0.03509033 = sum of:
    0.021820573 = weight(_text_:of in 4186) [ClassicSimilarity], result of:
      0.021820573 = score(doc=4186,freq=34.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.35617945 = fieldWeight in 4186, product of:
          5.8309517 = tf(freq=34.0), with freq of:
            34.0 = termFreq=34.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4186)
    0.013269759 = product of:
      0.026539518 = sum of:
        0.026539518 = weight(_text_:22 in 4186) [ClassicSimilarity], result of:
          0.026539518 = score(doc=4186,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = 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)
  0.22222222 = coord(2/9)

Abstract

The Impact Factors (IFs) of the Institute for Scientific Information suffer from a number of drawbacks, among them the statistics-Why should one use the mean and not the median?-and the incomparability among fields of science because of systematic differences in citation behavior among fields. Can these drawbacks be counteracted by fractionally counting citation weights instead of using whole numbers in the numerators? (a) Fractional citation counts are normalized in terms of the citing sources and thus would take into account differences in citation behavior among fields of science. (b) Differences in the resulting distributions can be tested statistically for their significance at different levels of aggregation. (c) Fractional counting can be generalized to any document set including journals or groups of journals, and thus the significance of differences among both small and large sets can be tested. A list of fractionally counted IFs for 2008 is available online at http:www.leydesdorff.net/weighted_if/weighted_if.xls The between-group variance among the 13 fields of science identified in the U.S. Science and Engineering Indicators is no longer statistically significant after this normalization. Although citation behavior differs largely between disciplines, the reflection of these differences in fractionally counted citation distributions can not be used as a reliable instrument for the classification.

Date

22. 1.2011 12:51:07

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.217-229

0.0031884005 = product of:
  0.028695604 = sum of:
    0.028695604 = weight(_text_:of in 4466) [ClassicSimilarity], result of:
      0.028695604 = score(doc=4466,freq=30.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.46840128 = fieldWeight in 4466, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4466)
  0.11111111 = coord(1/9)

Abstract

Fractional counting of citations can improve on ranking of multidisciplinary research units (such as universities) by normalizing the differences among fields of science in terms of differences in citation behavior. Furthermore, normalization in terms of citing papers abolishes the unsolved questions in scientometrics about the delineation of fields of science in terms of journals and normalization when comparing among different (sets of) journals. Using publication and citation data of seven Korean research universities, we demonstrate the advantages and the differences in the rankings, explain the possible statistics, and suggest ways to visualize the differences in (citing) audiences in terms of a network.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.6, S.1146-1155

0.0028200909 = product of:
  0.025380818 = sum of:
    0.025380818 = weight(_text_:of in 1200) [ClassicSimilarity], result of:
      0.025380818 = score(doc=1200,freq=46.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.41429368 = fieldWeight in 1200, product of:
          6.78233 = tf(freq=46.0), with freq of:
            46.0 = termFreq=46.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1200)
  0.11111111 = coord(1/9)

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.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.2, S.334-351

0.002544205 = product of:
  0.022897845 = sum of:
    0.022897845 = weight(_text_:of in 1108) [ClassicSimilarity], result of:
      0.022897845 = score(doc=1108,freq=26.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.37376386 = fieldWeight in 1108, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1108)
  0.11111111 = coord(1/9)

Abstract

Two methods for comparing impact factors and citation rates across fields of science are tested against each other using citations to the 3,705 journals in the Science Citation Index 2010 (CD-Rom version of SCI) and the 13 field categories used for the Science and Engineering Indicators of the U.S. National Science Board. We compare (a) normalization by counting citations in proportion to the length of the reference list (1/N of references) with (b) rescaling by dividing citation scores by the arithmetic mean of the citation rate of the cluster. Rescaling is analytical and therefore independent of the quality of the attribution to the sets, whereas fractional counting provides an empirical strategy for normalization among sets (by evaluating the between-group variance). By the fairness test of Radicchi and Castellano (), rescaling outperforms fractional counting of citations for reasons that we consider.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2299-2309

0.0024443932 = product of:
  0.021999538 = sum of:
    0.021999538 = weight(_text_:of in 4107) [ClassicSimilarity], result of:
      0.021999538 = score(doc=4107,freq=24.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.3591007 = fieldWeight in 4107, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4107)
  0.11111111 = coord(1/9)

Abstract

Impact factors (and similar measures such as the Scimago Journal Rankings) suffer from two problems: (a) citation behavior varies among fields of science and, therefore, leads to systematic differences, and (b) there are no statistics to inform us whether differences are significant. The recently introduced "source normalized impact per paper" indicator of Scopus tries to remedy the first of these two problems, but a number of normalization decisions are involved, which makes it impossible to test for significance. Using fractional counting of citations-based on the assumption that impact is proportionate to the number of references in the citing documents-citations can be contextualized at the paper level and aggregated impacts of sets can be tested for their significance. It can be shown that the weighted impact of Annals of Mathematics (0.247) is not so much lower than that of Molecular Cell (0.386) despite a five-f old difference between their impact factors (2.793 and 13.156, respectively).

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2365-2369

0.0024443932 = product of:
  0.021999538 = sum of:
    0.021999538 = weight(_text_:of in 1187) [ClassicSimilarity], result of:
      0.021999538 = score(doc=1187,freq=24.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.3591007 = fieldWeight in 1187, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1187)
  0.11111111 = coord(1/9)

Abstract

Using the referencing patterns in articles in Cognitive Science over three decades, we analyze the knowledge base of this literature in terms of its changing disciplinary composition. Three periods are distinguished: (A) construction of the interdisciplinary space in the 1980s, (B) development of an interdisciplinary orientation in the 1990s, and (C) reintegration into "cognitive psychology" in the 2000s. The fluidity and fuzziness of the interdisciplinary delineations in the different visualizations can be reduced and clarified using factor analysis. We also explore newly available routines ("CorText") to analyze this development in terms of "tubes" using an alluvial map and compare the results with an animation (using "Visone"). The historical specificity of this development can be compared with the development of "artificial intelligence" into an integrated specialty during this same period. Interdisciplinarity should be defined differently at the level of journals and of specialties.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.1, S.164-177

0.0024443932 = product of:
  0.021999538 = sum of:
    0.021999538 = weight(_text_:of in 2224) [ClassicSimilarity], result of:
      0.021999538 = score(doc=2224,freq=24.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.3591007 = fieldWeight in 2224, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2224)
  0.11111111 = coord(1/9)

Abstract

We present a novel routine, namely medlineR, based on the R language, that allows the user to match data from Medline/PubMed with records indexed in the ISI Web of Science (WoS) database. The matching allows exploiting the rich and controlled vocabulary of medical subject headings (MeSH) of Medline/PubMed with additional fields of WoS. The integration provides data (e.g., citation data, list of cited reference, list of the addresses of authors' host organizations, WoS subject categories) to perform a variety of scientometric analyses. This brief communication describes medlineR, the method on which it relies, and the steps the user should follow to perform the matching across the two databases. To demonstrate the differences from Leydesdorff and Opthof (Journal of the American Society for Information Science and Technology, 64(5), 1076-1080), we conclude this artcle by testing the routine on the MeSH category "Burgada syndrome."

Object

Web of Science

Source

Journal of the Association for Information Science and Technology. 66(2015) no.10, S.2155-2159

0.0023521183 = product of:
  0.021169065 = sum of:
    0.021169065 = weight(_text_:of in 4919) [ClassicSimilarity], result of:
      0.021169065 = score(doc=4919,freq=32.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.34554482 = fieldWeight in 4919, product of:
          5.656854 = tf(freq=32.0), with freq of:
            32.0 = termFreq=32.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4919)
  0.11111111 = coord(1/9)

Abstract

In bibliometrics, the association of "impact" with central-tendency statistics is mistaken. Impacts add up, and citation curves therefore should be integrated instead of averaged. For example, the journals MIS Quarterly and Journal of the American Society for Information Science and Technology differ by a factor of 2 in terms of their respective impact factors (IF), but the journal with the lower IF has the higher impact. Using percentile ranks (e.g., top-1%, top-10%, etc.), an Integrated Impact Indicator (I3) can be based on integration of the citation curves, but after normalization of the citation curves to the same scale. The results across document sets can be compared as percentages of the total impact of a reference set. Total number of citations, however, should not be used instead because the shape of the citation curves is then not appreciated. I3 can be applied to any document set and any citation window. The results of the integration (summation) are fully decomposable in terms of journals or institutional units such as nations, universities, and so on because percentile ranks are determined at the paper level. In this study, we first compare I3 with IFs for the journals in two Institute for Scientific Information subject categories ("Information Science & Library Science" and "Multidisciplinary Sciences"). The library and information science set is additionally decomposed in terms of nations. Policy implications of this possible paradigm shift in citation impact analysis are specified.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.11, S.2133-2146

0.0023403284 = product of:
  0.021062955 = sum of:
    0.021062955 = weight(_text_:of in 3436) [ClassicSimilarity], result of:
      0.021062955 = score(doc=3436,freq=22.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.34381276 = fieldWeight in 3436, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3436)
  0.11111111 = coord(1/9)

Abstract

The possibilities of using the Arts & Humanities Citation Index (A&HCI) for journal mapping have not been sufficiently recognized because of the absence of a Journal Citations Report (JCR) for this database. A quasi-JCR for the A&HCI ([2008]) was constructed from the data contained in the Web of Science and is used for the evaluation of two journals as examples: Leonardo and Art Journal. The maps on the basis of the aggregated journal-journal citations within this domain can be compared with maps including references to journals in the Science Citation Index and Social Science Citation Index. Art journals are cited by (social) science journals more than by other art journals, but these journals draw upon one another in terms of their own references. This cultural impact in terms of being cited is not found when documents with a topic such as digital humanities are analyzed. This community of practice functions more as an intellectual organizer than a journal.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.4, S.787-801

0.0023284785 = product of:
  0.020956306 = sum of:
    0.020956306 = weight(_text_:of in 1238) [ClassicSimilarity], result of:
      0.020956306 = score(doc=1238,freq=16.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.34207192 = fieldWeight in 1238, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1238)
  0.11111111 = coord(1/9)

Abstract

We introduce the quantitative method named "Reference Publication Year Spectroscopy" (RPYS). With this method one can determine the historical roots of research fields and quantify their impact on current research. RPYS is based on the analysis of the frequency with which references are cited in the publications of a specific research field in terms of the publication years of these cited references. The origins show up in the form of more or less pronounced peaks mostly caused by individual publications that are cited particularly frequently. In this study, we use research on graphene and on solar cells to illustrate how RPYS functions, and what results it can deliver.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.4, S.751-764

0.002277429 = product of:
  0.02049686 = sum of:
    0.02049686 = weight(_text_:of in 3335) [ClassicSimilarity], result of:
      0.02049686 = score(doc=3335,freq=30.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.33457235 = fieldWeight in 3335, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3335)
  0.11111111 = coord(1/9)

Abstract

Using the Scopus dataset (1996-2007) a grand matrix of aggregated journal-journal citations was constructed. This matrix can be compared in terms of the network structures with the matrix contained in the Journal Citation Reports (JCR) of the Institute of Scientific Information (ISI). Because the Scopus database contains a larger number of journals and covers the humanities, one would expect richer maps. However, the matrix is in this case sparser than in the case of the ISI data. This is because of (a) the larger number of journals covered by Scopus and (b) the historical record of citations older than 10 years contained in the ISI database. When the data is highly structured, as in the case of large journals, the maps are comparable, although one may have to vary a threshold (because of the differences in densities). In the case of interdisciplinary journals and journals in the social sciences and humanities, the new database does not add a lot to what is possible with the ISI databases.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.352-369

0.0022314154 = product of:
  0.020082738 = sum of:
    0.020082738 = weight(_text_:of in 2781) [ClassicSimilarity], result of:
      0.020082738 = score(doc=2781,freq=20.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.32781258 = fieldWeight in 2781, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2781)
  0.11111111 = coord(1/9)

Abstract

Using Web of Science data, portfolio analysis in terms of journal coverage can be projected onto a base map for units of analysis such as countries, cities, universities, and firms. The units of analysis under study can be compared statistically across the 10,000+ journals. The interdisciplinarity of the portfolios is measured using Rao-Stirling diversity or Zhang et?al.'s improved measure 2D3. At the country level we find regional differentiation (e.g., Latin American or Asian countries), but also a major divide between advanced and less-developed countries. Israel and Israeli cities outperform other nations and cities in terms of diversity. Universities appear to be specifically related to firms when a number of these units are exploratively compared. The instrument is relatively simple and straightforward, and one can generalize the application to any document set retrieved from the Web of Science (WoS). Further instruction is provided online at http://www.leydesdorff.net/portfolio.

Aid

Web of Science

Source

Journal of the Association for Information Science and Technology. 67(2016) no.3, S.741-748

0.0022314154 = product of:
  0.020082738 = sum of:
    0.020082738 = weight(_text_:of in 3322) [ClassicSimilarity], result of:
      0.020082738 = score(doc=3322,freq=20.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.32781258 = fieldWeight in 3322, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3322)
  0.11111111 = coord(1/9)

Abstract

This paper examines the use of scientometric overlay mapping as a tool of "strategic intelligence" to aid the governing of emerging technologies. We develop an integrative synthesis of different overlay mapping techniques and associated perspectives on technological emergence across geographical, social, and cognitive spaces. To do so, we longitudinally analyze (with publication and patent data) three case studies of emerging technologies in the medical domain. These are RNA interference (RNAi), human papillomavirus (HPV) testing technologies for cervical cancer, and thiopurine methyltransferase (TPMT) genetic testing. Given the flexibility (i.e., adaptability to different sources of data) and granularity (i.e., applicability across multiple levels of data aggregation) of overlay mapping techniques, we argue that these techniques can favor the integration and comparison of results from different contexts and cases, thus potentially functioning as a platform for "distributed" strategic intelligence for analysts and decision makers.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.1, S.214-233

0.0022002053 = product of:
  0.019801848 = sum of:
    0.019801848 = weight(_text_:of in 1241) [ClassicSimilarity], result of:
      0.019801848 = score(doc=1241,freq=28.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.32322758 = fieldWeight in 1241, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1241)
  0.11111111 = coord(1/9)

Abstract

Group-based trajectory modeling (GBTM) is applied to the citation curves of articles in six journals and to all citable items in a single field of science (virology, 24 journals) to distinguish among the developmental trajectories in subpopulations. Can citation patterns of highly-cited papers be distinguished in an early phase as "fast-breaking" papers? Can "late bloomers" or "sleeping beauties" be identified? Most interesting, we find differences between "sticky knowledge claims" that continue to be cited more than 10 years after publication and "transient knowledge claims" that show a decay pattern after reaching a peak within a few years. Only papers following the trajectory of a "sticky knowledge claim" can be expected to have a sustained impact. These findings raise questions about indicators of "excellence" that use aggregated citation rates after 2 or 3 years (e.g., impact factors). Because aggregated citation curves can also be composites of the two patterns, fifth-order polynomials (with four bending points) are needed to capture citation curves precisely. For the journals under study, the most frequently cited groups were furthermore much smaller than 10%. Although GBTM has proved a useful method for investigating differences among citation trajectories, the methodology does not allow us to define a percentage of highly cited papers inductively across different fields and journals. Using multinomial logistic regression, we conclude that predictor variables such as journal names, number of authors, etc., do not affect the stickiness of knowledge claims in terms of citations but only the levels of aggregated citations (which are field-specific).

Source

Journal of the Association for Information Science and Technology. 65(2014) no.4, S.797-811

0.0021201707 = product of:
  0.019081537 = sum of:
    0.019081537 = weight(_text_:of in 532) [ClassicSimilarity], result of:
      0.019081537 = score(doc=532,freq=26.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.31146988 = fieldWeight in 532, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=532)
  0.11111111 = coord(1/9)

Abstract

Using the CD-ROM version of the Science Citation Index 2010 (N = 3,705 journals), we study the (combined) effects of (a) fractional counting on the impact factor (IF) and (b) transformation of the skewed citation distributions into a distribution of 100 percentiles and six percentile rank classes (top-1%, top-5%, etc.). Do these approaches lead to field-normalized impact measures for journals? In addition to the 2-year IF (IF2), we consider the 5-year IF (IF5), the respective numerators of these IFs, and the number of Total Cites, counted both as integers and fractionally. These various indicators are tested against the hypothesis that the classification of journals into 11 broad fields by PatentBoard/NSF (National Science Foundation) provides statistically significant between-field effects. Using fractional counting the between-field variance is reduced by 91.7% in the case of IF5, and by 79.2% in the case of IF2. However, the differences in citation counts are not significantly affected by fractional counting. These results accord with previous studies, but the longer citation window of a fractionally counted IF5 can lead to significant improvement in the normalization across fields.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.1, S.96-107

0.0021201707 = product of:
  0.019081537 = sum of:
    0.019081537 = weight(_text_:of in 2047) [ClassicSimilarity], result of:
      0.019081537 = score(doc=2047,freq=26.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.31146988 = fieldWeight in 2047, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2047)
  0.11111111 = coord(1/9)

Abstract

The BRICS countries (Brazil, Russia, India, China, and South Africa) are notable for their increasing participation in science and technology. The governments of these countries have been boosting their investments in research and development to become part of the group of nations doing research at a world-class level. This study investigates the development of the BRICS countries in the domain of top-cited papers (top 10% and 1% most frequently cited papers) between 1990 and 2010. To assess the extent to which these countries have become important players at the top level, we compare the BRICS countries with the top-performing countries worldwide. As the analyses of the (annual) growth rates show, with the exception of Russia, the BRICS countries have increased their output in terms of most frequently cited papers at a higher rate than the top-cited countries worldwide. By way of additional analysis, we generate coauthorship networks among authors of highly cited papers for 4 time points to view changes in BRICS participation (1995, 2000, 2005, and 2010). Here, the results show that all BRICS countries succeeded in becoming part of this network, whereby the Chinese collaboration activities focus on the US.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.7, S.1507-1513