Search (17 results, page 1 of 1)

0.055277795 = product of:
  0.08291669 = sum of:
    0.055512875 = weight(_text_:reference in 2779) [ClassicSimilarity], result of:
      0.055512875 = score(doc=2779,freq=2.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.2696973 = fieldWeight in 2779, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.046875 = fieldNorm(doc=2779)
    0.027403818 = product of:
      0.054807637 = sum of:
        0.054807637 = weight(_text_:database in 2779) [ClassicSimilarity], result of:
          0.054807637 = score(doc=2779,freq=2.0), product of:
            0.20452234 = queryWeight, product of:
              4.042444 = idf(docFreq=2109, maxDocs=44218)
              0.050593734 = queryNorm
            0.26797873 = fieldWeight in 2779, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.042444 = idf(docFreq=2109, maxDocs=44218)
              0.046875 = fieldNorm(doc=2779)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Normalization of citation scores using reference sets based on Web of Science subject categories (WCs) has become an established ("best") practice in evaluative bibliometrics. For example, the Times Higher Education World University Rankings are, among other things, based on this operationalization. However, WCs were developed decades ago for the purpose of information retrieval and evolved incrementally with the database; the classification is machine-based and partially manually corrected. Using the WC "information science & library science" and the WCs attributed to journals in the field of "science and technology studies," we show that WCs do not provide sufficient analytical clarity to carry bibliometric normalization in evaluation practices because of "indexer effects." Can the compliance with "best practices" be replaced with an ambition to develop "best possible practices"? New research questions can then be envisaged.

0.04361504 = product of:
  0.13084511 = sum of:
    0.13084511 = weight(_text_:reference in 3998) [ClassicSimilarity], result of:
      0.13084511 = score(doc=3998,freq=16.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.6356827 = fieldWeight in 3998, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3998)
  0.33333334 = coord(1/3)

Abstract

In this study, reference standards and reference multipliers are suggested as a means to compare the citation impact of earlier research publications in physics (from the period of "Little Science" in the early 20th century) with that of contemporary papers (from the period of "Big Science," beginning around 1960). For the development of time-specific reference standards, the authors determined (a) the mean citation rates of papers in selected physics journals as well as (b) the mean citation rates of all papers in physics published in 1900 (Little Science) and in 2000 (Big Science); this was accomplished by relying on the processes of field-specific standardization in bibliometry. For the sake of developing reference multipliers with which the citation impact of earlier papers can be adjusted to the citation impact of contemporary papers, they combined the reference standards calculated for 1900 and 2000 into their ratio. The use of reference multipliers is demonstrated by means of two examples involving the time adjusted h index values for Max Planck and Albert Einstein.

0.030530527 = product of:
  0.09159158 = sum of:
    0.09159158 = weight(_text_:reference in 1238) [ClassicSimilarity], result of:
      0.09159158 = score(doc=1238,freq=4.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.4449779 = fieldWeight in 1238, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1238)
  0.33333334 = coord(1/3)

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.

0.024672393 = product of:
  0.074017175 = sum of:
    0.074017175 = weight(_text_:reference in 2381) [ClassicSimilarity], result of:
      0.074017175 = score(doc=2381,freq=8.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.35959643 = fieldWeight in 2381, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.03125 = fieldNorm(doc=2381)
  0.33333334 = coord(1/3)

Abstract

All journals that use peer review have to deal with the following question: Does the peer review system fulfill its declared objective to select the best scientific work? We investigated the journal peer-review process at Angewandte Chemie International Edition (AC-IE), one of the prime chemistry journals worldwide, and conducted a citation analysis for Communications that were accepted by the journal (n = 878) or rejected but published elsewhere (n = 959). The results of negative binomial-regression models show that holding all other model variables constant, being accepted by AC-IE increases the expected number of citations by up to 50%. A comparison of average citation counts (with 95% confidence intervals) of accepted and rejected (but published elsewhere) Communications with international scientific reference standards was undertaken. As reference standards, (a) mean citation counts for the journal set provided by Thomson Reuters corresponding to the field chemistry and (b) specific reference standards that refer to the subject areas of Chemical Abstracts were used. When compared to reference standards, the mean impact on chemical research is for the most part far above average not only for accepted Communications but also for rejected (but published elsewhere) Communications. However, average and below-average scientific impact is to be expected significantly less frequently for accepted Communications than for rejected Communications. All in all, the results of this study confirm that peer review at AC-IE is able to select the best scientific work with the highest impact on chemical research.

0.02158834 = product of:
  0.06476502 = sum of:
    0.06476502 = weight(_text_:reference in 1293) [ClassicSimilarity], result of:
      0.06476502 = score(doc=1293,freq=2.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.31464687 = fieldWeight in 1293, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1293)
  0.33333334 = coord(1/3)

Abstract

This Brief Communication discusses the benefits of citation analysis in research evaluation based on Galton's "Wisdom of Crowds" (1907). Citations are based on the assessment of many which is why they can be considered to have some credibility. However, we show that citations are incomplete assessments and that one cannot assume that a high number of citations correlates with a high level of usefulness. Only when one knows that a rarely cited paper has been widely read is it possible to say-strictly speaking-that it was obviously of little use for further research. Using a comparison with "like" data, we try to determine that cited reference analysis allows for a more meaningful analysis of bibliometric data than times-cited analysis.

0.018504292 = product of:
  0.055512875 = sum of:
    0.055512875 = weight(_text_:reference in 4485) [ClassicSimilarity], result of:
      0.055512875 = score(doc=4485,freq=2.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.2696973 = fieldWeight in 4485, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.046875 = fieldNorm(doc=4485)
  0.33333334 = coord(1/3)

Abstract

We submit newly developed citation impact indicators based not on arithmetic averages of citations but on percentile ranks. Citation distributions are-as a rule-highly skewed and should not be arithmetically averaged. With percentile ranks, the citation score of each paper is rated in terms of its percentile in the citation distribution. The percentile ranks approach allows for the formulation of a more abstract indicator scheme that can be used to organize and/or schematize different impact indicators according to three degrees of freedom: the selection of the reference sets, the evaluation criteria, and the choice of whether or not to define the publication sets as independent. Bibliometric data of seven principal investigators (PIs) of the Academic Medical Center of the University of Amsterdam are used as an exemplary dataset. We demonstrate that the proposed family indicators [R(6), R(100), R(6, k), R(100, k)] are an improvement on averages-based indicators because one can account for the shape of the distributions of citations over papers.

0.018504292 = product of:
  0.055512875 = sum of:
    0.055512875 = weight(_text_:reference in 1108) [ClassicSimilarity], result of:
      0.055512875 = score(doc=1108,freq=2.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.2696973 = fieldWeight in 1108, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.046875 = fieldNorm(doc=1108)
  0.33333334 = coord(1/3)

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.

0.015420245 = product of:
  0.046260733 = sum of:
    0.046260733 = weight(_text_:reference in 4919) [ClassicSimilarity], result of:
      0.046260733 = score(doc=4919,freq=2.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.22474778 = fieldWeight in 4919, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4919)
  0.33333334 = coord(1/3)

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.

0.015420245 = product of:
  0.046260733 = sum of:
    0.046260733 = weight(_text_:reference in 2261) [ClassicSimilarity], result of:
      0.046260733 = score(doc=2261,freq=2.0), product of:
        0.205834 = queryWeight, product of:
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.050593734 = queryNorm
        0.22474778 = fieldWeight in 2261, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.0683694 = idf(docFreq=2055, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2261)
  0.33333334 = coord(1/3)

Abstract

Many studies (in information science) have looked at the growth of science. In this study, we reexamine the question of the growth of science. To do this we (a) use current data up to publication year 2012 and (b) analyze the data across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data were analyzed with an advanced statistical technique-segmented regression analysis-which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (a) the number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (b) the number of cited references in the publications of the source items per cited reference year. We looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three essential growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars, and 8 to 9% to 2010.

0.013709504 = product of:
  0.041128512 = sum of:
    0.041128512 = product of:
      0.082257025 = sum of:
        0.082257025 = weight(_text_:22 in 1239) [ClassicSimilarity], result of:
          0.082257025 = score(doc=1239,freq=2.0), product of:
            0.17717063 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050593734 = queryNorm
            0.46428138 = fieldWeight in 1239, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=1239)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

18. 3.2014 19:13:22

0.00913967 = product of:
  0.02741901 = sum of:
    0.02741901 = product of:
      0.05483802 = sum of:
        0.05483802 = weight(_text_:22 in 1431) [ClassicSimilarity], result of:
          0.05483802 = score(doc=1431,freq=2.0), product of:
            0.17717063 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050593734 = queryNorm
            0.30952093 = fieldWeight in 1431, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1431)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

22. 8.2014 17:05:18

0.009134606 = product of:
  0.027403818 = sum of:
    0.027403818 = product of:
      0.054807637 = sum of:
        0.054807637 = weight(_text_:database in 4767) [ClassicSimilarity], result of:
          0.054807637 = score(doc=4767,freq=2.0), product of:
            0.20452234 = queryWeight, product of:
              4.042444 = idf(docFreq=2109, maxDocs=44218)
              0.050593734 = queryNorm
            0.26797873 = fieldWeight in 4767, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.042444 = idf(docFreq=2109, maxDocs=44218)
              0.046875 = fieldNorm(doc=4767)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The methods presented in this paper allow for a statistical analysis revealing centers of excellence around the world using programs that are freely available. Based on Web of Science data (a fee-based database), field-specific excellence can be identified in cities where highly cited papers were published more frequently than can be expected. Compared to the mapping approaches published hitherto, our approach is more analytically oriented by allowing the assessment of an observed number of excellent papers for a city against the expected number. Top performers in output are cities in which authors are located who publish a statistically significant higher number of highly cited papers than can be expected for these cities. As sample data for physics, chemistry, and psychology show, these cities do not necessarily have a high output of highly cited papers.

0.0076121716 = product of:
  0.022836514 = sum of:
    0.022836514 = product of:
      0.045673028 = sum of:
        0.045673028 = weight(_text_:database in 3095) [ClassicSimilarity], result of:
          0.045673028 = score(doc=3095,freq=2.0), product of:
            0.20452234 = queryWeight, product of:
              4.042444 = idf(docFreq=2109, maxDocs=44218)
              0.050593734 = queryNorm
            0.2233156 = fieldWeight in 3095, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.042444 = idf(docFreq=2109, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3095)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Citation data for institutes are generally provided as numbers of citations or as relative citation rates (as, for example, in the Leiden Ranking). These numbers can then be compared between the institutes. This study aims to present a new approach for the evaluation of citation data at the institutional level, based on regression models. As example data, the study includes all articles and reviews from the Web of Science for the publication year 2003 (n?=?886,416 papers). The study is based on an in-house database of the Max Planck Society. The study investigates how much the expected number of citations for a publication changes if it contains the address of an institute. The calculation of the expected values allows, on the one hand, investigating how the citation impact of the papers of an institute appears in comparison with the total of all papers. On the other hand, the expected values for several institutes can be compared with one another or with a set of randomly selected publications. Besides the institutes, the regression models include factors which can be assumed to have a general influence on citation counts (e.g., the number of authors).

0.0076121716 = product of:
  0.022836514 = sum of:
    0.022836514 = product of:
      0.045673028 = sum of:
        0.045673028 = weight(_text_:database in 3432) [ClassicSimilarity], result of:
          0.045673028 = score(doc=3432,freq=2.0), product of:
            0.20452234 = queryWeight, product of:
              4.042444 = idf(docFreq=2109, maxDocs=44218)
              0.050593734 = queryNorm
            0.2233156 = fieldWeight in 3432, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.042444 = idf(docFreq=2109, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3432)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

In November 2014, the Nature Index (NI) was introduced (see http://www.natureindex.com) by the Nature Publishing Group (NPG). The NI comprises the primary research articles published in the past 12 months in a selection of reputable journals. Starting from two short comments on the NI (Haunschild & Bornmann, 2015a, 2015b), we undertake an empirical analysis of the NI using comprehensive country data. We investigate whether the huge efforts of computing the NI are justified and whether the size-dependent NI indicators should be complemented by size-independent variants. The analysis uses data from the Max Planck Digital Library in-house database (which is based on Web of Science data) and from the NPG. In the first step of the analysis, we correlate the NI with other metrics that are simpler to generate than the NI. The resulting large correlation coefficients point out that the NI produces similar results as simpler solutions. In the second step of the analysis, relative and size-independent variants of the NI are generated that should be additionally presented by the NPG. The size-dependent NI indicators favor large countries (or institutions) and the top-performing small countries (or institutions) do not come into the picture.

0.006854752 = product of:
  0.020564256 = sum of:
    0.020564256 = product of:
      0.041128512 = sum of:
        0.041128512 = weight(_text_:22 in 656) [ClassicSimilarity], result of:
          0.041128512 = score(doc=656,freq=2.0), product of:
            0.17717063 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050593734 = queryNorm
            0.23214069 = fieldWeight in 656, 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=656)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

22. 3.2013 19:44:17

0.006854752 = product of:
  0.020564256 = sum of:
    0.020564256 = product of:
      0.041128512 = sum of:
        0.041128512 = weight(_text_:22 in 4681) [ClassicSimilarity], result of:
          0.041128512 = score(doc=4681,freq=2.0), product of:
            0.17717063 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050593734 = 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)
  0.33333334 = coord(1/3)

Date

8. 1.2019 18:22:45

0.005712294 = product of:
  0.017136881 = sum of:
    0.017136881 = product of:
      0.034273762 = sum of:
        0.034273762 = weight(_text_:22 in 4186) [ClassicSimilarity], result of:
          0.034273762 = score(doc=4186,freq=2.0), product of:
            0.17717063 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050593734 = 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)
  0.33333334 = coord(1/3)

Date

22. 1.2011 12:51:07