Search (270 results, page 2 of 14)

0.012813476 = product of:
  0.025626952 = sum of:
    0.025626952 = product of:
      0.051253904 = sum of:
        0.051253904 = weight(_text_:h in 854) [ClassicSimilarity], result of:
          0.051253904 = score(doc=854,freq=8.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.5489909 = fieldWeight in 854, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.078125 = fieldNorm(doc=854)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Gegenüberstellung der Berechnung des h-Index in den drei Tools mit Beispiel Stephen Hawking (WoS: 59, Scopus: 19, Google Scholar: 76)

Object

h-index

Source

https://dspace.ndlr.ie/jspui/bitstream/10633/27353/9/H%20index%20datasheet.pdf

0.012684694 = product of:
  0.025369387 = sum of:
    0.025369387 = product of:
      0.050738774 = sum of:
        0.050738774 = weight(_text_:h in 477) [ClassicSimilarity], result of:
          0.050738774 = score(doc=477,freq=16.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.54347324 = fieldWeight in 477, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0546875 = fieldNorm(doc=477)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Jorge Hirsch recently proposed the h index to quantify the research output of individual scientists. The new index has attracted a lot of attention in the scientific community. The claim that the h index in a single number provides a good representation of the scientific lifetime achievement of a scientist as well as the (supposed) simple calculation of the h index using common literature databases lead to the danger of improper use of the index. We describe the advantages and disadvantages of the h index and summarize the studies on the convergent validity of this index. We also introduce corrections and complements as well as single-number alternatives to the h index.

Object

H-Index

0.012684694 = product of:
  0.025369387 = sum of:
    0.025369387 = product of:
      0.050738774 = sum of:
        0.050738774 = weight(_text_:h in 1111) [ClassicSimilarity], result of:
          0.050738774 = score(doc=1111,freq=16.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.54347324 = fieldWeight in 1111, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1111)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The h-index can be a useful metric for evaluating a person's output of Internet media. Here I advocate and demonstrate adaption of the h-index and the g-index to the top video content creators on YouTube. The h-index for Internet video media is based on videos and their view counts. The h-index is defined as the number of videos with >=h × 10**5 views. The g-index is defined as the number of videos with >=g × 10**5 views on average. When compared with a video creator's total view count, the h-index and g-index better capture both productivity and impact in a single metric.

Object

h-index

0.012554591 = product of:
  0.025109181 = sum of:
    0.025109181 = product of:
      0.050218362 = sum of:
        0.050218362 = weight(_text_:h in 2859) [ClassicSimilarity], result of:
          0.050218362 = score(doc=2859,freq=12.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.537899 = fieldWeight in 2859, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0625 = fieldNorm(doc=2859)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We analyze the publication output of 119 Chilean ecologists and find strong evidence that self-citations significantly affect the h-index increase. Furthermore, we show that the relationship between the increase in the h-index and the proportion of self-citations differs between high and low h-index researchers. In particular, our results show that it is in the low h-index group where self-citations cause the greater impact.

Object

h-Index

0.0121559305 = product of:
  0.024311861 = sum of:
    0.024311861 = product of:
      0.048623722 = sum of:
        0.048623722 = weight(_text_:h in 40) [ClassicSimilarity], result of:
          0.048623722 = score(doc=40,freq=20.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.5208185 = fieldWeight in 40, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.046875 = fieldNorm(doc=40)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The h-index is a popular bibliometric indicator for assessing individual scientists. We criticize the h-index from a theoretical point of view. We argue that for the purpose of measuring the overall scientific impact of a scientist (or some other unit of analysis), the h-index behaves in a counterintuitive way. In certain cases, the mechanism used by the h-index to aggregate publication and citation statistics into a single number leads to inconsistencies in the way in which scientists are ranked. Our conclusion is that the h-index cannot be considered an appropriate indicator of a scientist's overall scientific impact. Based on recent theoretical insights, we discuss what kind of indicators can be used as an alternative to the h-index. We pay special attention to the highly cited publications indicator. This indicator has a lot in common with the h-index, but unlike the h-index it does not produce inconsistent rankings.

Object

h-index

0.011549911 = product of:
  0.023099823 = sum of:
    0.023099823 = product of:
      0.046199646 = sum of:
        0.046199646 = weight(_text_:h in 1968) [ClassicSimilarity], result of:
          0.046199646 = score(doc=1968,freq=26.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4948537 = fieldWeight in 1968, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1968)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

J.E. Hirsch (2005) introduced the h-index to quantify an individual's scientific research output by the largest number h of a scientist's papers that received at least h citations. To take into account the highly skewed frequency distribution of citations, L. Egghe (2006a) proposed the g-index as an improvement of the h-index. I have worked out 26 practical cases of physicists from the Institute of Physics at Chemnitz University of Technology, and compare the h and g values in this study. It is demonstrated that the g-index discriminates better between different citation patterns. This also can be achieved by evaluating B.H. Jin's (2006) A-index, which reflects the average number of citations in the h-core, and interpreting it in conjunction with the h-index. h and A can be combined into the R-index to measure the h-core's citation intensity. I also have determined the A and R values for the 26 datasets. For a better comparison, I utilize interpolated indices. The correlations between the various indices as well as with the total number of papers and the highest citation counts are discussed. The largest Pearson correlation coefficient is found between g and R. Although the correlation between g and h is relatively strong, the arrangement of the datasets is significantly different depending on whether they are put into order according to the values of either h or g.

Object

h-Index

0.011532128 = product of:
  0.023064256 = sum of:
    0.023064256 = product of:
      0.04612851 = sum of:
        0.04612851 = weight(_text_:h in 3418) [ClassicSimilarity], result of:
          0.04612851 = score(doc=3418,freq=18.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.49409178 = fieldWeight in 3418, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.046875 = fieldNorm(doc=3418)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Of h-type indices available now, the g-index is an important one in that it not only keeps some advantages of the h-index but also counts citations from highly cited articles. However, the g-index has a drawback that one has to add fictitious articles with zero citation to calculate this index in some important cases. Based on an alternative definition without introducing fictitious articles, an analytical method has been proposed to calculate the g-index based approximately on the h-index and the e-index. If citations for a scientist are ranked by a power law, it is shown that the g-index can be calculated accurately by the h-index, the e-index, and the power parameter. The relationship of the h-, g-, and e-indices presented here shows that the g-index contains the citation information from the h-index, the e-index, and some papers beyond the h-core.

Object

h-index

0.0114607215 = product of:
  0.022921443 = sum of:
    0.022921443 = product of:
      0.045842886 = sum of:
        0.045842886 = weight(_text_:h in 147) [ClassicSimilarity], result of:
          0.045842886 = score(doc=147,freq=10.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4910324 = fieldWeight in 147, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0625 = fieldNorm(doc=147)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

When there are a group of articles and the present time is fixed we can determine the unique number h being the number of articles that received h or more citations while the other articles received a number of citations which is not larger than h. In this article, the time dependence of the h-index is determined. This is important to describe the expected career evolution of a scientist's work or of a journal's production in a fixed year.

0.0114607215 = product of:
  0.022921443 = sum of:
    0.022921443 = product of:
      0.045842886 = sum of:
        0.045842886 = weight(_text_:h in 2351) [ClassicSimilarity], result of:
          0.045842886 = score(doc=2351,freq=10.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4910324 = fieldWeight in 2351, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0625 = fieldNorm(doc=2351)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

A time-dependent h-type indicator is proposed. This indicator depends on the size of the h-core, the number of citations received, and recent change in the value of the h-index. As such, it tries to combine in a dynamic way older information about the source (e.g., a scientist or research institute that is evaluated) with recent information.

Object

h-index

0.010985267 = product of:
  0.021970535 = sum of:
    0.021970535 = product of:
      0.04394107 = sum of:
        0.04394107 = weight(_text_:h in 243) [ClassicSimilarity], result of:
          0.04394107 = score(doc=243,freq=12.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.47066164 = fieldWeight in 243, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0546875 = fieldNorm(doc=243)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Based on earlier results about the shifted Lotka function, we prove an implicit functional relation between the Hirsch index (h-index) and the total number of sources (T). It is shown that the corresponding function, h(T), is concavely increasing. Next, we construct an implicit relation between the h-index and the impact factor IF (an average number of items per source). The corresponding function h(IF) is increasing and we show that if the parameter C in the numerator of the shifted Lotka function is high, then the relation between the h-index and the impact factor is almost linear.

Object

h-index

0.010985267 = product of:
  0.021970535 = sum of:
    0.021970535 = product of:
      0.04394107 = sum of:
        0.04394107 = weight(_text_:h in 460) [ClassicSimilarity], result of:
          0.04394107 = score(doc=460,freq=12.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.47066164 = fieldWeight in 460, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0546875 = fieldNorm(doc=460)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

For measuring multilevel impact, we introduce the distributive h-indices, which balance two important components (breadth and strength) of multilevel impact at various citing levels. After exploring the theoretical properties of these indices, we studied two cases: 57 library and information science (LIS) journals and social science research in 38 European countries/territories. Results reveal that there are approximate power-law relations between distributive h-indices and some underlying citation indicators, such as total citations, total citing entities, and the h-index. Distributive h-indices provide comprehensive measures for multilevel impact, and lead to a potential tool for citation analysis, particularly at aggregative levels.

Object

h-index

0.010872594 = product of:
  0.021745188 = sum of:
    0.021745188 = product of:
      0.043490376 = sum of:
        0.043490376 = weight(_text_:h in 6094) [ClassicSimilarity], result of:
          0.043490376 = score(doc=6094,freq=4.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4658342 = fieldWeight in 6094, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.09375 = fieldNorm(doc=6094)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.010872594 = product of:
  0.021745188 = sum of:
    0.021745188 = product of:
      0.043490376 = sum of:
        0.043490376 = weight(_text_:h in 1878) [ClassicSimilarity], result of:
          0.043490376 = score(doc=1878,freq=16.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4658342 = fieldWeight in 1878, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.046875 = fieldNorm(doc=1878)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this article, for any group of authors, we define three different h-indices. First, there is the successive h-index h2 based on the ranked list of authors and their h-indices h1 as defined by Schubert (2007). Next, there is the h-index hP based on the ranked list of authors and their number of publications. Finally, there is the h-index hC based on the ranked list of authors and their number of citations. We present formulae for these three indices in Lotkaian informetrics from which it also follows that h2 < hp < hc. We give a concrete example of a group of 167 authors on the topic optical flow estimation. Besides these three h-indices, we also calculate the two-by-two Spearman rank correlation coefficient and prove that these rankings are significantly related.

Object

h-index

0.010872594 = product of:
  0.021745188 = sum of:
    0.021745188 = product of:
      0.043490376 = sum of:
        0.043490376 = weight(_text_:h in 3594) [ClassicSimilarity], result of:
          0.043490376 = score(doc=3594,freq=4.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4658342 = fieldWeight in 3594, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.09375 = fieldNorm(doc=3594)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Object

h-index

0.010872594 = product of:
  0.021745188 = sum of:
    0.021745188 = product of:
      0.043490376 = sum of:
        0.043490376 = weight(_text_:h in 4990) [ClassicSimilarity], result of:
          0.043490376 = score(doc=4990,freq=4.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4658342 = fieldWeight in 4990, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.09375 = fieldNorm(doc=4990)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Object

h-index

0.010872594 = product of:
  0.021745188 = sum of:
    0.021745188 = product of:
      0.043490376 = sum of:
        0.043490376 = weight(_text_:h in 287) [ClassicSimilarity], result of:
          0.043490376 = score(doc=287,freq=4.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4658342 = fieldWeight in 287, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.09375 = fieldNorm(doc=287)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Object

h-index

0.010872594 = product of:
  0.021745188 = sum of:
    0.021745188 = product of:
      0.043490376 = sum of:
        0.043490376 = weight(_text_:h in 454) [ClassicSimilarity], result of:
          0.043490376 = score(doc=454,freq=4.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4658342 = fieldWeight in 454, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.09375 = fieldNorm(doc=454)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Object

h-index

0.010872594 = product of:
  0.021745188 = sum of:
    0.021745188 = product of:
      0.043490376 = sum of:
        0.043490376 = weight(_text_:h in 683) [ClassicSimilarity], result of:
          0.043490376 = score(doc=683,freq=4.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4658342 = fieldWeight in 683, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.09375 = fieldNorm(doc=683)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Object

h-index

0.010872594 = product of:
  0.021745188 = sum of:
    0.021745188 = product of:
      0.043490376 = sum of:
        0.043490376 = weight(_text_:h in 976) [ClassicSimilarity], result of:
          0.043490376 = score(doc=976,freq=4.0), product of:
            0.09336021 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.03757783 = queryNorm
            0.4658342 = fieldWeight in 976, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.09375 = fieldNorm(doc=976)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Object

h-index

0.01080023 = product of:
  0.02160046 = sum of:
    0.02160046 = product of:
      0.04320092 = sum of:
        0.04320092 = weight(_text_:22 in 5275) [ClassicSimilarity], result of:
          0.04320092 = score(doc=5275,freq=4.0), product of:
            0.13159116 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03757783 = queryNorm
            0.32829654 = fieldWeight in 5275, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=5275)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 16:20:22