8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Measures for measures.

      Nature
      Algorithms, Bayes Theorem, Bias (Epidemiology), Bibliometrics, Calibration, Efficiency, Research, standards, Research Personnel

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references3

          • Record: found
          • Abstract: not found
          • Article: not found

          Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Statistical properties of bibliometric indicators: Research group indicator distributions and correlations

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Citation Networks in High Energy Physics

              , , (2002)
              The citation network constituted by the SPIRES data base is investigated empirically. The probability that a given paper in the SPIRES data base has \(k\) citations is well described by simple power laws, \(P(k) \propto k^{-\alpha}\), with \(\alpha \approx 1.2\) for \(k\) less than 50 citations and \(\alpha \approx 2.3\) for 50 or more citations. Two models are presented that both represent the data well, one which generates power laws and one which generates a stretched exponential. It is not possible to discriminate between these models on the present empirical basis. A consideration of citation distribution by subfield shows that the citation patterns of high energy physics form a remarkably homogeneous network. Further, we utilize the knowledge of the citation distributions to demonstrate the extreme improbability that the citation records of selected individuals and institutions have been obtained by a random draw on the resulting distribution.
                Bookmark

                Author and article information

                Journal
                17183295
                10.1038/4441003a

                Chemistry
                Algorithms,Bayes Theorem,Bias (Epidemiology),Bibliometrics,Calibration,Efficiency,Research,standards,Research Personnel

                Comments

                Comment on this article