I. K. Ravichandra Rao

Bio: I. K. Ravichandra Rao is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Scientometrics & Informetrics. The author has an hindex of 12, co-authored 45 publications receiving 547 citations. Previous affiliations of I. K. Ravichandra Rao include University of Hasselt & University of Western Ontario.

Classification of growth models based on growth rates and its applications

Abstract: In this paper, growth models are classified and characterised using two types of growth rates: from time t to t+1 and from time t to 2t. They are interesting in themselves but can also be used for a quick prediction of the type of growth model that is valid in a particular case. These ideas are applied on 20 data sets collected byWolfram, Chu andLu. We determine (using the above classification as well as via nonlinear regression techniques) that the power model (with exponent>1) is the best growth model for Sci-Tech online databases, but that Gompertz-S-shaped distribution is the best for social sciences and humanities online databases.

Citation Age Data and the Obsolescence Function: Fits and Explanations.

Abstract: LOUVAIN UNIV CATHOLIC,B-3590 DIEPENBEEK,BELGIUM. RV COLL,CTR DOCUMENTAT RES & TRAINING,BANGALORE 560059,INDIA.EGGHE, L, UNIV INSTELLING ANTWERP,UNIV PLEIN 1,B-2610 WILRIJK,BELGIUM.

The distribution of scientific productivity and social change

Abstract: Results in the literature concerning the probability that an author publishes r articles in time t are reexamined, and it is found that a negative binomial distribution fits scientific productivity data (by the chi-squared goodness-of-fit test) better than many other distributions such as geometric, logarithmic, zeta, cumulative advantage, etc. It is shown analytically that the negative binomial distribution describes a pattern of scientific productivity under the “success-breeds-success” condition in a wide variety of social circumstances.

Study of different h-indices for groups of authors

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. © 2008 Wiley Periodicals, Inc.

An analysis of Bradford multipliers and a model to explain law of scattering

Abstract: In his book on “Documentation”, Bradford derived the law of scattering, based on algebric explanation with the supposition that n1=n2=n. n1 and n2 are computed based on average no. of articles per journals in the first three zones. An analysis of a small sample of 12 data sets, using t-test suggests that it is unlikely that n1=n2. Further an attempt has been made to identify a suitable model to explain the law of scattering; among the various models tried, log-normal fits much better than many models including the log-linear model.

Theory and practise of the g-index

Abstract: The g-index is introduced as an improvement of the h-index of Hirsch to measure the global citation performance of a set of articles. If this set is ranked in decreasing order of the number of citations that they received, the g-index is the (unique) largest number such that the top g articles received (together) at least g 2 citations. We prove the unique existence of g for any set of articles and we have that g ≥ h. The general Lotkaian theory of the g-index is presented and we show that g = (α-1 / α-2) α-1/α T 1/α where a> 2 is the Lotkaian exponent and where T denotes the total number of sources. We then present the g-index of the (still active) Price medallists for their complete careers up to 1972 and compare it with the h-index. It is shown that the g-index inherits all the good properties of the h-index and, in addition, better takes into account the citation scores of the top articles. This yields a better distinction between and order of the scientists from the point of view of visibility.

国际刊物《Scientometrics》文献计量研究

Abstract: 本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

The state of h index research. Is the h index the ideal way to measure research performance

Abstract: How does one measure the quality of science? The question is not rhetorical; it is extremely relevant to promotion committees, funding agencies, national academies and politicians, all of whom need a means by which to recognize and reward good research and good researchers. Identifying high‐quality science is necessary for science to progress, but measuring quality becomes even more important in a time when individual scientists and entire research fields increasingly compete for limited amounts of money. The most obvious measure available is the bibliographic record of a scientist or research institute—that is, the number and impact of their publications. > Identifying high‐quality science is necessary for science to progress… Currently, the tool most widely used to determine the quality of scientific publications is the journal impact factor (IF), which is calculated by the scientific division of Thomson Reuters (New York, NY, USA) and is published annually in the Journal Citation Reports (JCR). The IF itself was developed in the 1960s by Eugene Garfield and Irving H. Sher, who were concerned that simply counting the number of articles a journal published in any given year would miss out small but influential journals in their Science Citation Index (Garfield, 2006). The IF is the average number of times articles from the journal published in the past two years have been cited in the JCR year and is calculated by dividing the number of citations in the JCR year—for example, 2007—by the total number of articles published in the two previous years—2005 and 2006. Owing to the availability and utility of the IF, promotion committees, funding agencies and scientists have taken to using it as a shorthand assessment of the quality of scientists or institutions, rather than only journals. As Garfield has noted, this use of the IF is often necessary, owing to time …