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Power laws in citation distributions: evidence from Scopus.

ABSTRACT: Modeling distributions of citations to scientific papers is crucial for understanding how science develops. However, there is a considerable empirical controversy on which statistical model fits the citation distributions best. This paper is concerned with rigorous empirical detection of power-law behaviour in the distribution of citations received by the most highly cited scientific papers. We have used a large, novel data set on citations to scientific papers published between 1998 and 2002 drawn from Scopus. The power-law model is compared with a number of alternative models using a likelihood ratio test. We have found that the power-law hypothesis is rejected for around half of the Scopus fields of science. For these fields of science, the Yule, power-law with exponential cut-off and log-normal distributions seem to fit the data better than the pure power-law model. On the other hand, when the power-law hypothesis is not rejected, it is usually empirically indistinguishable from most of the alternative models. The pure power-law model seems to be the best model only for the most highly cited papers in "Physics and Astronomy". Overall, our results seem to support theories implying that the most highly cited scientific papers follow the Yule, power-law with exponential cut-off or log-normal distribution. Our findings suggest also that power laws in citation distributions, when present, account only for a very small fraction of the published papers (less than 1 % for most of science fields) and that the power-law scaling parameter (exponent) is substantially higher (from around 3.2 to around 4.7) than found in the older literature.

SUBMITTER: Brzezinski M

PROVIDER: S-EPMC4365275 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Power laws in citation distributions: evidence from Scopus.

Brzezinski Michal M

Scientometrics 20150122 1

Modeling distributions of citations to scientific papers is crucial for understanding how science develops. However, there is a considerable empirical controversy on which statistical model fits the citation distributions best. This paper is concerned with rigorous empirical detection of power-law behaviour in the distribution of citations received by the most highly cited scientific papers. We have used a large, novel data set on citations to scientific papers published between 1998 and 2002 dr ...[more]

PMID: 25821280

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Project description:Measures of allele and haplotype diversity, which are fundamental properties in population genetics, often follow heavy tailed distributions. These measures are of particular interest in the field of hematopoietic stem cell transplant (HSCT). Donor/Recipient suitability for HSCT is determined by Human Leukocyte Antigen (HLA) similarity. Match predictions rely upon a precise description of HLA diversity, yet classical estimates are inaccurate given the heavy-tailed nature of the distribution. This directly affects HSCT matching and diversity measures in broader fields such as species richness. We, therefore, have developed a power-law based estimator to measure allele and haplotype diversity that accommodates heavy tails using the concepts of regular variation and occupancy distributions. Application of our estimator to 6.59 million donors in the Be The Match Registry revealed that haplotypes follow a heavy tail distribution across all ethnicities: for example, 44.65% of the European American haplotypes are represented by only 1 individual. Indeed, our discovery rate of all U.S. European American haplotypes is estimated at 23.45% based upon sampling 3.97% of the population, leaving a large number of unobserved haplotypes. Population coverage, however, is much higher at 99.4% given that 90% of European Americans carry one of the 4.5% most frequent haplotypes. Alleles were found to be less diverse suggesting the current registry represents most alleles in the population. Thus, for HSCT registries, haplotype discovery will remain high with continued recruitment to a very deep level of sampling, but population coverage will not. Finally, we compared the convergence of our power-law versus classical diversity estimators such as Capture recapture, Chao, ACE and Jackknife methods. When fit to the haplotype data, our estimator displayed favorable properties in terms of convergence (with respect to sampling depth) and accuracy (with respect to diversity estimates). This suggests that power-law based estimators offer a valid alternative to classical diversity estimators and may have broad applicability in the field of population genetics.

Project description:It is essential to evaluate the impact of scientific publications through citation analysis in citation indexes. In addition, scientometric measures of social media also should be assessed. These measures include how many times the publications were read, viewed, and downloaded. The present study aimed to assess the scientific output of scholars at Kashan University of Medical Sciences by the end of March 2014 based on scientometric measures of Scopus, ResearchGate, and Mendeley.A survey method was used to study the articles published in Scopus journals by scholars at Kashan University of Medical Sciences by the end of March 2014. The required data were collected from Scopus, ResearchGate, and Mendeley. The data were analyzed with descriptive statistics. Also, the Spearman correlation was used between the number of views of articles in ResearchGate with citation number of the articles in Scopus and reading frequency of the articles in Mendeley with citation number in Scopus were examined using the Spearman correlation in SPSS 16.Five-hundred and thirty-three articles were indexed in the Scopus Citation Database by the end of March 2014. Collectively, those articles were cited 1,315 times. The articles were covered by ResearchGate (74%) more than Mendeley (44%). In addition, 98% of the articles indexed in ResearchGate and 92% of the articles indexed in Mendeley were viewed at least once. The results showed that there was a positive correlation between the number of views of the articles in ResearchGate and Mendeley and the number of citations of the articles in Scopus.Coverage and the number of visitors were higher in ResearchGate than in Mendeley. The increase in the number of views of articles in ResearchGate and Mendeley also increased the number of citations of the papers. Social networks, such as ResearchGate and Mendeley, also can be used as tools for the evaluation of academics and scholars based on the scientific research they have conducted.

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Power laws in citation distributions: evidence from Scopus.

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Power laws in citation distributions: evidence from Scopus.

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