Search

To analyze the diversity of citation distributions to publications in different research topics to investigate the accuracy of size-independent, rank-based indicators. The top percentile-based indicators are the most common indicators of this type, and the evaluations of Japan are the most evident misjudgments.

Social inequalities are ubiquitous, and here we show that the values of the Gini (g) and Kolkata (k) indices, two generic inequality indices, approach each other (starting from g = 0 and k = 0. 5 for equality) as the competitions grow in various social institutions like markets, universities and elections. It is further shown that these two indices become equal and stabilize at a value (at g = k ≃ 0. 8 7) under unrestricted competitions. We propose to view this coincidence of inequality indices as a generalized version of the (more than a) century old 80-20 law of Pareto. Furthermore, the coincidence of the inequality indices noted here is very similar to the ones seen before for self-organized critical (SOC) systems. The observations here, therefore, stand as a quantitative support toward viewing interacting socio-economic systems in the framework of SOC, an idea conjectured for years. [ABSTRACT FROM AUTHOR]

There are different citation habits in the research fields that influence the obsolescence of the research literature. We analyze the distinctive obsolescence of research literature in disciplinary journals in eight scientific subfields based on cited references distribution, as a synchronous approach. We use both negative binomial (NB) and Poisson distributions to capture this obsolescence. The corpus being examined is published in 2019 and covers 22,559 papers citing 872,442 references. Moreover, three measures to analyze the tail of the distribution are proposed: (i) cited reference survival rate, (ii) cited reference mortality rate, and (iii) cited reference percentile. These measures are interesting because the tail of the distribution collects the behavior of the citations at the time when the document starts to get obsolete in the sense that it is little cited (used). As main conclusion, the differences observed in obsolescence are so important even between disciplinary journals in the same subfield, that it would be necessary to use some measure for the tail of the citation distribution, such as those proposed in this paper, when analyzing in an appropriate way the long time impact of a journal. [ABSTRACT FROM AUTHOR]

Scholars have debated whether changes in digital environments have led to greater concentration or dispersal of scientific citations, but this debate has paid little attention to how other changes in the publication environment may impact the commonly used measures of inequality. Using Monte Carlo experiments, we demonstrate that a variety of inequality measures—including the Gini coefficient, the Herfindahl-Hirschman index, and the percentage of articles ever cited—are substantially biased downward by increases in the total number of articles and citations. We propose and validate a resampling-based correction for this “marginals bias” and apply this correction to empirical data on scholarly citation distributions using Web of Science data covering four broad scientific fields (health, humanities, mathematics and the computer sciences, and the social sciences) from 1996 to 2014. We find that in each field the bulk of the apparent decline in citation inequality in recent years is an artifact of marginals bias, as are most apparent interfield differences in citation inequality. Researchers using inequality measures to compare citation distributions and other distributions with many cases at or near the zero-bound should interpret these metrics carefully and account for the influence of changing marginals.

The real data of cumulative citations ln of selected nth paper of individual N papers published by some highly- and moderately-cited individual authors are analyzed to compare Hirsch and Hirsch-type indices h, h1, hf and hm, and citation radii R and Rf from consideration of: (1) the number An of coauthors of the paper, (2) the normalization of citations ln and cumulative fraction lnf of citation of the nth paper by mean and median citations of the citations ln of all Nc cited papers, and (3) the determination of effective rank neff of the lnf citations. Analysis of the ln(n), lnf(n) and lnf(neff) data was also carried out by using a Langmuir-type function l = l0[1-aKn/(1+Kn)], where l denotes the citations ln and lnf of all cited Nc papers arranged in the decreasing order, a is an effectiveness parameter, K is the so-called Langmuir constant, n denotes the rank n or neff of citations and l0 is the value of l when n or neff approaches zero. For a comparison of the publication output of different authors it was found that the hm index is more consistent than other indices, and it can be normalized to account for the publication career of different authors. However, Langmuir-type function is not adequate for comparison of the publication output of different authors because it describes the rank-order distribution patterns satisfactorily in terms of two parameters. To compare the publication output of different authors independent of their career length t, it is suggested to use scaling parameters h/t, hf/t and hm/t.

Although library holding data is constructed upon work format of books, it is less known how much print and electronic books in libraries contribute to the library holding counts. In response, this research is an attempt to explore the distribution of library holding data across work formats and investigate the availability of library print holdings and library electronic holdings for books as constituents of the library holding metric across fields and over time and compared with other book metrics. ISBNs, titles and author names of 119,794 Scopus-indexed book titles across 26 fields were examined for fourteen variables including OCLC Library Holdings, Scopus Citations, Google Books Citations, Goodreads engagements, and Altmetric indicators. There are three major findings: (a) library holdings are a more comprehensively available metric for books (over 97%) than any other metric and could be useful after short time after first edition publication, followed by Google Books, Goodreads and Scopus, respectively; (b) on average electronic holdings are seven times (median three times) more numerous than print holdings and their ratio is growing considerably for more recent books; (c) there is consistent downward trend in average print book holdings, suggesting that library print holding data are cumulative in nature and statistically comparable to formal citations; however, acquisition of electronic books in libraries is inconsistent in distribution plot as well as over time. In sum, the differences between print and electronic holding data are broad making them distinct metrics, suggesting that further research is needed for understanding their implications for book impact assessment. [ABSTRACT FROM AUTHOR]

Dividing papers based on their numbers of citations into several groups constitutes one of the most common research practices in bibliometrics and beyond. However, existing dividing methods are both arbitrary and subject to bias. This article proposes a novel approach to partition highly, medium and lowly cited publications based on their citation distribution. We utilise the whole Web of Science (WoS) dataset to demonstrate how to apply this approach to scholarly datasets and examine the robustness of our algorithm in each of the six disciplines under the WoS dataset. The codes that underlie the algorithm are available online. [ABSTRACT FROM AUTHOR]

The best possible methods are needed to evaluate the scientific excellence of individuals and research groups in order to award positions and distribute research grants with higher efficiency. It is shown here that for the symmetrical distribution of citations of an individual the currently used h-index is approximately half of the square root of the total number of citations, according to the rule of Hirsch. It is also shown that deviations from this “ideal” h-index are common and they are due to deviations in the citation distributions of different individuals. However, those deviations are not characteristic for the scientific excellence of an individual and therefore they lead only to confusion in scientific evaluation. Therefore the h-index is suggested here to be replaced by the k-index. The k-index of an individual is calculated from his/her all independent citations as self-citations cannot be considered as an indication of the excellence of any paper or its authors (the citation is independent if there is no overlap in the lists of authors of the citing and the cited paper). The k-index takes into account only partial citations for each author of multi-authored papers. In ideal case the shares of the authors in a paper are published in the same paper similarly as shares of the inventors are published in patents. If not, the share of each co-author is taken equal to the inverse of the number of authors of the given paper. The k-index of an individual is defined as the square root from the sum of his/her independent partial citations. The value of the k-index is dependent on the databank used for the citations and on the time of the measurement. If scientists of similar age working in similar fields are compared using the same databank, their personal scientific excellence will be proportional to their k-index. When the k-index is divided by the number of active scientific years, a correction can be made for different ages of different applicants. In average, the k-index has similar values, but a wider range compared to the h-index. More importantly the k-index is not biased by this or that type of citation distribution of an individual, not biased by the self-citations and not biased by the results of the co-authors. The squares of k-indexes of smaller units are additive, and so the k-index is extended to journals, publishing houses, departments, institutions, countries, continents and to the mankind.

Objective: This study evaluated the bibliometric data of the most common publication types of the neuroimaging literature.Methods: PubMed was searched to identify all published papers with “neuroimaging” as their MeSH Major Topics, and they were further searched by the following publication types: case report, clinical trial, comparative study, editorial, evaluation study, guideline, meta-analysis, multicenter study, randomized controlled trial, review, technical report, and validation study. The proportion of papers belonging to each publication type published in neuroimaging journals was calculated. Year-adjusted mean citation counts for each publication type were computed using data from Web of Science. Publication trend and its correlation with citation performance were assessed.Results: Review and comparative study were the most common publication types. Publication types with the highest proportion in neuroimaging journals were guideline, validation study, and technical reports. Since the year 2000, multicenter study, review, and meta-analysis showed the strongest linear increase in annual publication count. These publication types also had the highest year-adjusted citation counts (4.7–10.0). Publication types with the lowest year-adjusted citation counts were editorial and case report (0.5–1.0). It was estimated that 12.5% of the publications labeled as case reports were incorrectly labeled.Conclusions: Neuroimaging literature has been expanding with papers of higher levels of evidence, such as meta-analyses, multicenter studies, and randomized controlled trials.

Skewed citation distribution is a major limitation of the Journal Impact Factor (JIF) representing an outlier-sensitive mean citation value per journal The present study focuses primarily on this phenomenon in the medical literature by investigating a total of n = 982 journals from two medical categories of the Journal Citation Report (JCR). In addition, the three highest-ranking journals from each JCR category were included in order to extend the analyses to non-medical journals. For the journals in these cohorts, the citation data (2018) of articles published in 2016 and 2017 classified as citable items (CI) were analysed using various descriptive approaches including e.g. the skewness, the Gini coefficient, and, the percentage of CI contributing 50% or 90% of the journal's citations. All of these measures clearly indicated an unequal, skewed distribution with highly-cited articles as outliers. The %CI contributing 50% or 90% of the journal's citations was in agreement with previously published studies with median values of 13–18% CI or 44–60% CI generating 50 or 90% of the journal's citations, respectively. Replacing the mean citation values (corresponding to the JIF) with the median to represent the central tendency of the citation distributions resulted in markedly lower numerical values ranging from − 30 to − 50%. Up to 39% of journals showed a median citation number of zero in one medical journal category. For the two medical cohorts, median-based journal ranking was similar to mean-based ranking although the number of possible rank positions was reduced to 13. Correlation of mean citations with the measures of citation inequality indicated that the unequal distribution of citations per journal is more prominent and, thus, relevant for journals with lower citation rates. By using various indicators in parallel and the hitherto probably largest journal sample, the present study provides comprehensive up-to-date results on the prevalence, extent and consequences of citation inequality across medical and all-category journals listed in the JCR. [ABSTRACT FROM AUTHOR]

Citations can be an indicator of publication significance, utility, attention, visibility or short-term impact but analysts need to confirm whether a high citation count for an individual is a genuine reflection of influence or a consequence of extraordinary, even excessive, self-citation. It has recently been suggested there may be increasing misrepresentation of research performance by individuals who self-cite inordinately to achieve scores and win rewards. In this paper we consider self-referencing and self-citing, describe the typical shape of self-citation patterns for carefully curated publication sets authored by 3517 Highly Cited Researchers and quantify the variance in the distribution of self-citation rates within and between all 21 Essential Science Indicators' fields. We describe both a generic level of median self-referencing rates, common to most fields, and a graphical, distribution-driven assessment of excessive self-citation that demarcates a threshold not dependent on statistical tests or percentiles (since for some fields all values are within a central 'normal' range). We describe this graphical procedure for identifying exceptional self-citation rates but emphasize the necessity for expert interpretation of the citation profiles of specific individuals, particularly in fields with atypical self-citation patterns. [ABSTRACT FROM AUTHOR]

Objective: This study evaluated the bibliometric data of the most common publication types of the neuroimaging literature. Methods: PubMed was searched to identify all published papers with "neuroimaging" as their MeSH Major Topics, and they were further searched by the following publication types: case report, clinical trial, comparative study, editorial, evaluation study, guideline, meta-analysis, multicenter study, randomized controlled trial, review, technical report, and validation study. The proportion of papers belonging to each publication type published in neuroimaging journals was calculated. Year-adjusted mean citation counts for each publication type were computed using data from Web of Science. Publication trend and its correlation with citation performance were assessed. Results: Review and comparative study were the most common publication types. Publication types with the highest proportion in neuroimaging journals were guideline, validation study, and technical reports. Since the year 2000, multicenter study, review, and meta-analysis showed the strongest linear increase in annual publication count. These publication types also had the highest year-adjusted citation counts (4.7–10.0). Publication types with the lowest year-adjusted citation counts were editorial and case report (0.5–1.0). It was estimated that 12.5% of the publications labeled as case reports were incorrectly labeled. Conclusions: Neuroimaging literature has been expanding with papers of higher levels of evidence, such as meta-analyses, multicenter studies, and randomized controlled trials. [ABSTRACT FROM AUTHOR]

A model of scientific citation distribution is given. We apply it to understand the role of the Hirsch index as an indicator of scientific publication importance in Mathematics and some related fields. The proposed model is based on a generalization of such well-known distributions as geometric and Sibuya laws. Real data analysis of the Hirsch index and corresponding citation numbers is given.

The relationships among various citation metrics have been probed in multiple scientific research disciplines but not neuroimaging. The aim of the current study was to assess the citation metrics of neuroimaging journals and analyze their relationships. The Journal Citation Reports (JCRs) published by Clarivate Analytics was accessed to extract relevant data for each of the 14 journals from the neuroimaging category. Pearson correlation tests were conducted to test if the citation metrics had significant correlations. Impact factor was positively correlated with citable items (r = 0.717, p = 0.004). Percentage of uncited citable items and percentage of journal self citations were partially negatively correlated with citation distribution, i.e., the percentages of citable items that contributed to 20%, 50% and 80% of total citations. The current study has implied that all the abovementioned metrics should be considered together to provide multi-faceted evaluations instead of using a single metric, at least in the neuroimaging field.

In the double rank analysis of research publications, the local rank position of a country or institution publication is expressed as a function of the world rank position. Excluding some highly or lowly cited publications, the double rank plot fits well with a power law, which can be explained because citations for local and world publications follow lognormal distributions. We report here that the distribution of the number of country or institution publications in world percentiles is a double rank distribution that can be fitted to a power law. Only the data points in high percentiles deviate from it when the local and world μ parameters of the lognormal distributions are very different. The likelihood of publishing very highly cited papers can be calculated from the power law that can be fitted either to the upper tail of the citation distribution or to the percentile-based double rank distribution. The great advantage of the latter method is that it has universal application, because it is based on all publications and not just on highly cited publications. Furthermore, this method extends the application of the well-established percentile approach to very low percentiles where breakthroughs are reported but paper counts cannot be performed. [ABSTRACT FROM AUTHOR]

Reliable methods for the assessment of research success are still in discussion. One method, which uses the likelihood of publishing very highly cited papers, has been validated in terms of Nobel prizes garnered. However, this method cannot be applied widely because it uses the fraction of publications in the upper tail of citation distribution that follows a power law, which includes a low number of publications in most countries and institutions. To achieve the same purpose without restrictions, we have developed the double rank analysis, in which publications that have a low number of citations are also included. By ranking publications by their number of citations from highest to lowest, publications from institutions or countries have two ranking numbers: one for their internal and another one for world positions; the internal ranking number can be expressed as a function of the world ranking number. In log–log double rank plots, a large number of publications fit a straight line; extrapolation allows estimating the likelihood of publishing the highest cited publication. The straight line derives from a power law behavior of the double rank that occurs because citations follow lognormal distributions with values of μ and σ that vary within narrow limits. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology to detect latent referential articles through a universal, citation-based investigation. We discuss articles' dynamic vitality performance, concealed in their citation distributions, in order to understand the mechanisms that govern which articles are likely to be referenced in the future. Articles have diverse vitality performances expressed in the amount of citations obtained in different time periods. Through an examination of the correlation between articles' future citation count and their past citations, we establish the optimal time period during which it is best to forecast articles' future referential possibilities. The results show that the latest 2 years is the optimal time period. In other words, the correlation between the articles' future citation count and their past citation count reaches a maximum value in the most recent 2-year period. The articles with a higher vitality performance in the most recent 2 years have a higher ratio of being cited as references in the future. These results help, not only, in understanding mechanisms of generating references, but also provide an additional indicator for decision makers to evaluate the academic performance of individuals according to their citation performance in the latest 2 years. [ABSTRACT FROM AUTHOR]

Measuring the contribution of each author of a multi-author paper has been a long standing concern. As a possible solution to this, we propose a list of intellectual activities and logistic support activities that might be involved in the production of a research paper. We then develop a quantitative approach to estimate an author’s relative intellectual contribution to a published work. An author’s relative intellectual contribution is calculated as the percent contribution of an author to each intellectual activity involved in the production of the paper multiplied by a weighing factor for each intellectual activity. The relative intellectual contribution calculated in this way can be used to determine the position of an author in the author list of a paper. Second, a corrected citation index for each author, called the T-index, can be calculated by multiplying the relative intellectual contribution by the total citations received by a paper. The proposed approach can be used to measure the impact of an author of a multi-authored paper in a more accurate way than either giving each author full credit or dividing credit equally. Our proposal not only resolves the long standing concern for the fair distribution of each author’s credit depending on his/her contribution, but it will also, hopefully, discourage addition of non-contributing authors to a paper. [ABSTRACT FROM AUTHOR]

This study aims to gain a better understanding of communication patterns in different publication types and the applicability of the Book Citation Index (BKCI) for building indicators for use in both informetrics studies and research evaluation. The authors investigated the differences not only in citation impact between journal and book literature, but also in citation patterns between edited books and their monographic authored counterparts. The complete 2005 volume of the Web of Science Core Collection database including the three journal databases and the BKCI has been processed as source documents. The results of this study show that books are more heterogeneous information sources and addressed to more heterogeneous target groups than journals. Comparatively, the differences between edited and authored books in terms of the citation impact are not so impressive as books versus journals. Advanced models and indicators which have been developed for periodicals also work for books-however with some limitations. [ABSTRACT FROM AUTHOR]

When a research infrastructure is funded and implemented, new information and new publications are created. This new information is the measurable output of discovery process. In this paper, we describe the impact of infrastructure for physics experiments in terms of publications and citations. In particular, we consider the Large Hadron Collider (LHC) experiments (ATLAS, CMS, ALICE, LHCb) and compare them to the Large Electron Positron Collider (LEP) experiments (ALEPH, DELPHI, L3, OPAL) and the Tevatron experiments (CDF, D0). We provide an overview of the scientific output of these projects over time and highlight the role played by remarkable project results in the publication–citation distribution trends. The methodological and technical contributions of this work provide a starting point for the development of a theoretical model of modern scientific knowledge propagation over time. [ABSTRACT FROM AUTHOR]

This paper discusses how to translate the well-confirmed phenomenon of increasing citation of older scientific literature into an argument for the persistent citation impact of older scientific journal articles. Since libraries purchase or subscribe to scientific journal articles in packages consisting of journal-years, the citation impact of past journal-years needs to be assessed separately from that of recent years. The simple and flexible (Bouabid in Scientometrics 88:199-211, 2011. doi:) model, as applied to particular journal-years, is applied and assessed. [ABSTRACT FROM AUTHOR]

Over the last decade, the relationship between interdisciplinarity and scientific impact has been the focus of many bibliometric papers, with diverging results. This paper aims at contributing to this body of research, by analyzing the level of interdisciplinarity, compiled with the Simpson Index, of the top 1% most highly cited papers and of papers with lower citation percentile ranks. Results shows that the top 1% most cited papers exhibit higher levels of interdisciplinarity than papers in other citation rank classes and that this relationship is observed in more than 90% of NSF specialties. This suggests that interdisciplinary research plays a more important role in generating high impact knowledge. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mean-based method may be the most popular linear method for field normalization of citation impact. However, the relatively good but not ideal performance of mean-based method, plus its being a special case of the general scaling method y = kx and the more general affine method y = kx + b, implies that more effective linear methods may exist. Under the idea of making the citation distribution of each field approximate a common reference distribution through the transformation of scaling method and affine method with unknown parameters k and b, we derived the scaling and affine methods under separate unweighted and weighted optimization models for 236 Web of Science subject categories. While the unweighted-optimization-based scaling and affine methods did not show full advantages over mean-based method, the weighted-optimization-based affine method showed a decided advantage over mean-based method along most parts of the distributions. At the same time, the trivial advantage of weighted-optimization-based scaling method over mean-based method indirectly validated the good normalization performance of mean-based method. Based on these results, we conclude that mean-based method is acceptable for general field normalization, but in the face of higher demands on normalization effect, the weighted-optimization-based affine method may be a better choice. [ABSTRACT FROM AUTHOR]

Field normalization is a necessary step in a fair cross-field comparison of citation impact. In practice, mean-based method ( m-score) is the most popular method for field normalization. However, considering that mean-based method only utilizes the central tendency of citation distribution in the normalization procedure and dispersion is also a significant characteristic, an open and important issue is whether alternative normalization methods which take both central tendency and variability into account perform better than mean-based method. With the aim of collapsing citation distributions of different fields into a universal distribution, this study compares the normalization effect of m-score and z-score based on 236 Web of Science (WoS) subject categories. The results show that both m-score and z-score have remarkable normalization effect as compared with raw citations, but neither of them can realize the ideal goal of 'universality of citation distributions'. The results also suggest that m-score is generally preferable to z-score. The essential cause that m-score has an edge over z-score as a whole has a direct relationship with the characteristics of skewed citation distributions in which case m-score is more applicable than z-score. [ABSTRACT FROM AUTHOR]

The citations to a set of academic articles are typically unevenly shared, with many articles attracting few citations and few attracting many. It is important to know more precisely how citations are distributed in order to help statistical analyses of citations, especially for sets of articles from a single discipline and a small range of years, as normally used for research evaluation. This article fits discrete versions of the power law, the lognormal distribution and the hooked power law to 20 different Scopus categories, using citations to articles published in 2004 and ignoring uncited articles. The results show that, despite its popularity, the power law is not a suitable model for collections of articles from a single subject and year, even for the purpose of estimating the slope of the tail of the citation data. Both the hooked power law and the lognormal distributions fit best for some subjects but neither is a universal optimal choice and parameter estimates for both seem to be unreliable. Hence only the hooked power law and discrete lognormal distributions should be considered for subject-and-year-based citation analysis in future and parameter estimates should always be interpreted cautiously. [ABSTRACT FROM AUTHOR]

The distribution of cumulative citations L and contributed citations L f to individual multiauthored papers published by selected authors working in different scientific disciplines is analyzed and discussed using Langmuir-type function: y n = y 0 [1 − αKn /(1 + Kn )], where y n denotes the total number of normalized cumulative citations l n * and normalized contributed citations l n f * received by individual papers of rank n , y 0 is the maximum value of y n when n = 0, α ≥ 1 is an effectiveness parameter, and K is the Langmuir constant related to the dimensionless differential energy Q = ln( KN c ), with N c as the number of papers receiving citations. Relationships between the values of the Langmuir constant K of the distribution function, the number N c of papers of an individual author receiving citations and the effectiveness parameter α of this function, obtained from analysis of the data of rank-size distributions of the authors, are investigated. It was found that: (1) the quantity KN c obtained from the real citation distribution of papers of various authors working in different disciplines is inversely proportional to ( α − 1) with a proportional constant ( KN c ) 0 < 1, (2) the relation KN c = ( KN c ) 0 /( α − 1) also holds for the citation distribution of journals published in countries of two different groups, investigated earlier (Sangwal, K. (2013). Journal of Informetrics, 7, 487–504), and (3) deviations of the real citation distribution from curves predicted by the Langmuir-type function are associated with changing activity of sources of generation of items (citations). [ABSTRACT FROM AUTHOR]

Abstract: The non-citation rate refers to the proportion of papers that do not attract any citation over a period of time following their publication. After reviewing all the related papers in Web of Science, Google Scholar and Scopus database, we find the current literature on citation distribution gives more focus on the distribution of the percentages and citations of papers receiving at least one citation, while there are fewer studies on the time-dependent patterns of the percentage of never-cited papers, on what distribution model can fit their time-dependent patterns, as well as on the factors influencing the non-citation rate. Here, we perform an empirical pilot analysis to the time-dependent distribution of the percentages of never-cited papers in a series of different, consecutive citation time windows following their publication in our selected six sample journals, and study the influence of paper length on the chance of papers’ getting cited. Through the above analysis, the following general conclusions are drawn: (1) a three-parameter negative exponential model can well fit time-dependent distribution curve of the percentages of never-cited papers; (2) in the initial citation time window, the percentage of never-cited papers in each journal is very high. However, as the citation time window becomes wider and wider, the percentage of never-cited papers begins to drop rapidly at first, and then drop more slowly, and the total degree of decline for most of journals is very large; (3) when applying the wider citation time windows, the percentage of never-cited papers for each journal begins to approach a stable value, and after that value, there will be very few changes in these stable percentages, unless we meet a large amount of “Sleeping Beauties” type papers; (4) the length of an paper has a great influence on whether it will be cited or not. [Copyright &y& Elsevier]

The model proposed by Burrell (Information Processing and Management 28:637-645, , Journal of Informetrics 1:16-25, ) to describe the way that an individual author's publication/citation career develops in time is investigated further, the aim being to describe in more detail the form of the citation distribution and the way it evolves over time. Both relative and actual frequency distributions are considered. Theoretical aspects are developed analytically and graphically and then illustrated using small empirical data sets relating to some well-known informetrics scholars. Perhaps surprisingly, it is found that the distribution may well be approximated in some cases by a simple geometric distribution. [ABSTRACT FROM AUTHOR]

The aging of scientific has generally been studied using synchronous approaches, i.e., based on references made by papers. This paper uses a diachronous model based on citations received by papers to study the changes in the life expectancy of three corpus of papers: papers from G6 and BRICS countries, papers published in Science, Nature, Physical Review and the Lancet and all papers divided into four broad fields: medical sciences, natural sciences and engineering, social sciences and arts and humanities. It shows that that: (i) life expectancy is extensively different from a corpus to another and may be either finite or infinite, meaning that the corpus would never be obsolete from a mathematical perspective; (ii) life expectancy for scientific literature has lengthened over the 1980–2000 period; (iii) life expectancy of developed countries’ (G6) literature is on average shorter than that of emerging countries (BRICS). [ABSTRACT FROM AUTHOR]

Highlights: [•] Examples of citation distributions conform in general terms with the stochastic model. [•] Published data from three sources are analysed, covering 15 scientists. [•] The simple geometric model is found useful to identify the approximate h-index. [•] A quasi h-index chart is constructed. [•] The chart allows an approximate h-index to be read-off knowing only the total numbers of papers and citations. [Copyright &y& Elsevier]

Abstract: Evaluating the performance of institutions with different resources is not easy, any citation distribution comparisons are strongly affected by the differences in the number of articles published. The paper introduces a method for comparing citation distributions of research groups that differ in size. The citation distribution of a larger group is reduced by a certain factor and compared with the original distribution of a smaller group. Expected values and tolerance intervals of the reduced set of citations are calculated. A comparison of both distributions can be conveniently viewed in a graph. The size-independent reduced Hirsch index – a function of reducing factor that allows the comparison of groups within a scientific field – is calculated in the same way. The method can be used for comparing groups or units differing in full-time equivalent, funding or the number of researchers, for comparing countries by population, gross domestic product, etc. It is shown that for the calculation of the reduced Hirsch index, the upper part of the original citation distribution is sufficient. The method is illustrated through several case comparisons. [Copyright &y& Elsevier]

The study of citation distribution provides retrospective and prospective picture of the evolving impact of a corpus of publications on knowledge community. All distribution models agree on the rise of the number of citations in the first years following the publication to reach a peak and then tend to be less cited when time passes. However, questions such as how long it will continue being cited and what is objectively the rate of the decline remain unanswered. Built up of simple polynomial function, the proposed model is proven to be suitable to represent the observed citation distribution over time and to interestingly identify with accuracy when the major loss of citations happens. I calculate from the model the ' residual citations' representing the citations kept after a long time period after publication year. I demonstrate that the residual citations may be greater than or equal to zero, meaning that the 'life-cycle' of the corpus is infinite, contrary to what some researches termed to be around 21 years. This model fits the observed data from SCI according to R-sq which is greater than 98.9%. Rather, it is very simple and easy to implement and can be used by not highly-skilled scientometric users. Finally, the model serves as a citation predictive tool for a corpus by determining the citations that would obtain at any time of its life-cycle. [ABSTRACT FROM AUTHOR]

The power-law distribution and the Garfield's Law of Concentration of journal citation have long been verified by empirical data. As a relatively new type of reference, the URL references are cited more and more frequently in the scientific papers and their distribution is proved to fit for the Garfield's Law of Concentration too. In this article, we collect three URL references datasets extracted from papers written by researchers belonging to three big research groups : Chinese Academy of Sciences, Max Planck Institute, and the whole Chinese scientific researchers. Through the curve-fitting with SPSS and contrast the results with the judgment standard of power-law distribution, we verify that there also exists power-law distribution in the citation frequency of hostnames in these three URL references datasets. And our experimental results show that the range of power exponent in the journal references and the URL references are different. Started from the concrete empirical procedures and the final experimental results, we analyze four factors that may lead to this difference between journal references and URL references: the sample size, the sampling method, the concentration of citation and the type property of citation. [ABSTRACT FROM AUTHOR]

Abstract: This paper contains the first empirical applications of a novel methodology for comparing the citation distributions of research units working in the same homogeneous field. The paper considers a situation in which the world citation distribution in 22 scientific fields is partitioned into three geographical areas: the U.S., the European Union (EU), and the rest of the world (RW). Given a critical citation level (CCL), we suggest using two real valued indicators to describe the shape of each area''s distribution: a high- and a low-impact measure defined over the set of articles with citations below or above the CCL. It is found that, when the CCL is fixed at the 80th percentile of the world citation distribution, the U.S. performs dramatically better than the EU and the RW according to both indicators in all scientific fields. This superiority generally increases as we move from the incidence to the intensity and the citation inequality aspects of the phenomena in question. Surprisingly, changes observed when the CCL is increased from the 80th to the 95th percentile are of a relatively small order of magnitude. Finally, it is found that international co-authorship increases the high-impact and reduces the low-impact level in the three geographical areas. This is especially the case for the EU and the RW when they cooperate with the U.S. [Copyright &y& Elsevier]

Abstract: This paper introduces a novel methodology for comparing the citation distributions of research units of a certain size working in the same homogeneous field. Given a critical citation level (CCL), we suggest using two real valued indicators to describe the shape of any distribution: a high-impact and a low-impact measure defined over the set of articles with citations above or below the CCL. The key to this methodology is the identification of a citation distribution with an income distribution. Once this step is taken, it is easy to realize that the measurement of low-impact coincides with the measurement of economic poverty. In turn, it is equally natural to identify the measurement of high-impact with the measurement of a certain notion of economic affluence. On the other hand, it is seen that the ranking of citation distributions according to a family of low-impact measures is essentially characterized by a number of desirable axioms. Appropriately redefined, these same axioms lead to the selection of an equally convenient class of decomposable high-impact measures. These two families are shown to satisfy other interesting properties that make them potentially useful in empirical applications, including the comparison of research units working in different fields. [Copyright &y& Elsevier]

Abstract: Slovenia’s Current Research Information System (SICRIS) currently hosts 86,443 publications with citation data from 8359 researchers working on the whole plethora of social and natural sciences from 1970 till present. Using these data, we show that the citation distributions derived from individual publications have Zipfian properties in that they can be fitted by a power law , with between 2.4 and 3.1 depending on the institution and field of research. Distributions of indexes that quantify the success of researchers rather than individual publications, on the other hand, cannot be associated with a power law. We find that for Egghe’s -index and Hirsch’s -index the log-normal form applies best, with and depending moderately on the underlying set of researchers. In special cases, particularly for institutions with a strongly hierarchical constitution and research fields with high self-citation rates, exponential distributions can be observed as well. Both indexes yield distributions with equivalent statistical properties, which is a strong indicator for their consistency and logical connectedness. At the same time, differences in the assessment of citation histories of individual researchers strengthen their importance for properly evaluating the quality and impact of scientific output. [Copyright &y& Elsevier]

The tools of aggregation kinetics are applied to the “popularity” phenomena of single-lane traffic clustering, and to the growth of a network that mimics citations of scientific publications. In the latter, the network is built by introducing papers (new nodes) one at a time, with preferential linking to more popular previously existing nodes. From the rate equations, the distribution of node degree, as well as various global properties, can be determined easily. A simple extension of the model appears to describe the degree distributions of the world-wide web. [Copyright &y& Elsevier]