Bibliometrics, Stylized Facts and the Way Ahead: How to Build Good Social Simulation Models of Science?

This paper discusses how stylized facts derived from bibliometric studies can be used to build social simulation models of science. Based on a list of six stylized facts of science it illustrates how they can be brought into play to consolidate and direct research. Moreover, it discusses challenges such a stylized facts based approach of modeling science has to solve.Bibliometrics, Stylized Facts, Methodology, Model Comparison, Validation

Lotka’s Law and Authorship distribution pattern in Global Synthetic Biology Literature

An attempt is made to examine the authorship distribution in Synthetic Biology (SB) literature and to validate Lotka\u27s law of author productivity. Authors obtained data for this study from the WOS database. A total of 12012 papers with 33151 unique authors has identified , and used for further analysis. Authors calculated the exponents n and c . Researchers employed Kolmogorov-Smirnov (K-S) test of goodness-of-fit to verify the validity of Lotka\u27s Law in SB literature. The results of this study proved that Lotka\u27s Law of author productivity does fit with SB literature based on the calculated values n = -2.45 and c= 0.74

Authorship Pattern and Research Collaboration of Journal of Informetrics

The study highlights the authorship pattern and research collaboration in the area of Informetrics based on 420 scholarly communications appeared in the Journal of Informetrics during 2007 to 2013. Study illustrates various significant aspects like –types and trends of authorship, author productivity, degree of collaboration,collaborative index, geographical diffusion and institutional diversification of authorship. Findings suggest tangible growth of Informetrics literature over the years with predominantly multi-authored contributions. Result also show that Informetrics research is unevenly scattered among 251 institutions from 38 countries around the globe

Empirical Examination of Lotka’s Law for Information Science and Library Science

The paper presents a bibliometric study on the fit of Lotka’s law on Information Science & Library Science journals indexed in Social Science Citation Index of Journal Citation Report from the period 1956 to 2014. The parameters of the Lotka's law model, C and α, were found using the linear least squares method and the Kolmogorov-Smirnov test was applied to estimate the kindness of adjustment of the results to the Lotka’s distribution. It was found that the pattern of publication of the LIS category articles fits to Lotka’s law

Empirical Examination of Lotka’s Law for Information Science and Library Science

The paper presents a bibliometric study on the fit of Lotka’s law on Information Science & Library Science journals indexed in Social Science Citation Index of Journal Citation Report from the period 1956 to 2014. The parameters of the Lotka's law model, C and α, were found using the linear least squares method and the Kolmogorov-Smirnov test was applied to estimate the kindness of adjustment of the results to the Lotka’s distribution. It was found that the pattern of publication of the LIS category articles fits to Lotka’s law

Applicability of Lotka’s Law in Parasitology research output of India

This paper examines the conformity of Lotka’s law to authorship distribution in the field of parasitology using Scopus during 2007-2016. Totally, 5792 articles produced by 3473 unique first authors, was compiled for analysis. Lotka’s law was tested using both generalized and modified forms by using the formula: , the values of the exponent n and the constant c were computed; and Kolmogorov-Smirnov (K-S) and Chi-square tests were applied. The results showed that the Lotka’s law fit to the author productivity distribution pattern in parasitology literature

Journal Productivity in Fishery Science an informetric analysis

Knowledge is a human resource which has the ability to consolidate the valuable results of human thinking and civilization through different times. It is the totality of understanding of nature and its features for improved quality of life of human society. Because of this, knowledge has been increasing in volume, dimension and directions. The term ‘information’ and 'knowledge' are often used as if they are interchangeable. Information is ‘potential knowledge‘ which is converted into knowledge by the integration of memory of human beings. In modern times there is a confusion on knowledge usage. Therefore an understanding of the concept ‘knowledge’ is needed for formulation of strategies in information science

Measures of greatness: A Lotkaian approach to literary authors using OCLC WorldCat

This study examines the productivity, eminence, and impact of literary authors using Lotka\u27s law, a bibliometric approach developed for studying the published output of scientists. Data on literary authors were drawn from two recent surveys that identified and ranked authors who had made the greatest contributions to world lit- erature. Data on the number of records of works by and about selected authors were drawn from OCLC WorldCat in 2007 and 2014. Findings show that the distribution of literary authors followed a pattern consistent with Lotka\u27s law and show that these studies enable one to empirically test subjective rankings of eminent authors. Future examination of distribution of author productivity might include studies based on language, location, and culture

The productivity of top researchers: A semi-nonparametric approach

Research productivity distributions exhibit heavy tails because it is common for a few researchers to accumulate the majority of the top publications and their corresponding citations. Measurements of this productivity are very sensitive to the field being analyzed and the distribution used. In particular, distributions such as the lognormal distribution seem to systematically underestimate the productivity of the top researchers. In this article, we propose the use of a (log)semi-nonparametric distribution (log-SNP) that nests the lognormal and captures the heavy tail of the productivity distribution through the introduction of new parameters linked to high-order moments. To compare the results, we use research performance data on 140,971 researchers who have produced 253,634 publications in 18 fields of knowledge (O’Boyle and Aguinis, 2012) and show how the log-SNP distribution provides more accurate measures of the performance of the top researchers in their respective fields of knowledge

Application of Lotka’s Law and i10-Index with the Number of Authors of Articles in Chemistry in Iran Published between 2000 and 2020

The primary objective of the current research is to compare Lotka's law of author productivity and the Google Scholar i10-Index with the number of authors and their articles in the field of chemistry in Iran indexed in the Web of Science (WoS) from 2000 to 2020. This study is a descriptive-qualitative type of research that was carried out using the scientometric approach. The statistical population of the present study consisted of all Iranian articles published in the field of chemistry indexed in the Science Citation Index Expanded. Some scientometric software packages were used to analyze the data with Lotka’s law and i10-index. The most prolific Iranian authors in chemistry were Mohamadreza Ganjali from the University of Tehran, Majid Heravi from Alzahra University, and Mojtaba Shamsipur from the Razi University of Kermanshah, all being acclaimed scientists in Iran. The results suggest that the validity of Lotka’s law was not confirmed in measuring Iranian authors' productivity in the field of chemistry. However, it is hard to draw a negative conclusion about the validity of Lotka’s law from a single experiment. Moreover, investigating the i10-index revealed that 85% of the Iranian authors with more than one publication have an i10-index. The results also indicated that the validity of Lotka’s law cannot be confirmed, considering the Iranian chemistry papers indexed in the WoS. Furthermore, the results imply that the i10-index closely follows the authors with over one published paper and presents a high capability application in this field as a credible index