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

Power-law Distributions in Information Science - Making the Case for Logarithmic Binning

We suggest partial logarithmic binning as the method of choice for uncovering the nature of many distributions encountered in information science (IS). Logarithmic binning retrieves information and trends "not visible" in noisy power-law tails. We also argue that obtaining the exponent from logarithmically binned data using a simple least square method is in some cases warranted in addition to methods such as the maximum likelihood. We also show why often used cumulative distributions can make it difficult to distinguish noise from genuine features, and make it difficult to obtain an accurate power-law exponent of the underlying distribution. The treatment is non-technical, aimed at IS researchers with little or no background in mathematics.Comment: Accepted for publication in JASIS

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

Application of Lotka’s Law in Bell’s palsy (facial paralysis) research output during 2004 -2018.

This paper examines the validity of Lotka’s law of scientific publication productivity of the articles published on Bell’s palsy disease during 2004-2018. Data for this analysis was retrieved from WOS data base of Clarivate analytics. In this study, the straight and complete count of authorship was used. A total of 4039 articles along with 3384 and 14517 authors were identified by using straight and complete count method of authorship respectively. K-S goodness of- fit statistical test were employed to verify the applicability of Lotka’s law. The results showed that, Lotka’s law fits with the data of straight count of authors. While this law doesn’t find fits to complete count authorship\u27s. Hence, it is concluded that Lotka’s law partially fits with Bell’s palsy literature

The Literature Review of Technology Acceptance Model: A Study of the Bibliometric Distributions

Technology acceptance model plays a signified issue in the information systems field since this theory was introduced by Davis in 1989. This paper investigates the features of technology acceptance model literature based on bibliometric method. By searching the ISI Web of knowledge database under the keyword of “technology acceptance model,” 689 articles were retrieved and analyzed though growth of the literatures and citation, document type, publication countries, subject area, keyword analysis are addressed. The distribution of journal paper was also examined using Bradford’s law and Lotka’s law. As the result, this research found that technology acceptance model literature has a steady growth as well as the citations. Relevant articles were concentrating on computer science, information systems, management, information science, and library science. The author productivity distribution data in technology acceptance literature was consistent with Lotka’s law. Furthermore, eight core journals were identified utilizing the Bradford’s law

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

Testing Lotka’s Law and Pattern of Author Productivity in the Scholarly Publications of Artificial Intelligence

Artificial intelligence has changed our day to day life in multitude ways. AI technology is rearing itself as a driving force to be reckoned with in the largest industries in the world. AI has already engulfed our educational system, our businesses and our financial establishments. The future is definite that machines with artificial intelligence will soon be captivating over trained manual work that now is mostly cared by humans. Machines can carry out human-like tasks by new inputs as artificial intelligence makes it possible for machines to learn from experience. AI data from web of science database from 2008 to 2017 have been mapped to depict the average growth rate, relative growth rate, contribution made by authors in the view of research productivity, authorship pattern and collaboration of AI literature. The Lotka’s law on authorship productivity of AI literature has been tested to confirm the applicability of the law to the present data set. A K-S test was applied to measure the degree of agreement between the distribution of the observed set of data against the inverse general power relationship and the theoretical value of α = 2. It is found that the inverse square law of Lotka follow as such

Examining the Scientific Productivity of Authors in Dyslexia Research: A Study Using Lotka’s Law

Dyslexia, or a reading disability, occurs when an individual has significant difficulty with speed and accuracy of word decoding. Comprehension of text and spelling are also affected. The diagnosis of dyslexia involves the use of reading tests, but the continuum of reading performance means that any cut off point is arbitrary. The IQ score does not play a role in the diagnosis of dyslexia. The cognitive difficulties of dyslexics include problems with speech perception, recognizing and manipulating the basic sounds in a language, language memory, and learning the sounds of letters. Dyslexia is a neurological condition with a genetic basis. There are abnormalities in the brains of dyslexic individuals. There are also differences in the electrophysiological and structural characteristics of the brains of dyslexics. Physicians play a particularly important role in recognizing children who are at risk for dyslexia and helping their parents obtain the proper assessment. The fundamental aim of this study was, to analyze the application of Lotka’s law to the research publication, in the field of Dyslexia. The data related to Dyslexia were extracted from web of science database, which is a scientific, citation and indexing service, maintained by Clarivate Analytics. A total of 5182 research publications were published by the researchers, in the field of Dyslexia. The study found out that, the Lotka’s inverse square law is not fit for this data. The study also analyzed the authorship pattern, Collaborative Index (CI), Degree of Collaboration (DC), Co-authorship Index (CAI), Collaborative Co-efficient (CC), Modified Collaborative Co-efficient (MCC), Lotka’s Exponent value, Kolmogorov-Smirnov Test (K-S Test), Relative Growth Rate and Doubling Time

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