Three new bibliometric indicators/approaches derived from keyword analysis

Wang, Mengyang; Chai, Lihe

doi:10.1007/s11192-018-2768-9

Cited by 70 publications

(54 citation statements)

References 27 publications

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“…However, in the last years there have appeared different studies that try to establish the patterns of the AK over time. These studies use methods mainly based on the frequency of the occurrence of terms over the years, employing in some cases sophisticated methods of normalization, bibliometric indicators and linear regression models (Chang, Huang, & Lin, 2015;Chen & Xiao, 2016;Faust, 2018;M. Wang & Chai, 2018;Xu et al, 2018).…”

Section: Survival Of Author Abstractmentioning

confidence: 99%

Survival analysis of author keywords: An application to the library and information sciences area

Peset

Garzón-Farinós

Moreno

et al. 2019

Asso for Info Science & Tech

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Our purpose is to adapt a statistical method for the analysis of discrete numerical series to the keywords appearing in scientific articles of a given area. As an example, we apply our methodological approach to the study of the keywords in the Library and Information Sciences (LIS) area. Our objective is to detect the new author keywords that appear in a fixed knowledge area in the period of 1 year in order to quantify the probabilities of survival for 10 years as a function of the impact of the journals where they appeared. Many of the new keywords appearing in the LIS field are ephemeral. Actually, more than half are never used again. In general, the terms most commonly used in the LIS area come from other areas. The average survival time of these keywords is approximately 3 years, being slightly higher in the case of words that were published in journals classified in the second quartile of the area. We believe that measuring the appearance and disappearance of terms will allow understanding some relevant aspects of the evolution of a discipline, providing in this way a new bibliometric approach.

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Section: Survival Of Author Abstractmentioning

confidence: 99%

Survival analysis of author keywords: An application to the library and information sciences area

Peset

Garzón-Farinós

Moreno

et al. 2019

Asso for Info Science & Tech

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“…По нашему мнению, частота встречаемости КТ в репрезентативном массиве публикаций является одним из индикаторов интенсивности развития научной темы. Однако частота встречаемости ни в коей мере не может служить критерием оценки значимости научного направления [7].…”

Section: Isiss Rasunclassified

Using the subject ontology of the unified digital space of scientific knowledge in scientometric tasks

Цветкова

Каленов²,

Mokhnacheva³

et al. 2021

Единое Цифровое Пространство Научных Знаний: Проблемы И Решения

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The Formation of a Common digital space of scientific knowledge (CDSSK) is one of the priorities of the modern period and involves the use of unified approaches to the selection of information, its presentation, navigation, and terminological content. When constructing an subject ontology of the scientific direction — «Microbiology» — we applied a method of statistical calculation of the frequency distribution of keywords (KW) in information arrays formed as a result of thematic search for information on Microbiology. We assume that the frequency of KW occurrence can be considered as indicator of the scientific topic development intensity. The results of the study show that the size of the pool of keywords in categories can serve as an indicator that shows the breadth of the spectrum of research diversity, methods and objects: the more keywords in a category, the greater the diversity of research directions.

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“…Novel ideas and breakthroughs are among the central concepts for the science of science. A number of studies propose different ideas to quantify originality in science and technology (Cozzens et al, 2010;Alexander et al, 2013;Rzhetsky et al, 2015;Rotolo et al, 2015;Wang and Chai, 2018;Shibayama and Wang, 2020) or their impact on the other works (Shi et al, 2010;Shahaf et al, 2012;Sinatra et al, 2016;Hutchins et al, 2016;Wesley-Smith et al, 2016;Herrmannova et al, 2018b,a;Zhao et al, 2019;Bornmann et al, 2019;Small et al, 2019). The prediction of breakthroughs, scientific impact and citation counts is a well developed area (Schubert and Schubert, 1997;Garfield et al, 2002;Dietz et al, 2007;Lokker et al, 2008;Shi et al, 2010;Uzzi et al, 2013;Alexander, 2013;Klimek et al, 2016;Tahamtan et al, 2016;McKeown et al, 2016;Clauset et al, 2017;Peoples et al, 2017;Salatino et al, 2018;Dong et al, 2018;Iacopini et al, 2018;Feldman et al, 2018;van den Besselaar and Sandström, 2018;Klavans et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

The impact of preprint servers in the formation of novel ideas

Satish¹,

Yao²,

Drozdov

et al. 2020

Preprint

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We study whether novel ideas in biomedical literature appear first in preprints or traditional journals. We develop a Bayesian method to estimate the time of appearance for a phrase in the literature, and apply it to a number of phrases, both automatically extracted and suggested by experts. We see that presently most phrases appear first in the traditional journals, but there is a number of phrases with the first appearance on preprint servers. A comparison of the general composition of texts from bioRxiv and traditional journals shows a growing trend of bioRxiv being predictive of traditional journals. We discuss the application of the method for related problems.

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Three new bibliometric indicators/approaches derived from keyword analysis

Cited by 70 publications

References 27 publications

Survival analysis of author keywords: An application to the library and information sciences area

Survival analysis of author keywords: An application to the library and information sciences area

Using the subject ontology of the unified digital space of scientific knowledge in scientometric tasks

The impact of preprint servers in the formation of novel ideas

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