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Найдено 26 публикаций
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Статья
Ena O., Mikova N., Saritas O. et al. Scientometrics. 2016. Vol. 108. No. 3. P. 1013-1041.

This paper introduces a systematic technology trend monitoring (TTM) methodology based on an analysis of bibliometric data. Among the key premises for developing a methodology are: (1) the increasing number of data sources addressing different phases of the STI development, and thus requiring a more holistic and integrated analysis; (2) the need for more customized clustering approaches particularly for the purpose of identifying trends; and (3) augmenting the policy impact of trends through gathering future-oriented intelligence on emerging developments and potential disruptive changes. Thus, the TTM methodology developed combines and jointly analyzes different datasets to gain intelligence to cover different phases of the technological evolution starting from the ‘emergence’ of a technology towards ‘supporting’ and ‘solution’ applications and more ‘practical’ business and market-oriented uses. Furthermore, the study presents a new algorithm for data clustering in order to overcome the weaknesses of readily available clusterization tools for the purpose of identifying technology trends. The present study places the TTM activities into a wider policy context to make use of the outcomes for the purpose of Science, Technology and Innovation policy formulation, and R&D strategy making processes. The methodology developed is demonstrated in the domain of “semantic technologies”.

Добавлено: 28 августа 2016
Статья
Zemtsov S., Kotsemir M. N. Scientometrics. 2019. Vol. 120. No. 2. P. 375-404.
Добавлено: 19 марта 2018
Статья
Burmaoglu S., Saritas O. Scientometrics. 2019. Vol. 118. No. 3. P. 823-847.
Добавлено: 5 сентября 2019
Статья
Ivanova I., Leydesdorff L. Scientometrics. 2014. Vol. 99. No. 3. P. 927-948.
Добавлено: 27 января 2016
Статья
Lovakov A., Agadullina E. Scientometrics. 2019. Vol. 119. No. 2. P. 1157-1171.
Добавлено: 26 марта 2019
Статья
Pislyakov V., Dyachenko E. Scientometrics. 2010. Vol. 83. No. 3. P. 739-749.

We consider the “Matthew effect” in the citation process which leads to reallocation (or misallocation) of the citations received by scientific papers within the same journals. The case when such reallocation correlates with a country where an author works is investigated. Russian papers in chemistry and physics published abroad were examined. We found that in both disciplines in about 60% of journals Russian papers are cited less than average ones. However, if we consider each discipline as a whole, citedness of a Russian paper in physics will be on the average level, while chemistry publications receive about 16% citations less than one may expect from the citedness of the journals where they appear. Moreover, Russian chemistry papers mostly become undercited in the leading journals of the field. Characteristics of a “Matthew index” indicator and its significance for scientometric studies are also discussed.

Добавлено: 26 сентября 2012
Статья
Pislyakov V. Scientometrics. 2009. Vol. 79. No. 3. P. 541-550.
Добавлено: 25 января 2013
Статья
Dranev Y., Kotsemir M. N., Syomin B. Scientometrics. 2018. Vol. 116. No. 3. P. 1565-1587.
Добавлено: 12 июня 2018
Статья
Burmaoglu S., Saritas O., Kıdak L. B. et al. Scientometrics. 2017. Vol. 112. No. 3. P. 1419-1438.

In this study, the evolution of the connected health concept is analysed and visualized to investigate the ever-tightening relationship between health and technology as well as emerging possibilities regarding delivery of healthcare services. A scientometric analysis was undertaken to investigate the trends and evolutionary relations between health and information systems through the queries in the Web of Science database using terms related to health and information systems. To understand the evolutionary relation between different concepts, scientometric analyses were conducted within five-year intervals using the VantagePoint, SciMAT, and CiteSpace II software. Consequently, the main stream of publications related to the connected health concept matching telemedicine cluster was determined. All other developments in health and technologies were discussed around this main stream across years. The trends obtained through the analysis provide insights about the future of healthcare and technology relationship particularly with rising importance of privacy, personalized care along with mobile networks and mobile infrastructure.

Добавлено: 20 февраля 2019
Статья
Paul-Hus A., Bouvier R. L., Ni C. et al. Scientometrics. 2015. Vol. 102. No. 2. P. 1541-1553.
Добавлено: 9 февраля 2016
Статья
Katchanov Y. L., Markova Y., Natalia A. Shmatko. Scientometrics. 2016. Vol. 108. No. 2. P. 875-893.
Добавлено: 6 июня 2016
Статья
Fursov K., Kadyrova A. Scientometrics. 2017. Vol. 111. No. 3. P. 1947-1963.

 

 

 

 

 

Добавлено: 15 октября 2016
Статья
Dyachenko E. Scientometrics. 2017. Vol. 113. No. 1. P. 105-122.
Добавлено: 13 октября 2017
Статья
Dyachenko E. Scientometrics. 2014. Vol. 101. No. 1. P. 241-255.
Добавлено: 4 октября 2015
Статья
Kotsemir M. N., Shashnov S. A. Scientometrics. 2017. Vol. 112. No. 3. P. 1659-1689.
Добавлено: 27 июня 2017
Статья
Oleinik A. N., Кирдина С. Г., Popova I. P. et al. Scientometrics. 2017. Vol. 113. No. 1. P. 417-435.
Добавлено: 21 октября 2017
Статья
Marzi G., Dabić M., Daim T. et al. Scientometrics. 2017. Vol. 113. No. 2. P. 673-704.
Добавлено: 27 сентября 2018
Статья
Moskaleva O., Pislyakov V., Sterligov I. et al. Scientometrics. 2018. Vol. 116. No. 1. P. 449-462.
Добавлено: 20 мая 2018
Статья
Maltseva D. V., Batagelj V. Scientometrics. 2019. Vol. 121. No. 2. P. 1085-1128.

  In this paper, the results of a study on the development of social network analysis (SNA) and its evolution over time, using the analysis of bibliographic networks are presented. The dataset consists of articles from the Web of Science Clarivate Analytics database obtained by searching for the keyword “social network*” and those published in the main journals in the field (in total 70,000+ publications). From the data, we constructed several networks. In this paper, the focus is on the analysis of the citation network. Analyzing the obtained network, we evaluated the SNA field’s growth and identified the most cited works. Using the normalized Search path count weights, we extracted the main path, key-route paths, and link islands in the citation network. Based on the probabilistic flow node values, we also identified the most important articles. Our results show that the number of published papers almost doubles each 3 years. We confirmed the finding that the authors from the social sciences, who were most active through the whole history of the field development, experienced the “invasion” of physicists from the 2000s. However, starting from the 2010s, a new very active group of animal social network analysts took the leading position.

Добавлено: 19 октября 2018
Статья
Batagelj V., Ferligoj A., SQUAZZONI F. Scientometrics. 2017. Vol. 113. No. 1. P. 503-532.
Добавлено: 1 ноября 2018
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