scholarly journals Association between Immediacy of Citations and Altmetrics in COVID-19 Research by Artificial Neural Networks

2021 ◽  
pp. 1-21
Author(s):  
Ran Na
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Impact Factor ◽  
Journal Impact Factor ◽  
Web Of Science ◽  
Scholarly Publications ◽  
Artificial Neural ◽  
Journal Impact ◽  
The Web

Abstract Objectives: Both citations and Altmetrics are indexes of influence of a publication, potentially useful, but to what extent that the professional-academic citation and media-dominated Altmetrics are consistent with each other is a topic worthy of being investigated. The objective is to show their correlation. Methods: DOI and citation information of COVID-19 researches were obtained from the Web of Science, its Altmetric indicators were collected from the Altmetrics. Correlation between the immediacy of citation and Altmetrics of COVID-19 research was studied by artificial neural networks. Results: Pearson coefficients are 0.962, 0.254, 0.222, 0.239, 0.363, 0.218, 0.136, 0.134, and 0.505 (p<0.01) for dimensions citation, attention score, journal impact factor, news, blogs, Twitter, Facebook, video, and Mendeley correlated with the SCI citation, respectively. The citations from the Web of Science and that from the Altmetrics have deviance large enough in the current. Altmetric score isn’t precise to describe the immediacy of citations of academic publication in COVID-19 research. Conclusions: The effects of news, blogs, Twitter, Facebook, video, and Mendeley on SCI citations are similar to that of the journal impact factor. This paper performs a pioneer study for investigating the role of academic topics across Altmetric sources on the dissemination of scholarly publications.

scholarly journals Modeling and Prediction of the Impact Factor of Journals Using Open-Access Databases

2020 ◽  
Vol 49 (5) ◽  
pp. 35-58
Author(s):  
Matthias Templ
Keyword(s):  
Open Access ◽  
Impact Factor ◽  
Journal Impact Factor ◽  
Web Of Science ◽  
Functional Relationships ◽  
Modeling And Prediction ◽  
Novel Approach ◽  
The Impact ◽  
Journal Impact ◽  
The Web

This article is motivated by the work as editor-in-chief of the Austrian Journal of Statistics and contains detailed analyses about the impact of the Austrian Journal of Statistics. The impact of a journal is typically expressed by journal metrics indicators. One of the important ones, the journal impact factor is calculated from the Web of Science (WoS) database by Clarivate Analytics. It is known that newly established journals or journals without membership in big publishers often face difficulties to be included, e.g., in the Science Citation Index (SCI) and thus they do not receive a WoS journal impact factor, as it is the case for example, for the Austrian Journal of Statistics. In this study, a novel approach is pursued modeling and predicting the WoS impact factor of journals using open access or partly open-access databases, like Google Scholar, ResearchGate, and Scopus. I hypothesize a functional linear dependency between citation counts in these databases and the journal impact factor. These functional relationships enable the development of a model that may allow estimating the impact factor for new, small, and independent journals not listed in SCI. However, only good results could be achieved with robust linear regression and well-chosen models. In addition, this study demonstrates that the WoS impact factor of SCI listed journals can be successfully estimated without using the Web of Science database and therefore the dependency of researchers and institutions to this popular database can be minimized. These results suggest that the statistical model developed here can be well applied to predict the WoS impact factor using alternative open-access databases. 

scholarly journals On indexing in the Web of Science and predicting journal impact factor

2008 ◽  
Vol 9 (7) ◽  
pp. 582-590 ◽  
Author(s):  
Xiu-fang Wu ◽  
Qiang Fu ◽  
Ronald Rousseau
Keyword(s):  
Impact Factor ◽  
Journal Impact Factor ◽  
Web Of Science ◽  
Journal Impact ◽  
The Web

scholarly journals All the Research That’s Fit to Print: Open Access and the News Media

2021 ◽  
pp. 1-35
Author(s):  
Teresa Schultz
Keyword(s):  
Open Access ◽  
Impact Factor ◽  
Peer Review ◽  
News Media ◽  
Journal Impact Factor ◽  
Web Of Science ◽  
Scholarly Research ◽  
Using Data ◽  
Journal Impact ◽  
The Web

Abstract The goal of the open access (OA) movement is to help everyone access the scholarly research, not just those who can afford to. However, most studies looking at whether OA has met this goal have focused on whether other scholars are making use of OA research. Few have considered how the broader public, including the news media, uses OA research. This study sought to answer whether the news media mentions OA articles more or less than paywalled articles by looking at articles published from 2010 through 2018 in journals across all four quartiles of the Journal Impact Factor using data obtained through Altmetric.com and the Web of Science. Gold, green and hybrid OA articles all had a positive correlation with the number of news mentions received. News mentions for OA articles did see a dip in 2018, although they remained higher than those for paywalled articles. Peer Review https://publons.com/publon/10.1162/qss_a_00139

scholarly journals Present Trends in Research on Application of Artificial Neural Networks in Agricultural Engineering

2016 ◽  
Vol 20 (4) ◽  
pp. 15-25 ◽  
Author(s):  
Sławomir Francik ◽  
Zbigniew Ślipek ◽  
Jarosław Frączek ◽  
Adrian Knapczyk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Quantitative Analysis ◽  
Web Of Science ◽  
Research Area ◽  
Scientific Publications ◽  
Agricultural Engineering ◽  
Artificial Neural ◽  
Ann Application ◽  
The Web

Abstract The objective of the paper was to carry out a bibliometric quantitative analysis of publications concerning the application of artificial neural networks in the research area - agriculture and a bibliometric quantitative analysis and subject analysis with regard to agricultural engineering. A number of scientific publications devoted to the ANN found in the data base of the Web of Science - in documents published to 2015 was a basis for the quantitative analysis. Research on the use of artificial neural networks in the research area - agriculture is extending systematically. Moreover, a rapidly growing number of citations prove a continuous increase in the scientists’ interest in possibilities of the ANN applications. The quantitative analysis of scientific publications in 5 selected scientific journals and thematically related to agricultural engineering (indexed in the Web of Science) allowed a statement that 236 scientific articles from 1996- 2015 were related to the ANN application. The biggest number of publications was reported in Computers and Electronics in Agriculture - 118 articles. In 2011-2015 there was a growing trend in dynamics of publishing of scientific papers devoted to the ANN application to agricultural engineering. Thus, we may assume that the research related to application of the artificial neural networks to agricultural engineering will be continued and their scope and number will be still growing. The thematic analysis of the most often quoted publications from 2011-2015 in the journal Computers and Electronics in Agriculture, proved that they concern both the issues related to the classification problem as well as to modelling processes and systems. We should suppose that the subjects related to modelling of drying processes and application of neural networks for image analysis will grow dynamically in the following years.

Technique of scientific and technical forecasting using neural networks

2018 ◽  
pp. 103-110
Author(s):  
V. V. Nefedev
Keyword(s):  
Mathematical Modeling ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Original Method ◽  
Forecasting Methods ◽  
Expert Assessments ◽  
Artificial Neural

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 

scholarly journals Ckj consolidation among Q1 Urology and Nephrology journals

2020 ◽  
Vol 13 (5) ◽  
pp. 723-727
Author(s):  
Alberto Ortiz
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Impact Factor ◽  
Cost Efficiency ◽  
Journal Impact Factor ◽  
Outcome Prediction ◽  
Web Of Science ◽  
Sglt2 Inhibitors ◽  
The Impact ◽  
Journal Impact

Abstract The Clinical Kidney Journal (ckj) impact factor from Clarivate’s Web of Science for 2019 was 3.388. This consolidates ckj among journals in the top 25% (first quartile, Q1) in the Urology and Nephrology field according to the journal impact factor. The manuscripts contributing the most to the impact factor focused on chronic kidney disease (CKD) epidemiology and evaluation, CKD complications and their management, cost-efficiency of renal replacement therapy, pathogenesis of CKD, familial kidney disease and the environment–genetics interface, onconephrology, technology, SGLT2 inhibitors and outcome prediction. We provide here an overview of the hottest and most impactful topics for 2017–19.

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