scholarly journals Chỉ mục tạp chí và hệ số tác động nhìn từ Google Trends

2021 ◽  
Author(s):  
Manh-Toan Ho
Keyword(s):  
Impact Factor ◽  
Journal Impact Factor ◽  
Web Of Science ◽  
Google Trends ◽  
Journal Impact

Ngày 27/05/2021, cơ sở dữ liệu (CSDL) Scopus chính thức cập nhật hệ số tác động (HSTĐ) CiteScore 2020. Trong lần cập nhật này, hơn 26.000 tạp chí đã được Scopus cung cấp CiteScore, trong đó hơn 13.000 tạp chí là không HSTĐ Journal Impact Factor của Web of Science.

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.

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 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 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

Analysis of the journal impact factor and related bibliometric indicators in education and educational research category

2021 ◽  
pp. 1-22
Author(s):  
Metin Orbay ◽  
Orhan Karamustafaoğlu ◽  
Ruben Miranda
Keyword(s):  
Impact Factor ◽  
Educational Research ◽  
Journal Impact Factor ◽  
Impact Factors ◽  
Bibliometric Indicators ◽  
Strongly Correlated ◽  
Starting Point ◽  
Consistent Assessment ◽  
The Impact ◽  
Journal Impact

This study analyzes the journal impact factor and related bibliometric indicators in Education and Educational Research (E&ER) category, highlighting the main differences among journal quartiles, using Web of Science (Social Sciences Citation Index, SSCI) as the data source. High impact journals (Q1) publish only slightly more papers than expected, which is different to other areas. The papers published in Q1 journal have greater average citations and lower uncitedness rates compared to other quartiles, although the differences among quartiles are lower than in other areas. The impact factor is only weakly negative correlated (r=-0.184) with the journal self-citation but strongly correlated with the citedness of the median journal paper (r= 0.864). Although this strong correlation exists, the impact factor is still far to be the perfect indicator for expected citations of a paper due to the high skewness of the citations distribution. This skewness was moderately correlated with the citations received by the most cited paper of the journal (r= 0.649) and the number of papers published by the journal (r= 0.484), but no important differences by journal quartiles were observed. In the period 2013–2018, the average journal impact factor in the E&ER has increased largely from 0.908 to 1.638, which is justified by the field growth but also by the increase in international collaboration and the share of papers published in open access. Despite their inherent limitations, the use of impact factors and related indicators is a starting point for introducing the use of bibliometric tools for objective and consistent assessment of researcher.

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