scholarly journals Computer Science in Transcaucasia and Baltic States: a Comparative Bibliometric Analysis

2019 ◽  
pp. 108-118
Author(s):  
Shushanik Sargsyan ◽  
Edita Gzoyan
Keyword(s):  
Computer Science ◽  
International Collaboration ◽  
Publication Output ◽  
Science Research ◽  
Research Field ◽  
Baltic States ◽  
Primary Position ◽  
Computer Science Research ◽  
Leading Position ◽  
The Baltic

Based on the Web of Science, InCites database, this article will analyze the publication output of the Transcaucasia and Baltic states in computer science research field, their citations, as well as international collaboration in the field of computer science. The obtained results demonstrate that publications on computer science from the Baltic states are nearly 4 times higher than publications from Transcaucasia. Among the Baltic states, Lithuania holds a primary position followed by Estonia and Latvia; while in the Transcaucasia, the leading position is held by Azerbaijan, and followed by Armenia and Georgia. The same picture can be seen in the case of citations on the works on computer science of the studied states. In the international collaboration framework, the European states are the most frequent collaboration countries of the Baltic States. The same tendency can be seen in the case of Georgia and Armenia, while Azerbaijan shows a dramatically different vector of scientific internationalization.

scholarly journals Detecting bursty terms in computer science research

2019 ◽  
Vol 122 (1) ◽  
pp. 681-699 ◽  
Author(s):  
E. Tattershall ◽  
G. Nenadic ◽  
R. D. Stevens
Keyword(s):  
Computer Science ◽  
Historical Data ◽  
Science Research ◽  
Detection Algorithm ◽  
Trend Detection ◽  
Free Text ◽  
Research Topics ◽  
Computer Science Research ◽  
Insight Into ◽  
Over Time

AbstractResearch topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.

Investigating the citation advantage of author-pays charges model in computer science research: a case study of Elsevier and Springer

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tehmina Amjad ◽  
Mehwish Sabir ◽  
Azra Shamim ◽  
Masooma Amjad ◽  
Ali Daud
Keyword(s):  
Open Access ◽  
Computer Science ◽  
Science Research ◽  
Vital Role ◽  
The Body ◽  
Content Type ◽  
Body Of Knowledge ◽  
Computer Science Research ◽  
Science Domain ◽  
Citation Advantage

PurposeCitation is an important measure of quality, and it plays a vital role in evaluating scientific research. However, citation advantage varies from discipline to discipline, subject to subject and topic to topic. This study aims to compare the citation advantage of open access and toll access articles from four subfields of computer science.Design/methodology/approachThis research studies the articles published by two prestigious publishers: Springer and Elsevier in the author-pays charges model from 2011 to 2015. For experimentation, four sub-domains of computer science are selected including (a) artificial intelligence, (b) human–computer interaction, (c) computer vision and graphics, and (d) software engineering. The open-access and toll-based citation advantage is studied and analyzed at the micro level within the computer science domain by performing independent sample t-tests.FindingsThe results of the study highlight that open access articles have a higher citation advantage as compared to toll access articles across years and sub-domains. Further, an increase in open access articles has been observed from 2011 to 2015. The findings of the study show that the citation advantage of open access articles varies among different sub-domains of a subject. The study contributed to the body of knowledge by validating the positive movement toward open access articles in the field of computer science and its sub-domains. Further, this work added the success of the author-pays charges model in terms of citation advantage to the literature of open access.Originality/valueTo the best of the authors’ knowledge, this is the first study to examine the citation advantage of the author-pays charges model at a subject level (computer science) along with four sub-domains of computer science.

Chameleon: A Scalable Production Testbed for Computer Science Research

2019 ◽  
pp. 123-148 ◽  
Author(s):  
Kate Keahey ◽  
Pierre Riteau ◽  
Dan Stanzione ◽  
Tim Cockerill ◽  
Joe Mambretti ◽  
...  
Keyword(s):  
Computer Science ◽  
Science Research ◽  
Computer Science Research ◽  
Scalable Production
Sign in / Sign up

Export Citation Format

Share Document