Scientometric assessment of Saudi publication productivity in computer science in the period of 1978-2012

2014 ◽  
Vol 10 (2) ◽  
pp. 194-208 ◽  
Author(s):  
Hend S. Al-Khalifa
Keyword(s):  
Saudi Arabia ◽  
Computer Science ◽  
Research Output ◽  
Science Research ◽  
Scientific Output ◽  
Arab Countries ◽  
Publication Productivity ◽  
Science Field ◽  
Content Type ◽  
Computer Science Research

Purpose – This study aims to analyze Saudi scientific output in the field of computer science in Web of Science database, covering the years 1978 through 2012. Design/methodology/approach – The study involved analyzing 998 publications in terms of the publication count and its growth, citation, share of international collaboration, research areas and researchers’ productivity. Findings – The results show that the number of papers produced in computer science field has only increased after year 2007; this is because Saudi universities have applied a catch-up strategy to increase its research output. Also, our study reveals that the publication performance of Saudi scientists in computer science was domestic and suffers from low international visibility. Only two universities took the lead in the production of computer science research. Furthermore, computer science research trends in Saudi Arabia focused on engineering, followed by mathematics and telecommunications. Originality/value – Studies on international academic publication productivity in the Middle East, particularly in Arab countries such as Saudi Arabia, are rarely found. In fact, bibliometric studies on Saudi researchers in the field of computer science are not available. Therefore, the originality of this study resides in being the first study to measure publication productivity of Saudi researchers in the field of computer science.

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.

scholarly journals Computer science in Eastern Europe 1989-2014: a bibliometric study

2015 ◽  
Vol 67 (5) ◽  
pp. 526-541 ◽  
Author(s):  
Dalibor Fiala ◽  
Peter Willett
Keyword(s):  
Computer Science ◽  
Web Of Science ◽  
Science Research ◽  
Bibliometric Study ◽  
Eastern European ◽  
Content Type ◽  
Publication Rates ◽  
Computer Science Research ◽  
The Individual ◽  
The Web

Purpose – The purpose of this paper is to study the development of research in computer science in 15 Eastern European countries following the breaching of the Berlin Wall in 1989. Design/methodology/approach – The authors conducted a bibliometric analysis of 82,121 computer science publications indexed in the Web of Science database and investigated publication, citation, and collaboration patterns of the individual countries. Findings – Poland has been the most productive country, followed by Russia, the Czech Republic, Romania, Hungary, and Slovenia. Publication rates have increased substantially over the period, but this has not been accompanied by a corresponding increase in the quality of the publications. Hungary and Slovenia are the most influential countries in terms of citations per paper. Artificial Intelligence is the most frequently occurring computer science subject category, with Interdisciplinary Applications the category with the greatest impact. USA, Germany, UK, France, and Canada are the most frequently collaborating western nations, and papers published in collaboration with US authors accrue the most citations. Originality/value – This is the first ever bibliometric study of the whole post-communist Eastern European computer science research as indexed in the Web of Science.

Mapping of Indian computer science research output, 1999–2008

2010 ◽  
Vol 86 (2) ◽  
pp. 261-283 ◽  
Author(s):  
B. M. Gupta ◽  
Avinash Kshitij ◽  
Charu Verma
Keyword(s):  
Computer Science ◽  
Research Output ◽  
Science Research ◽  
Computer Science Research

Leading countries in computer science: A bibliometric overview

2020 ◽  
pp. 1-14
Author(s):  
Gustavo Zurita ◽  
José M. Merigó ◽  
Valeria Lobos-Ossandón ◽  
Carles Mulet-Forteza
Keyword(s):  
Computer Science ◽  
Field Research ◽  
Science Research ◽  
South American ◽  
Science Field ◽  
The United Kingdom ◽  
Computer Science Research ◽  
Wide Range ◽  
English Speaking ◽  
A Current

This paper presents a current overview of the main productive and influential countries around the world in the computer science field. Research in the computer science field has experienced significant growth in recent years. This study develops a bibliometric overview of all journals that have been indexed in the Web of Science (WoS) database over the past 25 years (1995–2019), according to several bibliometric indicators in the seven categories of computer science research. The study shows that United States is the leading country in the computer science field. Other countries, such as the United Kingdom, China, Canada and Germany, also obtain high positions in the ranking. The average country that performs research in computer science is European, has English-speaking researchers, is highly developed and has a high income. However, there is a wide range of countries that perform research in computer science, including South American and Arabic countries, meaning that computer science traverses many countries and cultures.

scholarly journals An overview of Brazilian researches in the Computer Science field in last years

2019 ◽  
Author(s):  
Leandro Peres ◽  
Pablo Cecilio ◽  
Francielly Rodrigues ◽  
Nícollas Silva ◽  
Leonardo Rocha
Keyword(s):  
Computer Science ◽  
Topic Modeling ◽  
Data Science ◽  
Science Research ◽  
Analysis Data ◽  
Science Field ◽  
Sense Data ◽  
Public Investments ◽  
Computer Science Research ◽  
Science Activities

Recently, most traditional market services have joined online service platforms. Despite the practicality achieved, such services eventually bring a large amount of data to the Web. In this sense, data analysis, data engi- neering, and data science activities have become extremely necessary. In general, they can extract extra information about systems and users, allowing the owners to produce insights and analyze patterns. Then, we propose an evalua- tion methodology to be applied in the online scenario of registration of publications and scientific productions, such as ResearchGate and Lattes Platform of CNPq. This methodology is unsupervised and divided into three main stages: (i) obtaining and representing the data; (ii) application of topic modeling; and (iii) the labeling of topics. This proposal diverges from the literature’s proposes that are based on collaborative networks and supervised techniques. We applied this methodology to a Lattes database and were able to observe the evolution of Computer Science research in Brazil. Based on this analysis, it is possible to identify the most popular and least explored research lines in order to direct public investments according to a certain interest.

scholarly journals The Arab region’s contribution to global COVID-19 research: Bibliometric and visualization analysis

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Sa’ed H. Zyoud
Keyword(s):  
Public Health ◽  
Saudi Arabia ◽  
Population Size ◽  
Low Income ◽  
Research Output ◽  
Arab World ◽  
The United States ◽  
Arab Countries ◽  
Public Health Issue ◽  
Global Research

Abstract Background At the global level and in the Arab world, particularly in low-income countries, COVID-19 remains a major public health issue. As demonstrated by an incredible number of COVID-19-related publications, the research science community responded rapidly. Therefore, this study was intended to assess the growing contribution of the Arab world to global research on COVID-19. Methods For the period between December 2019 and March 2021, the search for publications was conducted via the Scopus database using terms linked to COVID-19. VOSviewer 1.6.16 software was applied to generate a network map to assess hot topics in this area and determine the collaboration patterns between different countries. Furthermore, the research output of Arab countries was adjusted in relation to population size and gross domestic product (GDP). Results A total of 143,975 publications reflecting the global overall COVID-19 research output were retrieved. By restricting analysis to the publications published by the Arab countries, the research production was 6131 documents, representing 4.26% of the global research output regarding COVID-19. Of all these publications, 3990 (65.08%) were original journal articles, 980 (15.98%) were review articles, 514 (8.38%) were letters and 647 (10.55%) were others, such as editorials or notes. The highest number of COVID-19 publications was published by Saudi Arabia (n = 2186, 35.65%), followed by Egypt (n = 1281, 20.78%) and the United Arab Emirates (UAE), (n = 719, 11.73%). After standardization by population size and GDP, Saudi Arabia, UAE and Lebanon had the highest publication productivity. The collaborations were mostly with researchers from the United States (n = 968), followed by the United Kingdom (n = 661). The main research lines identified in COVID-19 from the Arab world are related to: public health and epidemiology; immunological and pharmaceutical research; signs, symptoms and clinical diagnosis; and virus detection. Conclusions A novel analysis of the latest Arab COVID-19-related studies is discussed in the current study and how these findings are connected to global production. Continuing and improving future collaboration between developing and developed countries will also help to facilitate the sharing of responsibilities for COVID-19 in research results and the implementation of policies for COVID-19.

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.

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