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

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.

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Data science from a library and information science perspective

Data Technologies and Applications ◽

10.1108/dta-05-2019-0076 ◽

2019 ◽

Vol 53 (4) ◽

pp. 422-441 ◽

Cited By ~ 4

Author(s):

Sirje Virkus ◽

Emmanouel Garoufallou

Keyword(s):

Computer Science ◽

Data Science ◽

Information Science ◽

Web Of Science ◽

Research Data ◽

Library And Information Science ◽

Content Type ◽

Knowledge And Skills ◽

Computer Science Research ◽

The Web

Purpose Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective. Design/methodology/approach Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective? Findings The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. Research limitations/implications The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.” Originality/value The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.

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

Library Hi Tech ◽

10.1108/lht-05-2021-0154 ◽

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.

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The development of computer science research in the People’s Republic of China 2000–2009: a bibliometric study

Information Development ◽

10.1177/0266666912458515 ◽

2012 ◽

Vol 29 (3) ◽

pp. 251-264 ◽

Cited By ~ 7

Author(s):

Zhilong Xie ◽

Peter Willett

Keyword(s):

Computer Science ◽

Science Research ◽

Bibliometric Study ◽

People’S Republic Of China ◽

People's Republic Of China ◽

Republic Of China ◽

Computer Science Research

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A Scientometric Study of Neurocomputing Publications (1992–2018): An Aerial Overview of Intrinsic Structure

Publications ◽

10.3390/publications6030032 ◽

2018 ◽

Vol 6 (3) ◽

pp. 32 ◽

Cited By ~ 8

Author(s):

Manvendra Janmaijaya ◽

Amit Shukla ◽

Ajith Abraham ◽

Pranab Muhuri

Keyword(s):

Web Of Science ◽

Science Research ◽

Bibliometric Data ◽

Intrinsic Structure ◽

Vos Viewer ◽

Network Cluster ◽

Internal Structures ◽

Computer Science Research ◽

Scientometric Study ◽

The Web

The international journal of neurocomputing (NC) is considered to be one of the most sought out journals in the computer science research fraternity. In this paper, an extensive bibliometric overview of this journal is performed. The bibliometric data is extracted from the Web of Science (WoS) repository. The main objective of this study is to reveal internal structures and hidden inferences, such as highly productive and influential authors, most contributing countries, top institutions, collaborating authors, and so on. The CiteSpace and VOS viewer is used to visualize the graphical mapping of the bibliometric data. Further, the document co-citations network, cluster detection and references with strong citation burst is analyzed to reveal the intellectual base of NC publications.

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The growing trend of India's participation in planetary science research

Library Hi Tech ◽

10.1108/lht-05-2021-0153 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

B.S. Mohan ◽

Mallinath Kumbar

Keyword(s):

Core Collection ◽

Web Of Science ◽

Science Research ◽

Planetary Science ◽

Annual Growth Rate ◽

Content Type ◽

Research Fields ◽

The Usa ◽

Prolific Authors ◽

The Web

PurposeThe present investigation aims to present the status of planetary science research in India using different scientometric indicators, as reflected in the Web of Science Core Collection database.Design/methodology/approachThe researcher adopted systematic approaches to retrieve the data from the Web of Science Core Collection database for 20 years by using AAS Astronomical subject keywords. A total of 1,504 Indian publications and 55,572 World's publications were considered for analysis. The data were analyzed using the biblioshiny application of bibliometrix to investigate the most productive countries/territories, institutions, authors, research fields, journals, keywords, and h, g-index. The VOSviewer program is used to construct and visualize scientometric networks and analyze the co-occurrence of terms. “Webometric Analyst 2.0” is used to retrieve the Altmetric attention scores for the articles.FindingsThe results revealed that the publications on planetary science research has increased over time, with an annual growth rate of 9.66%. The study also revealed the prolific authors and institutions, productive journals and most frequently cited journals. The USA was the major collaborating partner of India. The results also provided valuable information on the citations made to these papers on planetary science, including a total number of citations, average citations per item, cited rate and h-index. There were 28,086 citations to 1,504 papers. The top 67 citation papers were the h-core papers on planetary science in India. Altmetric score for planetary science articles ranged from 1 to 2,418. Twitter (69%), news outlets (16%), blogs (6%), and Facebook (6%) were the most popular Altmetric data resources.Originality/valueThis investigation is the first attempt to employ scientometrics and visualization techniques to planetary science research in India.

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

International Journal of Web Information Systems ◽

10.1108/ijwis-01-2014-0001 ◽

2014 ◽

Vol 10 (2) ◽

pp. 194-208 ◽

Cited By ~ 3

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.

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Detecting bursty terms in computer science research

Scientometrics ◽

10.1007/s11192-019-03307-5 ◽

2019 ◽

Vol 122 (1) ◽

pp. 681-699 ◽

Cited By ~ 1

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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