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Computer science research: the top 100 institutions in India and in the world

Author

Listed:
  • Vivek Kumar Singh

    (South Asian University)

  • Ashraf Uddin

    (South Asian University)

  • David Pinto

    (Benemérita Universidad Autonóma de Puebla)

Abstract

This paper aims to perform a detailed scientometric and text-based analysis of Computer Science (CS) research output of the 100 most productive institutions in India and in the world. The analytical characterization is based on research output data indexed in Scopus during the last 25 years period (1989–2013). Our computational analysis involves a two-dimensional approach involving the standard scientometric methodology and text-based analysis. The scientometric characterization aims to assess CS domain research output in leading Indian institutions vis-à-vis the leading world institutions and to bring out the similarities and differences among them. It involves analysis along traditional scientometric indicators such as total output, citation-based impact assessment, co-authorship patterns, international collaboration levels etc. The text-based characterization aims to identify the key research themes and their temporal trends for the two sets. The key contribution of the experimental work is that it’s an analytical characterization of its kind, which identifies characteristic similarities and differences in CS research landscape of Indian institutions vis-à-vis world institutions.

Suggested Citation

  • Vivek Kumar Singh & Ashraf Uddin & David Pinto, 2015. "Computer science research: the top 100 institutions in India and in the world," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(2), pages 529-553, August.
    • Handle: RePEc:spr:scient:v:104:y:2015:i:2:d:10.1007_s11192-015-1612-8
    DOI: 10.1007/s11192-015-1612-8
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    References listed on IDEAS

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    Cited by:

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    3. Raghu Raman & Ricardo Vinuesa & Prema Nedungadi, 2021. "Bibliometric Analysis of SARS, MERS, and COVID-19 Studies from India and Connection to Sustainable Development Goals," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    4. Wieslawa Gryncewicz & Monika Sitarska-Buba, 2021. "Leading Research by Institutions and Authors: A Modern Research Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 1012-1026.
    5. Vivek Kumar Singh & Sumit Kumar Banshal & Khushboo Singhal & Ashraf Uddin, 2015. "Scientometric mapping of research on ‘Big Data’," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 727-741, November.
    6. Ashraf Uddin & Jaideep Bhoosreddy & Marisha Tiwari & Vivek Kumar Singh, 2016. "A Sciento-text framework to characterize research strength of institutions at fine-grained thematic area level," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1135-1150, March.
    7. Debarshi Kumar Sanyal & Plaban Kumar Bhowmick & Partha Pratim Das & Samiran Chattopadhyay & T. Y. S. S. Santosh, 2019. "Enhancing access to scholarly publications with surrogate resources," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1129-1164, November.
    8. Chihli Hung & Wei-Chao Lin, 2022. "VisualRPI: Visualizing Research Productivity and Impact," Sustainability, MDPI, vol. 14(13), pages 1-11, June.
    9. Ashraf Uddin & Vivek Kumar Singh & David Pinto & Ivan Olmos, 2015. "Scientometric mapping of computer science research in Mexico," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 97-114, October.
    10. Aparna Basu & Sumit Kumar Banshal & Khushboo Singhal & Vivek Kumar Singh, 2016. "Designing a Composite Index for research performance evaluation at the national or regional level: ranking Central Universities in India," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1171-1193, June.
    11. Matthew Harsh & Ravtosh Bal & Alex Weryha & Justin Whatley & Charles C. Onu & Lisa M. Negro, 2021. "Mapping computer science research in Africa: using academic networking sites for assessing research activity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 305-334, January.
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    13. Kimitaka Asatani & Junichiro Mori & Masanao Ochi & Ichiro Sakata, 2018. "Detecting trends in academic research from a citation network using network representation learning," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-13, May.

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