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A Sciento-text framework to characterize research strength of institutions at fine-grained thematic area level

Author

Listed:
  • Ashraf Uddin

    (South Asian University)

  • Jaideep Bhoosreddy

    (University at Buffalo)

  • Marisha Tiwari

    (Banaras Hindu University)

  • Vivek Kumar Singh

    (Banaras Hindu University)

Abstract

This paper presents a Sciento-text framework to characterize and assess research performance of leading world institutions in fine-grained thematic areas. While most of the popular university research rankings rank universities either on their overall research performance or on a particular subject, we have tried to devise a system to identify strong research centres at a more fine-grained level of research themes of a subject. Computer science (CS) research output of more than 400 universities in the world is taken as the case in point to demonstrate the working of the framework. The Sciento-text framework comprises of standard scientometric and text analytics components. First of all every research paper in the data is classified into different thematic areas in a systematic manner and then standard scientometric methodology is used to identify and assess research strengths of different institutions in a particular research theme (say Artificial Intelligence for CS domain). The performance of framework components is evaluated and the complete system is deployed on the Web at url: www.universityselectplus.com . The framework is extendable to other subject domains with little modification.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:106:y:2016:i:3:d:10.1007_s11192-016-1836-2
    DOI: 10.1007/s11192-016-1836-2
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    References listed on IDEAS

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    Cited by:

    1. Gabriel Alves Vieira & Jacqueline Leta, 2024. "biblioverlap: an R package for document matching across bibliographic datasets," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4513-4527, July.
    2. Hiran H. Lathabai & Abhirup Nandy & Vivek Kumar Singh, 2021. "x-index: Identifying core competency and thematic research strengths of institutions using an NLP and network based ranking framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9557-9583, December.
    3. 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.

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