, Volume 106, Issue 3, pp 1135–1150 | Cite as

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

  • Ashraf Uddin
  • Jaideep Bhoosreddy
  • Marisha Tiwari
  • Vivek Kumar Singh


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: The framework is extendable to other subject domains with little modification.


Computer science research Research competitiveness Field-based ranking Scientometrics UniversitySelectPlus 



This work is supported by research grants from Department of Science and Technology, Government of India (Grant: INT/MEXICO/P-13/2012) and University Grants Commission of India (Grant: F. No. 41-624/2012(SR)). A preliminary version of this work was presented in 20th Science Technology Indicators Conference in Sep. 2015 at Lugano, Switzerland.


  1. Alwahaishi, S., Martinovič, J., & Snášel, V. (2011). Analysis of the DBLP Publication Classification Using Concept Lattices. Digital enterprise and information systems (pp. 99–108). Berlin: Springer.CrossRefGoogle Scholar
  2. Avkiran, N. K., & Alpert, K. (2015). The influence of co-authorship on article impact in OR/MS/OM and the exchange of knowledge with Finance in the twenty-first century. Annals of Operations Research, 235(1), 1–23.Google Scholar
  3. Basu, A., & Aggarwal, R. (2001). International collaboration in science in India and its impact on institutional performance. Scientometrics, 52(3), 379–394.CrossRefGoogle Scholar
  4. Bordons, M., Aparicio, J., González-Albo, B., & Díaz-Faes, A. A. (2015). The relationship between the research performance of scientists and their position in co-authorship networks in three fields. Journal of Informetrics, 9(1), 135–144.CrossRefGoogle Scholar
  5. Bornmann, L., Leydesdorff, L., & Mutz, R. (2013a). The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits. Journal of Informetrics, 7(1), 158–165.CrossRefGoogle Scholar
  6. Bornmann, L., Leydesdorff, L., & Wang, J. (2013b). Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P100). Journal of Informetrics, 7(4), 933–944.CrossRefGoogle Scholar
  7. Bornmann, L., & Marx, W. (2011). The h index as a research performance indicator. EurSci Ed, 37(3), 77–80.Google Scholar
  8. Bornmann, L., & Marx, W. (2014). How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations. Scientometrics, 98(1), 487–509.CrossRefGoogle Scholar
  9. Bornmann, L., Moya Anegón, F., & Mutz, R. (2013c). Do universities or research institutions with a specific subject profile have an advantage or a disadvantage in institutional rankings? Journal of the American Society for Information Science and Technology, 64(11), 2310–2316.CrossRefGoogle Scholar
  10. Bornmann, L., Stefaner, M., de Moya Anegón, F., & Mutz, R. (2014). Ranking and mapping of universities and research-focused institutions worldwide based on highly-cited papers: A visualisation of results from multi-level models. Online Information Review, 38(1), 43–58.CrossRefGoogle Scholar
  11. Ductor, L. (2015). Does co-authorship lead to higher academic productivity? Oxford Bulletin of Economics and Statistics, 77(3), 385–407.CrossRefGoogle Scholar
  12. García, J. A., Rodriguez-Sánchez, R., Fdez-Valdivia, J., Torres-Salinas, D., & Herrera, F. (2012). Ranking of research output of universities on the basis of the multidimensional prestige of influential fields: Spanish universities as a case of study. Scientometrics, 93(3), 1081–1099.CrossRefGoogle Scholar
  13. Glänzel, W., & Moed, H. F. (2013). Opinion paper: Thoughts and facts on bibliometric indicators. Scientometrics, 96(1), 381–394.CrossRefGoogle Scholar
  14. Golub, K. (2006). Automated subject classification of textual Web pages, based on a controlled vocabulary: Challenges and recommendations. New Review of Hypermedia and Multimedia, 12(1), 11–27.CrossRefGoogle Scholar
  15. Gupta, B. M., Kshitij, A., & Verma, C. (2011). Mapping of Indian computer science research output, 1999–2008. Scientometrics, 86(2), 261–283.CrossRefGoogle Scholar
  16. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.CrossRefGoogle Scholar
  17. Janssens, F., Zhang, L., De Moor, B., & Glänzel, W. (2009). Hybrid clustering for validation and improvement of subject-classification schemes. Information Processing and Management, 45(6), 683–702.CrossRefGoogle Scholar
  18. Lazaridis, T. (2009). Ranking university departments using the mean h-index. Scientometrics, 82(2), 211–216.CrossRefGoogle Scholar
  19. Leydesdorff, L., & Bornmann, L. (2011). Integrated impact indicators compared with impact factors: An alternative research design with policy implications. Journal of the American Society for Information Science and Technology, 62(11), 2133–2146.CrossRefGoogle Scholar
  20. Leydesdorff, L., & Bornmann, L. (2012). The integrated impact indicator (I3), the top-10% excellence indicator, and the use of non-parametric statistics. Research Trends, 29, 5–8.Google Scholar
  21. Leydesdorff, L., Bornmann, L., Mutz, R., & Opthof, T. (2011). Turning the tables on citation analysis one more time: Principles for comparing sets of documents. Journal of the American Society for Information Science and Technology, 62(7), 1370–1381.CrossRefGoogle Scholar
  22. Liu, N. C., & Liu, L. (2005). University rankings in China. Higher Education in Europe, 30(2), 217–227.CrossRefGoogle Scholar
  23. Molinari, A., & Molinari, J. F. (2008). Mathematical aspects of a new criterion for ranking scientific institutions based on the h-index. Scientometrics, 75(2), 339–356.CrossRefMathSciNetGoogle Scholar
  24. Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60(9), 1823–1835.CrossRefGoogle Scholar
  25. Rehn, C., Kronman, U., & Wadskog, D. (2007). Bibliometric indicators—definitions and usage at Karolinska Institutet. Karolinska Institutet, 13, 2012.Google Scholar
  26. Singh, V. K., Uddin, A., & Pinto, D. (2015). Computer science research: The top 100 institutions in India and in the world. Scientometrics, 104(2), 539–563.CrossRefGoogle Scholar
  27. Uddin, A., & Singh, V. K. (2015). A quantity–quality composite ranking of Indian institutions in computer science research. IETE Technical Review, 32(4), 273–283.CrossRefGoogle Scholar
  28. Van Raan, A. (1998). The influence of international collaboration on the impact of research results: Some simple mathematical considerations concerning the role of self-citations. Scientometrics, 42(3), 423–428.CrossRefGoogle Scholar
  29. Waltman, L., & Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378–2392.CrossRefGoogle Scholar
  30. Waltman, L., & Schreiber, M. (2013). On the calculation of percentile-based bibliometric indicators. Journal of the American Society for Information Science and Technology, 64(2), 372–379.CrossRefGoogle Scholar
  31. Zhang, L., Liu, X., Janssens, F., Liang, L., & Glänzel, W. (2010). Subject clustering analysis based on ISI category classification. Journal of Informetrics, 4(2), 185–193.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  • Ashraf Uddin
    • 1
  • Jaideep Bhoosreddy
    • 2
  • Marisha Tiwari
    • 3
  • Vivek Kumar Singh
    • 4
  1. 1.Department of Computer ScienceSouth Asian UniversityNew DelhiIndia
  2. 2.Department of Computer Science and EngineeringUniversity at BuffaloBuffaloUSA
  3. 3.DST-CIMSBanaras Hindu UniversityVaranasiIndia
  4. 4.Department of Computer ScienceBanaras Hindu UniversityVaranasiIndia

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