A Sciento-text framework to characterize research strength of institutions at fine-grained thematic area level
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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.
KeywordsComputer 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.
- 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
- 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
- Bornmann, L., & Marx, W. (2011). The h index as a research performance indicator. EurSci Ed, 37(3), 77–80.Google Scholar
- 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
- 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
- Rehn, C., Kronman, U., & Wadskog, D. (2007). Bibliometric indicators—definitions and usage at Karolinska Institutet. Karolinska Institutet, 13, 2012.Google Scholar