It is now generally accepted that institutions of higher education and research, largely publicly funded, need to be subjected to some benchmarking process or performance evaluation. Currently there are several international ranking exercises that rank institutions at the global level, using a variety of performance criteria such as research publication data, citations, awards and reputation surveys etc. In these ranking exercises, the data are combined in specified ways to create an index which is then used to rank the institutions. These lists are generally limited to the top 500–1000 institutions in the world. Further, some criteria (e.g., the Nobel Prize), used in some of the ranking exercises, are not relevant for the large number of institutions that are in the medium range. In this paper we propose a multidimensional ‘Quality–Quantity’ Composite Index for a group of institutions using bibliometric data, that can be used for ranking and for decision making or policy purposes at the national or regional level. The index is applied here to rank Central Universities in India. The ranks obtained compare well with those obtained with the h-index and partially with the size-dependent Leiden ranking and University Ranking by Academic Performance. A generalized model for the index using other variables and variable weights is proposed.

Introduction This paper describes our experimental framework for a text analysis based fine-grained characterization of leading world institutions in Computer Science (CS) research. Though the present paper uses CS research output data from Web of Science, it can be extended and applied to any discipline and data source. The existing wellknown ranking systems, such as ARWU,Times Higher Education World University rankings, QS World University Rankings, SIR, Leiden Ranking and Webometrics, only present an overall (or for a whole discipline) rank of institutions. These rankings may not be helpful if one is interested in knowing centers of excellence in research in a particular area (say Artificial Intelligence or Software Engineering in CS). Such fine-grained characterization could be very useful for different purposes. Prospective students looking to work in a particular specialized area may look at the fine-grained characterization and select institutions accordingly. Academicians or industry professionals looking for collaboration in a particular area can use the information for selecting potential institutions for collaboration. Similarly, funding agencies and policy making bodies in a country may identify institutions strong in different specialized areas of research. The other advantage of this kind of sciento-text characterization is that it is completely automated, verifiable and does not use any perceptual scores for ranking (such as reputation survey and perceptual scores of QS). Our system thus proposes a framework that uses scientometric data to produce a fine-grained research strength characterization of institutions and to rank them in order of their research excellence in a particular area.

This paper presents results of our Scientometrics and text-based analysis of computer science research output from India during the last 15 years. We have collected the data for research output indexed in Web of Science and performed a detailed computational analysis to obtain important indicators, such as total research output, citation impact, collaboration patterns, thematic areas of research. The analytical results present a detailed and useful picture of status and competence of CS domain research in India.

This paper presents results of our Scientometrics and text-based analysis of computer science research output from India during the last 25 years. We have collected the data for research output indexed in Scopus and performed a detailed computational analysis to obtain important indicators, such as total research output, citation impact, collaboration patterns, top institutions/authors/publication sources. We also performed a text-based analysis on keywords of all papers indexed in Scopus to identify thematic trends during the period. The analytical results present a detailed and useful picture of status and competence of CS domain research in India.

This paper presents a scientometric analysis of research work done on the emerging area of ‘Big Data’ during the recent years. Research on ‘Big Data’ started during last few years and within a short span of time has gained tremendous momentum. It is now considered one of the most important emerging areas of research in computational sciences and related disciplines. We have analyzed the research output data on ‘Big Data’ during 2010–2014 indexed in both, the Web of Knowledge and Scopus. The analysis maps comprehensively the parameters of total output, growth of output, authorship and country-level collaboration patterns, major contributors (countries, institutions and individuals), top publication sources, thematic trends and emerging themes in the field. The paper presents an elaborate and one of its kind scientometric mapping of research on ‘Big Data’.

Microblogging websites, especially Twitter have become an important means of communication, in today's time. Often these services have been found to be faster than conventional news services. With millions of users, a need was felt to classify users based on ambient metadata associated with their user accounts. We particularly look at the effectiveness of the profile description field in order to carry out the task of user classification. Our results show that such metadata can be an effective feature for any classification task.

The South Asian region habitat about 17% of the world population and it is very important that we ensure development and prosperity of the citizens in the region for a developed and prosperous world. Information technology (IT) is considered as the technology of the modern world, one which has virtually become necessary for economic and scientific development of a nation. IT sector now plays a very important role in all spheres of life and has a measurable impact on the development trajectory of a country. The South Asian countries, despite lagging behind in Science and Technology sector development during the second half of the 20th century, made noticeable progress in the IT sector in the recent past. IT has gradually become one of the largest services sector contributors to economies of the region and one of the major employment providers. However, at the same time, it is also true that the region lacs the effort toward the development of original hardware and software products and services. Most of the products (both hardware and software) are being designed, conceptualized, and manufactured elsewhere, and the South Asian region is merely contributing to the operational part by virtue of utilizing abundant manpower. It is in this context that we have tried to measure the level of preparedness of the core strength of the IT knowledge sector and the level of research efforts in this sector towards creating original algorithms, products, and services. The paper explores and measures the IT knowledge infrastructure and the research competence of South Asian countries and provides a detailed analysis of the scenario.

This paper represents a scientometric-based analysis of Computer Science research in Bangladesh. The paper reports our analytical results from Scopus data for the last 25 years. The results present detailed statistics about the research output, the growth of research publications, citation-based impact analysis and collaboration patterns. It also tries to identify the top institutions and authors and preferred publication sources. We also did a text-based research theme analysis for the 25 year period and present the thematic research areas and their trends during the period.