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This article is a commentary on features and usefulness of a newly developed STI data portal, Indian Science Reports (ISR) (available at www.indianscience.net). The ISR portal is an attempt towards a systematic portal on STI data and analytics for India, both at an overall as well as at the institutional level. It can be used by a very wide audience for a variety of purposes ranging from research performance assessment to evidence-based policy formulation. [ABSTRACT FROM AUTHOR]

The ubiquitous applications of Artificial Intelligence (AI) in various domains of human life have resulted in a phenomenal increase in AI research. The research output in AI has grown rapidly during the last decade. While some scientometric studies have noted this growth in publications, there are virtually no studies that could characterize the growth in publications in terms of the increase in domains and journals in which AI research is being carried out and published. This article makes an attempt to fill this research gap by using the Leimkuhler model of Bradford’s law of productivity to produce quantitative estimates of AI research publishing. Publications indexed in Web of Science for the period 2011 to 2020 are used for analysis. The analysis explains the variation in the corpus of AI research using productivity distribution and its characteristics. The quantitative findings support the idea that AI research has not only increased in volume but also in terms of applications to a wider list of areas. [ABSTRACT FROM AUTHOR]

Indian Institutes of Management (IIMs) are among the most prestigious business schools in India, mainly offering postgraduate, doctoral and executive education programmes in the fields of Management and Business Education. They also contribute significantly to research in the area. This article attempts to analyse the bibliometric patterns in research output of IIMs. The data for research publications indexed in Scopus during 2010-19 is downloaded and analysed to identify important patterns and trends of research output, citations, international collaboration, open access, gender distribution and social media visibility. The results are also compared with three top internationally renowned business schools (Harvard Business School, MIT Sloan School of Management and NUS Business School). Results indicate that the older IIMs like Ahmedabad and Bangalore are placed at the top in terms of publication counts and citations. Newer IIMs like Rohtak and Raipur are found to be doing well in publications as compared to other IIMs of their generation. IIM Udaipur has more than 40 % of its research output internationally collaborated and also highest citations per paper value amongst all the IIMs. However, when the IIMs are compared with three well-known international schools (two of which have mentored the initial two IIMs), there appears a large gap in several indicators, such as h-index. The paper, thus, indicates that IIMs need to improve their research output and quality to be at par with the top business schools of the world. Research themes like ‘sustainability’, ‘emerging markets’ and ‘supply chain management’ are the most prominent thematic areas observed in the research output from IIMs, which indicates that IIMs are working on research topics of contemporary relevance. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of this study is to explore and validate the question "whether altmetric mentions can predict citations to scholarly articles". The paper attempts to explore the nature and degree of correlation between altmetrics (from ResearchGate and three social media platforms) and citations. Design/methodology/approach: A large size data sample of scholarly articles published from India for the year 2016 is obtained from the Web of Science database and the corresponding altmetric data are obtained from ResearchGate and three social media platforms (Twitter, Facebook and blog through Altmetric.com aggregator). Correlations are computed between early altmetric mentions and later citation counts, for data grouped in different disciplinary groups. Findings: Results show that the correlation between altmetric mentions and citation counts are positive, but weak. Correlations are relatively higher in the case of data from ResearchGate as compared to the data from the three social media platforms. Further, significant disciplinary differences are observed in the degree of correlations between altmetrics and citations. Research limitations/implications: The results support the idea that altmetrics do not necessarily reflect the same kind of impact as citations. However, articles that get higher altmetric attention early may actually have a slight citation advantage. Further, altmetrics from academic social networks like ResearchGate are more correlated with citations, as compared to social media platforms. Originality/value: The paper has novelty in two respects. First, it takes altmetric data for a window of about 1–1.5 years after the article publication and citation counts for a longer citation window of about 3–4 years after the publication of article. Second, it is one of the first studies to analyze data from the ResearchGate platform, a popular academic social network, to understand the type and degree of correlations. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2019-0364 [ABSTRACT FROM AUTHOR]

This paper tries to map the research work carried out in the field of Big Data through a detailed analysis of scholarly articles published on the theme during 2010-16, as indexed in Scopus. We have collected and analyzed all relevant publications on Big Data, as indexed in Scopus, through a quantitative as well as textual characterization. The analysis attempts to dwell into parameters like research productivity, growth of research and citations, thematic trends, top publication sources and emerging topics in this field. The analytical study also investigates country-wise publications output and impact in terms of average citations per paper, country-level collaboration patterns, authorship and leading contributors (countries, institutions) etc. The scholarly publication data is also subjected to a detailed textual analysis method to identify key themes in Big Data research, disciplinary variations and thematic trends and patterns. The results produce interesting inferences. Quantitative measures show that there has been a tremendous increase in number of publications related to Big Data during last few years. Research work in Big Data, though primarily considered a sub-discipline of Computer Science, is now carried out by researchers in many disciplines. Thematic analysis of publications in Big Data show that it's a discipline involving research interest from fields as diverse as Medicine to Social Sciences. The paper also identifies major keywords now associated with Big Data research such as Cloud Computing, Deep Learning, Social Media and Data Analytics. This helps in a thorough understanding and visualization of the Big Data research area. [ABSTRACT FROM AUTHOR]

This article presents a computational analysis of the research performance of 16 relatively older Indian Institutes of Technology (IITs) in India. The research publication data indexed in Web of Science for all the 16 IITs is used for the analysis. The data is computationally analysed to identify productivity, productivity per capita, rate of growth of research output, authorship and collaboration pattern, citation impact and discipline-wise research strengths of the different IITs. The research performances of the IITs have been compared with those of two top ranking engineering and technology institutions of the world (MIT-USA and NTU-Singapore) and most cited papers from these IITs have also been identified. The analytical results are expected to provide a informative, up-to-date and useful account of research performance assessment of the IITs. [ABSTRACT FROM AUTHOR]

• Power law distribution in social media mentions (altmetrics) to scientific articles is explored. • Empirical analysis confirms that altmetrics exhibit power law. • Some other fits like log-normal are found to be plausible, whereas exponential distribution does not show a good fit. • Implications of existence of Power law in altmetrics are discussed. Power Laws are a characteristic distribution found in both natural as well as in man-made systems. Previous studies have shown that citations to scientific articles follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. However, the distributional character of altmetrics (such as reads, likes, mentions, etc.) has not been studied in much detail, particularly with respect to existence of power law behaviours. This article, therefore, attempts to do an empirical analysis of altmetric mention data of a large set of scholarly articles to see if they exhibit power law. The individual and the composite data series of 'mentions' on the various platforms are fit to a power law distribution, and the parameters and goodness of fit are determined, both using least squares regression as well as the Maximum Likelihood Estimate (MLE) approach. We also explore the fit of the mention data to other distribution families like the Log-normal and exponential distributions. Results obtained confirm the existence of power law behaviour in social media mentions to scholarly articles. The Log-normal distribution also looks plausible but is not found to be statistically significant, and the exponential distribution does not show a good fit. Major implications of power law in altmetrics are given and interesting research questions are posed in pursuit of enhancing the reliability of altmetrics for research evaluation purposes. [ABSTRACT FROM AUTHOR]

This paper presents a scientometric and keyword-based analysis of research work done in the discipline of computer science (CS) in the SAARC region during the last 25 years. We have performed a detailed computational analysis of bibliometric and textual data for publications originating from the SAARC countries indexed in Scopus. Our analysis measures and presents findings on total CS research output of the SAARC countries, global share and rank, growth trends and impact, collaboration levels within and outside of SAARC countries, top-ranking institutions and individuals in the region, most preferred journals in the region, and a text-based research topic trend. The analytical results provide a useful mapping of CS research work in the SAARC region. The results are correlated with other demographic and economic indicators of the SAARC countries, and present inferences useful for determining funding patterns and policy formulation for scientific research in CS domain. [ABSTRACT FROM PUBLISHER]

This article presents a bibliometric assessment of research performance of the National Institutes of Technology (NITs) in India. While many of these institutes were originally established in 1960s as Regional Engineering Colleges (RECs), they were upgraded to NITs around 2002 and later. Initially NITs offered only undergraduate programmes in engineering. However, during the last decade, several NITs have started postgraduate teaching and are focusing more on research activities. It is in this context that this article assesses the research performance of NITs during 2005-2016. The performance assessment uses research publication data obtained from the Web of Science index. The data collected are computationally analysed to identify productivity, productivity per capita, rate of growth of research, international collaboration pattern, citation impact and discipline-wise distribution of the research output for the NITs. The performance of NITs is also viewed vis-à-vis two top-performing Indian institutions, namely Indian Institute of Science, Bengaluru and Indian Institute of Technology Bombay, Mumbai. A simple single-value composite ranking of research performance of NITs is also presented by combining quantity and quality factors. The study presents an informative and useful account of assessment of research work in the NITs. [ABSTRACT FROM AUTHOR]

This article presents the research performance of the 39 central universities in India. The research publication data, indexed in the Web of Science, for the 39 central universities for a 25-year period (1990-2014) are used for analysis. The data are computationally analysed to identify productivity, productivity per capita, productivity per crore rupees grant, rate of growth of research output, authorship and collaboration pattern, citation impact and discipline-wise research strength of these institutions. Research performance of the central universities is measured and compared with two top-ranking world universities, namely University of Cambridge and Stanford University. While older well-established big universities such as University of Delhi and Banaras Hindu University perform better than newer universities, some relatively smaller universities, such as the university of Hyderabad have impressive research performance. What is disturbing is that combined research output of all central universities taken together is less than that of either of University of Cambridge or Stanford University alone. The results also provide discipline-wise research strengths of all the universities. [ABSTRACT FROM AUTHOR]