Cybermetrics of the Indian Universities

摘要

Introduction. During last years a great effort has been made for providing both a theoretical basic for the cybermetric analysis as well as enough empirical data for providing a global scenario of R&D in the Web. As new crawlers (personal robots) are now available and the behavior of search engines seems more stable and reliable the data available could provide valuable insights in regional and disciplinary quantitative studies.\nThe aim of this paper is to provide for the first time a set of web indicators for the academic sector in India, that can be used for future comparative analysis with other traditional bibliometric and scientometric data.\nMethods. A series of web indicators is proposed including definitions, the problems faced for obtaining them, several proposed applications and the shortcomings of their use and visualisation. We built the following indicators:\nNumber of subdomains\nWebsize\nnumber of webpages\nnumber of rich files\nnumber of multimedia objects)\nDensity (links per page)\nLuminosity (unique outlinks per page)\nLink quality (percentage of valid hypertext links)\nOutlink dispersion (by domain or subdomain)\nPopularity (relative position)\nVisibility (number of external inlinks)\nPage rank position\nGeographical distribution\nImpact (WebIF)\nLanguage diversity\nThe URL addresses were identified for the Indian universities. The sources of quantitative data have been major search engines that support field delimitation and the Alexa database. Personal crawlers were used for automatic extraction of numeric data from these websites.\nResults. Using Google and Altavista data, websize, visibility and impact indicators were calculated for 179 universities with website from a total amounting 296 institutions. Popularity relative ranking were built using Alexa listings. The results were presented in different levels of geographical aggregation and listings of institutions with the highest ranks are showed.\nConclusions. The sample analyzed only represents a fraction of the Indian universities, but those analyzed are the most relevant and productive. The results show a correlation among some indicators and scientific competence, productivity and impact of the universities involved when bibliometric data are available. The most interesting indicators are visibility that offers better performance than Web impact and diversity. Rich files are more randomly distributed, but a general pattern show a correlation with productivity of formal papers in hard sciences oriented universities.