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In this study we present an application which can be accessed viawww.excellence-networks.net and which represents networks of scientificinstitutions worldwide. The application is based on papers (articles, reviewsand conference papers) published between 2007 and 2011. It uses (network) data,on which the SCImago Institutions Ranking is based (Scopus data from Elsevier).Using this data, institutional networks have been estimated with statisticalmodels (Bayesian multilevel logistic regression, BMLR) for a number of Scopussubject areas. Within single subject areas, we have investigated and visualizedhow successfully overall an institution (reference institution) hascollaborated (compared to all the other institutions in a subject area), andwith which other institutions (network institutions) a reference institutionhas collaborated particularly successfully. The "best paper rate"(statistically estimated) was used as an indicator for evaluating thecollaboration success of an institution. This gives the proportion of highlycited papers from an institution, and is considered generally as an indicatorfor measuring impact in bibliometrics.

Excellence networks in science: A Web-based application based on  Bayesian multilevel logistic regression (BMLR) for the identification of  institutions collaborating successfully