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

TL;DR

This paper introduces a web-based tool that visualizes global scientific collaboration networks using Bayesian multilevel logistic regression to evaluate institutional success based on highly cited papers.

Contribution

It presents a novel application combining bibliometric data and Bayesian modeling to assess and visualize successful scientific collaborations across institutions.

Findings

Identified successful collaboration patterns within subject areas.

Visualized institutional networks and their collaboration strengths.

Provided a statistical measure ('best paper rate') for impact evaluation.

Abstract

In this study we present an application which can be accessed via www.excellence-networks.net and which represents networks of scientific institutions worldwide. The application is based on papers (articles, reviews and 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 statistical models (Bayesian multilevel logistic regression, BMLR) for a number of Scopus subject areas. Within single subject areas, we have investigated and visualized how successfully overall an institution (reference institution) has collaborated (compared to all the other institutions in a subject area), and with which other institutions (network institutions) a reference institution has collaborated particularly successfully. The "best paper rate" (statistically estimated) was used as an indicator for evaluating the collaboration success of an institution. This gives the proportion of highly cited papers from an institution, and is considered generally as an indicator for measuring impact in bibliometrics.