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The need for new indicators on universities is growing enormously. Governments and decision makers at all levels are faced with the huge opportunities generated by the availability of new knowledge and information and, simultaneously, are pressed by tight budget constraints. University rankings, in particular, are attracting policy and media attention, but at the same time receive harsh methodological criticism. After summarizing the main criticisms of rankings, we describe 2 trends in the user requirements for indicators; namely, granularity and cross-referencing. We then suggest that a change in the paradigm of the design and production of indicators is needed. The traditional approach is one that not only leverages on the existing data but also suggests heavy investment to integrate existing databases and to build up tailored indicators. We show, based on the European universities case, how the intelligent integration of existing data may lead to an open-linked data platform which permits the construction of new indicators. The power of the approach derives from the ability to combine heterogeneous sources of data to generate indicators that address a variety of user requirements without the need to design indicators on a custom basis. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of characterization and classification of universities in the European system, by using the recently developed Aquameth dataset. Preliminary cluster analysis based on structural variables identifies systematic differences in size. However, this structural distinction is associated with differences in strategic orientation of universities (towards research or towards teaching, respectively) only in a few countries. In most European countries there are no discernible differences across universities along these dimensions. The paper argues that countries in which universities are more differentiated according to research or teaching dimensions have implemented differentiation policies through a variety of policy instruments. In turn, these countries also are ranked high in international rankings of universities. This suggests a structural linkage between the poor performance of European universities in research-based rankings and the lack of differentiation. [ABSTRACT FROM AUTHOR]

Data on scientific productivity at institutes of the French INSERM and at biomedical research institutes of the Italian CNR for 1997 were analysed. Available data on human capital input and geographical agglomeration allowed the estimation and comparison of efficiency measures. Nonparametric envelopment techniques were used, and robust nonparametric techniques were applied in this work for the first time for evaluating scientific productivity. They are shown to be useful tools to compute scientific productivity indicators and make institutional comparative analyses. Taking into account a large number of methodological problems, a meaningful and rigorous indirect comparison is made possible. Several possible explanations of the observed differences in productivity are commented on. [ABSTRACT FROM AUTHOR]