The German federal and state governments' Excellence Strategy aims to sustainably dynamize Germany as a hub of science by promoting, enhancing, and increasing the competitiveness of world-class research at German universities. The Excellence Strategy will begin its second financing round in 2026, following the the first funding term from 2019-2025 and its predecessors, the Excellence Initiatives in 2005/06 and 2011/12. The financing announcement was issued in the middle of December 2022.

To ensure success in the prestigious program, researchers and university administrators have been working for months on their thematic focus, new research ideas, as well as innovative collaboration and engagement formats to either maintain their status as a University of Excellence or to join the elite club of funded institutions. 

The goal of this series of articles is 1) to assess the current state of excellence programs at the strategic, operational, and content levels. This involves determining whether the excellence programs met the objectives they set out to achieve. I also discuss if the effects differ across knowledge domains. Based on these considerations, I develop some 2) suggestions for the content design of future excellence cluster outlines and applications.

Click here for the first article (in German) ... 

As a science manager I have come into contact with all domains of knowledge - ranging from the humanities to the social sciences, to life sciences, to natural sciences, and to engineering. Apart from their individual and quite different contents, methods, jargons, and general philosophies there are some abstract "balance sheet" criteria, which I can apply to every field to get an idea of what is going on. 

I am convinced that there is an organized and systematic approach to evaluating and augmenting scientific idas which is closely related to problem solving. The development and improvement of promising avenues of research - much the same way as in product and technology devlopment - are guided by rather "objective laws" of knowdlege evolution. I use a practical set of tools for system analysis, problem formulation and blind spot identification to improve the innovative features of research or scientific solutions proposed by scientists in accordance with the patterns of system evolution of the respective field of research. 

Moreover, I summarize the essential key figures for the respective research areas and prepare them graphically for decision-makers. Over the years, I have automated parts of the workflow in order to produce high-quality assessments in a fraction of the time. For my work, I use publicly accessible data sets such as the Funding Atlas of the German Research Foundation (DFG) or the Scival publication database. In addition, I use methods such as web scraping to extract the newest available data from scientific databases such GEPRIS. 

The toughest question for me has always been what is worth thinking about. There are so many interesting things going on in the world that picking and prioritize a topic to invest time and energy in is no easy feat. So, getting started is the hardest part. But when topic selection is out of the way my method is quite straight forward. I follow these simple steps: Refine the topic - identify key sources and get the gist - outline the argumentation - provide evidence and visualize it - cite the sources - publish - and then move on.