A digital companion to the research paper
Alona Zharova, Janine Tellinger-Rice and Wolfgang Karl Härdle
How to Measure the Performance of a Collaborative Research Center.
Scientometrics, 117, 1023–1040 (2018).
https://doi.org/10.1007/s11192-018-2910-8
The paper is available at: Publisher's website. The preprint is also available at: SFB 649 Discussion Papers
New Public Management helps universities and research institutions to perform in a highly competitive research environment. Evaluating publicly financed research improves transparency, helps in reflection and self-assessment, and provides information for strategic decision making. In this paper we provide empirical evidence using data from a Collaborative Research Center (CRC) on financial inputs and research output from 2005 to 2016. After selecting performance indicators suitable for a CRC, we describe main properties of the data using visualization techniques. To study the relationship between the dimensions of research performance, we use a time fixed effects panel data model and fixed effects Poisson model. With the help of year dummy variables, we show how the pattern of research productivity changes over time after controlling for staff and travel costs. The joint depiction of the time fixed effects and the research project’s life cycle allows a better understanding of the development of the number of discussion papers over time.
Keywords: Research performance, Fixed effects panel data model, Network, Collaborative Research Center
JEL classification: C23, C13, M19.
Due to the data privacy reasons we are unfortunately not allowed to publish the data used in this paper. However, we do provide the aggregated values to reproduce each of the visualizations. Each folder in this repo contains all the data that is needed to run the codes.
The repository contains four visualizations in the corresponding folders. Each folder has an R script that produces a visualization, a csv file with data and a resulting plot. To reproduce a visualization, download the corresponding csv file and run the R script.
Financial support from the German Research Foundation (DFG) via Collaborative Research Center 649 ‘‘Economic Risk’’ and International Research Training Group 1792 ‘‘High Dimensional Nonstationary Time Series’’, Humboldt-Universität zu Berlin, is gratefully acknowledged. We are thankful for the assistance provided by Nicole Hermann und Dominik Prugger.
- Alona Zharova, alona.zharova@hu-berlin.de