In this paper, we propose two methods for scoring scientific output based on statistical quantile plotting. First, a rescaling of journal impact factors for scoring scientific output on a macro level is proposed. It is based on normal quantile plotting which allows to transform impact data over several subject categories to a standardized distribution. This can be used in comparing scientific output of larger entities such as departments working in quite different areas of research. Next, as an alternative to the Hirsch index [Hirsch, J.E. (2005). An index to quantify an individuals scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569-16572], the extreme value index is proposed as an indicator for assessment of the research performance of individual scientists. In case of Lotkaian-Zipf-Pareto behaviour of citation counts of an individual, the extreme value index can be interpreted as the slope in a Pareto-Zipf quantile plot. This index, in contrast to the Hirsch index, is not influenced by the number of publications but stresses the decay of the statistical tail of citation counts. It appears to be much less sensitive to the science field than the Hirsch index.
- Extreme value index
- Normal quantile plot
- Pareto quantile plot
- Quantile plots
ASJC Scopus subject areas
- Computer Science Applications
- Library and Information Sciences