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With the ready accessibility of bibliometric data and the availability of ready-to-use tools for generating bibliometric indicators for evaluation purposes, there is the danger of inappropri- ate use. Here we present standards of good practice for analyzing bibliometric data and presenting and interpreting the results. Comparisons drawn between research groups as to research perfor- mance are valid only if (1) the scientific impact of the research groups or their publications are looked at by using box plots, Lorenz curves, and Gini coefficients to represent distribution characteristics of data (in other words, going beyond the usual arithmetic mean value), (2) different reference stan- dards are used to assess the impact of research groups, and the appropriateness of the reference stan- dards undergoes critical examination, and (3) statistical analyses comparing citation counts take into consideration that citations are a function of many influencing factors besides scientific quality.

Citation counts for research evaluation: standards of good practice for analyzing bibliometric data and presenting and interpreting results