  Fractional scoring has been proposed to avoid inconsistencies in the
attribution of publications to percentile rank classes. Uncertainties and
ambiguities in the evaluation of percentile ranks can be demonstrated most
easily with small datasets. But for larger datasets an often large number of
papers with the same citation count leads to the same uncertainties and
ambiguities which can be avoided by fractional scoring. This is demonstrated
for four different empirical datasets with several thousand publications each
which are assigned to 6 percentile rank classes. Only by utilizing fractional
scoring the total score of all papers exactly reproduces the theoretical value
in each case.
