  Journal Impact Factors (IFs) can be considered historically as the first
attempt to normalize citation distributions by using averages over two years.
However, it has been recognized that citation distributions vary among fields
of science and that one needs to normalize for this. Furthermore, the mean-or
any central-tendency statistics-is not a good representation of the citation
distribution because these distributions are skewed. Important steps have been
taken to solve these two problems during the last few years. First, one can
normalize at the article level using the citing audience as the reference set.
Second, one can use non-parametric statistics for testing the significance of
differences among ratings. A proportion of most-highly cited papers (the
top-10% or top-quartile) on the basis of fractional counting of the citations
may provide an alternative to the current IF. This indicator is intuitively
simple, allows for statistical testing, and accords with the state of the art.
