  The h-index is an important bibliographic measure used to assess the
performance of researchers. Dutiful researchers merge different versions of
their articles in their Google Scholar profile even though this can decrease
their h-index. In this article, we study the manipulation of the h-index by
undoing such merges. In contrast to manipulation by merging articles (van
Bevern et al. [Artif. Intel. 240:19-35, 2016]) such manipulation is harder to
detect. We present numerous results on computational complexity (from
linear-time algorithms to parameterized computational hardness results) and
empirically indicate that at least small improvements of the h-index by
splitting merged articles are unfortunately easily achievable.
