  As the quantity of data being depositing into biological databases continues
to increase, it becomes ever more vital to develop methods that enable us to
understand this data and ensure that the knowledge is correct. It is
widely-held that data percolates between different databases, which causes
particular concerns for data correctness; if this percolation occurs, incorrect
data in one database may eventually affect many others while, conversely,
corrections in one database may fail to percolate to others.
  In this paper, we test this widely-held belief by directly looking for
sentence reuse both within and between databases. Further, we investigate
patterns of how sentences are reused over time. Finally, we consider the
limitations of this form of analysis and the implications that this may have
for bioinformatics database design.
  We show that reuse of annotation is common within many different databases,
and that also there is a detectable level of reuse between databases. In
addition, we show that there are patterns of reuse that have previously been
shown to be associated with percolation errors.
