  Biological science produces large amounts of data in a variety of formats,
which necessitates the use of computational tools to process, integrate,
analyse, and glean insights from the data. Researchers who use computational
biology tools range from those who use computers primarily for communication
and data lookup, to those who write complex software programs in order to
analyse data or make it easier for others to do so. This research examines how
people differ in how they conceptualise the same data, for which we coin the
term "subjective data models".
  We interviewed 22 people with biological experience and varied levels of
computational experience to elicit their perceptions of the same subset of
biological data entities. The results suggest that many people had fluid
subjective data models that would change depending on the circumstance or tool
they were using. Surprisingly, results generally did not seem to cluster around
a participant's computational experience/education levels, or the lack thereof.
We further found that people did not consistently map entities from an abstract
data model to the same identifiers in real-world files, and found that certain
data identifier formats were easier for participants to infer meaning from than
others.
  Real-world implications of these findings suggests that 1) software engineers
should design interfaces for task performance and emulate other related popular
user interfaces, rather than targeting a person's professional background; 2)
when insufficient context is provided, people may guess what data means,
whether or not their guesses are correct, emphasising the importance of
providing contextual metadata when preparing data for re-use by other, to
remove the need for potentially erroneous guesswork.
