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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 computationalbiology tools range from those who use computers primarily for communicationand data lookup, to those who write complex software programs in order toanalyse data or make it easier for others to do so. This research examines howpeople differ in how they conceptualise the same data, for which we coin theterm "subjective data models".  We interviewed 22 people with biological experience and varied levels ofcomputational experience to elicit their perceptions of the same subset ofbiological data entities. The results suggest that many people had fluidsubjective data models that would change depending on the circumstance or toolthey were using. Surprisingly, results generally did not seem to cluster arounda participant's computational experience/education levels, or the lack thereof.We further found that people did not consistently map entities from an abstractdata model to the same identifiers in real-world files, and found that certaindata identifier formats were easier for participants to infer meaning from thanothers.  Real-world implications of these findings suggests that 1) software engineersshould design interfaces for task performance and emulate other related popularuser 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 ofproviding contextual metadata when preparing data for re-use by other, toremove the need for potentially erroneous guesswork.

Subjective data models in bioinformatics: Do wet-lab and computational biologists comprehend data differently?