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

TL;DR

This study explores how biological and computational researchers perceive and interpret biological data differently, revealing fluid subjective data models that vary with context and highlighting implications for data interface design and metadata provision.

Contribution

It introduces the concept of subjective data models in bioinformatics and empirically investigates how different users conceptualize the same biological data.

Findings

People have fluid subjective data models that change with context.

Data identifier formats influence ease of understanding.

Perceptions do not cluster by computational experience.

Abstract

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.