Subjective Data Models in Bioinformatics - Interview-based personal data model elicitation

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". This protocol provides detailed steps to elicit an individual's perceptions of biological data models. This is accomplished by interviewing individuals with interdisciplinary backgrounds: academic-level biological experience and varied levels of computational experience. Participants in the study are given three tasks, alongside a set of props to form the experiment. Results expected: This set of tasks is particularly useful for PIs, software engineers bioinformaticians, and other researchers in biological/computational science who design data models or graphical/command line interfaces. It provides the researchers with a real-world idea of how their users and potential users conceptualise data models for biological data, as well as providing information about the FAIRness, usability and context of identifiers used in the individual biological file formats. Tasks: 1. A set of cards with biological data model entities and properties, and asked to "think aloud" whilst organising the cards in a way that makes sense to them. 2. A set of biological files (GFF and FASTA), where participants are asked to map terms on the cards to terms on the files. 3. Defining an "entry point" into the model. Tools provided: Preparation: an interview guide, a background survey for each participant to complete, covering the computational tools they have used in the past, formal education and degree level, file formats and programming languages a participant is familiar with. Interview props: a list of suggested data model cards for participants to sort, files to use as an example of mapping a data model to real-world files.