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Subjective data models in bioinformatics and how wet lab and computational biologists conceptualise data
Yo Yehudi1,2, Lukas Hughes-Noehrer3, Carole Goble3
1Department of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. yochannah.yehudi@postgrad.manchester.ac.uk.
Scientific Data
|November 3, 2023
Summary
Biological researchers often have flexible subjective data models that shift with context, not strictly tied to computational skill. Clear metadata is crucial for accurate data interpretation and avoiding guesswork.
Area of Science:
- Computational biology
- Bioinformatics
- Data science in life sciences
Background:
- Biological sciences generate large datasets ('big data') requiring computational tools for analysis.
- Researchers' use of computational tools varies widely, from basic communication to complex coding.
Purpose of the Study:
- To investigate how biological researchers conceptualize data, termed 'subjective data models'.
- To understand the relationship between computational experience and data conceptualization.
Main Methods:
- Interviewed 22 participants with diverse biological and computational backgrounds.
- Analyzed variations in how researchers understood and mapped data concepts.
Main Results:
- Subjective data models were often fluid and context-dependent, not consistently linked to computational expertise.
- Researchers did not always map abstract data concepts to real-world file entities.
- Certain data identifier formats aided comprehension more than others.
Conclusions:
- Software interfaces should prioritize task-based design over professional background.
- Contextual metadata is essential to prevent misinterpretation and reduce reliance on guesswork.
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