by A. Berglar
Visualisation-aided Research
Intro
Insight through visualisation
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Visualisation-Aided Research (VAR) is an approach that uses scientific visualisation not only for communication, but as an active tool for scientific discovery.
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In many research projects, visualisation appears only at the end of the process, once results are already known. VAR shifts visualisation into the early phases of research, where sketches, spatial diagrams, and structural models become tools for exploring mechanisms and generating hypotheses.
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The approach emerged from the combination of my background in biomolecular sciences and scientific visualisation, and from practical work on complex spatial problems in molecular biology.
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The principle
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Biological mechanisms are inherently spatial. However, many aspects of spatial reasoning remain implicit when ideas are expressed only through text or equations.
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VAR externalises these ideas through visual representations. Sketches, diagrams, and 3D models act as epistemic probes: they make assumptions visible, reveal spatial constraints, and expose inconsistencies in proposed mechanisms.
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In this way, visualisation becomes part of the reasoning process itself.
Process
The Framework​
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Visualisation-Aided Research follows an iterative exploration process inspired by design research frameworks such as the Double Diamond.
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The framework structures the exploration of scientific questions into phases of divergence and convergence, in which visual representations are used to explore possible mechanisms and gradually reduce the space of explanations.
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The four phases of the VAR framework
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Constraint extraction
Existing evidence is analysed to identify spatial, structural, and conceptual constraints that shape the problem space. Visual representations help reveal which assumptions are supported by available data and which aspects remain uncertain.
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Contextual embedding
The identified constraints are embedded into a reconstructed biological context. Through sketches, diagrams, and structural models, relevant spatial relationships and potential interaction surfaces become visible.
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Scenario reduction
Multiple possible mechanistic scenarios are explored and iteratively compared. Visual modelling and computational analysis help determine which configurations remain plausible and which can be excluded.
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Mechanistic insight
The reduced scenario space allows a more focused mechanistic interpretation. The resulting model can guide further experimental investigation and provide a coherent explanation of the observed system.
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Across these four phases, visual representations function as epistemic probes that help reveal constraints, structure uncertainty, and reduce the space of mechanistic explanations.
Collaboration with researchers
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VAR is typically developed in close collaboration with experimental researchers.
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While they perform laboratory experiments, I explore the spatial and mechanistic landscape using visual and computational tools.
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This process often leads to:
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new mechanistic hypotheses
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suggestions for experimental tests
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and clear visual models that can be used for publications, presentations, or grant proposals.