Challenges in scientific research include the difficulty in obtaining overviews of the large
amount of data required for analysis, and in resolving the differences in terminology used
to store and interpret information in multiple, independently created data sets. Ontologies
provide one solution for analysis involving multiple data sources, improving cross-referencing
and data integration.
This thesis looks at harnessing advanced human perception to reduce the cognitive load
in the analysis of the multiple, complex data sets the bioinformatics user group studied use
in research, taking advantage also of users’ domain knowledge, to build mental models of
data that map to its underlying structure. Guided by a user-centred approach, prototypes
were developed to provide a visual method for exploring users’ information requirements
and to identify solutions for these requirements. 2D and 3D node-link graphs were built to
visualise the hierarchically structured ontology data, to improve analysis of individual and
comparison of multiple data sets, by providing overviews of the data, followed by techniques
for detailed analysis of regions of interest.
Iterative, heuristic and structured user evaluations were used to assess and refine the
options developed for the presentation and analysis of the ontology data. The evaluation
results confirmed the advantages that visualisation provides over text-based analysis, and
also highlighted the advantages of each of 2D and 3D for visual data analysis.Overseas Research Students Awards SchemeJames Watt Scholarshi
