The thesis, "Accelerating Health Research Using Linked Data and Virtual Reality," explores methods, models, and programs that promote the integration, reuse and visualisation of scientific data, for both humans and computers.
The core is Graph2VR, a Virtual Reality (VR) application that enables users to explore large, complex datasets modelled as knowledge graphs (e.g. RDF). Graph2VR displays data interactively as networks of nodes and edges within a 3D space, allowing researchers to navigate and explore relationships in ways not possible with traditional 2D tools. Graph2VR’s unique strengths lie in its VR environment, interactive querying and layout, and the ability to integrate data from multiple sources. This thesis of Alexander Kellmann also demonstrates Graph2VR’s application, such as using for data catalogue, standardised medication codes, and genetic data.
Furthermore, we developed the MOLGENIS Catalogue. This online platform helps researchers internationally to share and find existing data from cohort studies and biobanks, helping to create new collaborations and promoting data reuse. The catalogue supports harmonising data across studies such that datasets can be jointly analysed to increase statistical power. It is already used by 12 (inter)international research networks and 796 data collections.
Lastly, during the COVID-19 pandemic, we helped to analyse the Lifelines COVID study by adapting the MOLGENIS SORTA Plugin to semi-automatically convert medication names from survey responses to standard medicine codes, a step necessary to allow statistical analyses. This tool reduced data processing time by 50%, while leaving decision-making in the hands of human experts rather than fully automating the process.
