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The Northern Visualization and Visual Analytics (NoVA) group at Newcastle University advances human-centred visualisation research, addressing data-driven challenges and generating impact through interdisciplinary collaborations.

Visualisation of missing values in a high dimensional dataset.
Visualization of chromosomes, gene expression, methylation, copy number
Visualisation of graph representing primary and secondary edge values
Time-to-space matrix visualisation
The IPCP visualization with a zoomed in subset
The PeaGlyph multivariate visualization.

Research group focus

In an age of data science and artificial intelligence, oversight and understanding of the data that we use are essential and require human comprehension. We study computational and human-centred perspectives for making data and models accessible to humans through visualisation. Our research aims to advance human-centred visualisation by addressing challenges in, eg:

  • human-centred decision making,
  • communication of multi-modal and multi-level data,
  • data-driven storytelling,
  • image perception,
  • network visualisation,
  • visualisation of incomplete data and uncertainty,
  • visualisation in support of AI and HCAI.

Research impact

The human stakeholder is at the heart of our research, and we generate impact through interdisciplinary collaborations across a range of data-driven domains, eg, health, defence, agriculture, engineering, biosciences, education, etc.

This is exemplified in the Mobilise-D project, where NoVA members designed visualisation to support validation and acceptance of digital mobility outcomes to monitor the daily life gait of people with various mobility problems, aiming to inform drug and technology development, clinical practice, precision medicine, regulatory bodies and other stakeholders. Mobilise-D was a 5-year Innovative Medicines Initiative project, supported by the European Union’s Horizon 2020 research and innovation programme and EFPIA, with members from 34 participating universities, hospitals, and industry organisations.

Edge bundling reduces visual clutter in graphs, but they can induce ambiguous diagrams when bundling unrelated edges together. Researchers from the group over a series of papers have developed Edge-Path bundling techniques that have been deployed in many graph visualisation software libraries such as: edge-bundle (R), gggraph (R), Scimago Graphica, and yWorks (initial yFiles demos).

Collaborators