VR gaming has been gaining widespread popularity in recent years. However, up to 40% of users suffer from VR motion sickness. The adverse effects can severely undermine the user experience. Recently, researchers have proposed the use of machine learning approaches to identify the presence of motion sickness.
The Archaeology eResearch Collaboration Initiative (ARCI) is a research group specialising in the management and analysis of data intensive archaeology. In tandem with the Centre for eResearch, we are currently trialling the CollectiveAccess deployment to collaboratively record, manage, and explore archaeological data and metadata relating to the Ahuahu Great Mercury Island project.
Travelling Heads – Measuring Reproducibility and Repeatability of Magnetic Resonance Imaging in Dementia
Approximately 50 million people are living with dementia worldwide, and in New Zealand, 1.4% of the population have Alzheimer’s disease or related dementia. With an ageing population, the prevalence is predicted to double by 2050
Novel Subject-Specific Method of Visualising Group Differences from Multiple DTI Metrics without Averaging
Developing a novel technique that integrates diffusion tensor (DTI) metrics along the whole volumes of the Fibre bundle using a mesh-fitting technique.
Melanoma is the deadliest form of skin cancer with increasing worldwide incidence. Understanding the underlying mechanisms driving melanoma is crucial for better treatment and prevention.
Using Statistics New Zealand’s Integrated Data Infrastructure (IDI) would enable linkage between NZ Police, health and social sector data, to better understand citizens in mental distress.
The project began with a patient’s decision to donate her inoperable cancer tissue for research. Over the years, medical monitoring has enabled scientists to gather a large amount of information on the growth of cancer as well as its distribution in the patient’s body.
Investigating the ecological effects of fishing and sediment run-off from land in Queen Charlotte Sound, whilst also looking for areas that might be suitable for restoration in the future.
The Centre’s Research Data Management team provides consultancy on processes and structures that support healthy data workflows. Central to this work, we must be able to answer the question: “Where is the data?”
By exploring how to improve the success rate of In Vitro Fertilisation (IVF) implantations, we hope the knowledge will be embedded in a model and made it widerly available locally and overseas where the investment will generate export value for New Zealand and benefit the needed parents by reducing IVF waiting time and increasing the rate of live births.