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NeSI provides a range of services, people, expertise, and information to help computational research projects become reality

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Photo of a riparian strip. Credit Dave Allen, NIWA.

Tools to better understand and address water quality issues

"The team at NeSI worked with us to provide a solution to achieve significant speed-up in legacy R- and FORTRAN-based code for catchment model runs."
Subject: 
Earth Science
Picture of a baby sitting on a rug, facing away from the camera. Image by thedanw from Pixabay

AI app to identify cerebral palsy in infants

“Our eyes cannot focus on the arm, leg and eyes at the same time, but a machine can learn the connectivity in the limbs’ movements.”
Subject: 
Medical Science
This image depicts a Deep Learning model's prediction of adult Tarāpuka (Black-billed gulls) sitting on nests in a colony. The ground truth is pictured on the left, and the prediction is on the right. Image courtesy of Saif Khan.

Using Deep Learning to detect braided river bird populations

"To be honest, being an ecologist, I was a bit nervous to approach people from advanced data science space. But this fear diminished quickly as all involved in the project was more than ready to understand my needs and was ready to work with my strengths.
Subject: 
Biology
Person wearing a virtual reality headset. Image by Wren Handman from Pixabay

AI software learns, tracks and predicts cybersickness in virtual reality users

“It would take me a day to train one machine learning model [on my desktop], but I'd train something like 56 models in a day on Mahuika.”
Subject: 
Medical Science
Photo of NIWA's Baring Head atmospheric station. Photo by Dave Allen, NIWA.

Automating workflows to help scientists address crucial carbon cycle questions

"The transformation of our CYLC setup into a fully automated and more flexible workflow has had a remarkable impact on our research processes."
Subject: 
Earth Science
View of a speaker talking to slides at the front of the room.

Entering a new era in genetic analysis

“We’re looking forward to be able to learn, discuss, try, and then pass this knowledge on.”
Mean distributions of biomass density of two tuna populations with contrasted life history and spatial dynamics, predicted by the reference SEAPODYM models.

Fishing for parallelisation strategies

"This work is the first phase of the SEAPODYM parallelization project, and given the very encouraging results and perspectives, I will be looking forward to continuing our collaboration."
Subject: 
Earth Science
Image of a black hole. Stock image by Alexander Antropov from Pixabay.

Simulating black holes

"Alex and his team quickly pointed out optimisations that could be done to enhance the code further. The result is a more robust codebase that runs faster than before."
Subject: 
Mathematics
Picutre of dark clouds on the horizon of an ocean. Image by Luda Kot from Pixabay.

OpenACC for tropical circulation model

When new A100 GPU resources were brought online at NeSI, it raised an interesting question for Gilles Bellon: Would offloading computations onto GPUs further improve the performance of his code?
Model of temperature and wind in the Drygalski Ice Tongue region of Antarctica.

Automating workflow capabilities

"The workflow has enabled us to run long simulations without constant vigilance."
Subject: 
Earth Science