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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 stock in a fenced dry area.

New tool revolutionising drought forecasts

“This tool enables us to give more frequent and district-level predictions of rainfall, dryness, and drought. Providing advanced warning of future dry spells will be invaluable.”
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Footbridge damaged by flood, Te Auaunga creek walkway. Auckland 28 January 2023. Photo by Paul Left.

A scalable digital elevation builder for flood mapping

"NeSI is really the only platform for producing our hydrologic condition digital elevation models at the national scale."
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Georgina Rae (NeSI) and Ben Taylor (AgResearch) presenting on the NeSI and AgResearch partnership.

Reflections on designing and delivering a future-focused eResearch Platform

Both NeSI and AgResearch are discovering and learning about how best to support and develop a rich national eResearch ecosystem.
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."
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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.
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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.”
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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.”
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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."
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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."
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