Community

"The benefits are of particular relevance to the regridding of atmospheric data on the globe."

“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.”

"NeSI is really the only platform for producing our hydrologic condition digital elevation models at the national scale."

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

“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.”

"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.

"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."

“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.”

"The transformation of our CYLC setup into a fully automated and more flexible workflow has had a remarkable impact on our research processes."

“We’re looking forward to be able to learn, discuss, try, and then pass this knowledge on.”