Charles Turner

For scientists wanting to work with and analyse earth science data, the standard remains delivering tooling via python packages, and data via HPC or the cloud. For data siloed on an HPC system, this presents a barrier to findability and accessibility.

Building software is often presented as the ultimate in asynchronous collaboration - open a PR, wait for a review, work on something else, and come back when it's a good time for you. The reality can often be...

Python has become one of the cornerstone languages for developing scientific software, thanks to its flexibility, extensibility, ease of use and extensive ecosystem. Whether you’re doing machine learning, processing & visualising data, or running a statistical analysis, there’s a good chance...

Climate models generate *a lot* of data - and this can make it hard for researchers to efficiently access and use the data they need. The solutions of yesteryear include standardised file structures, sqlite databases, and just knowing where to look.