ClimateTriage - Impactful open source contributions

A tool to identify disadvantaged communities due to environmental, socioeconomic and health burdens.

Help us build the most accessible and accurate climate tech resource on the planet.

Aims to make the data about trees in Great Britain more accessible and understandable, whilst still maintaining good industry practices.

Converts battery testing data from native formats to a standardized HDF5 file.

Deep learning tools that accelerate the review of motion-triggered wildlife camera images.

A Python package of mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models, primarily supporting the meteorological, climatological and geoscientific communities.

A Python package that provides a simple way to use nighttime lights data from NASA's Black Marble project that provides a product suite of daily, monthly and yearly global nighttime lights.

Aspires to identify key research gaps and opportunities at the intersection of agricultural modelling and machine learning research and support enhanced collaboration and engagement between experts in these disciplines.

A versatile, dynamic systems-optimization modeling framework developed by the IIASA Energy, Climate, and Environment (ECE) Program since the 1980s.

A comprehensive benchmark dataset designed to enhance the development of machine learning models for instance segmentation of agricultural field boundaries.

A library for satellite image processing providing all of the functionality necessary for retrieving, and storing EUMETSAT data.

Allows you to run any large weather model with a consumer grade GPU.

Stands for CLIMate ADAptation and is a probabilistic natural catastrophe impact model, that also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioural, etc.).

Simulating aeolian sediment transport in situations where supply-limiting factors are important, like in coastal environments.

The aim of the project is to build an open source PV forecast that is free and easy to use.