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Data Analysis and Visualization in Python for Ecologists.
lang: Jupyter Notebook
stars: 168
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A community learning resource for Python-based computing in the geosciences.
lang: Jupyter Notebook
stars: 67
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Help us build the most accessible and accurate climate tech resource on the planet.
lang: Jupyter Notebook
stars: 82
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Ocean and sea-ice model analysis tools and examples.
lang: Jupyter Notebook
stars: 54
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Datasets, code and virtual workspace for the Climate Change ATLAS.
lang: Jupyter Notebook
stars: 98
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Converts battery testing data from native formats to a standardized HDF5 file.
lang: Jupyter Notebook
stars: 49
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A collection of scripted tools developed to inform risk analytics for the World Bank's Country Climate and Development Report risk screening activities.
lang: Jupyter Notebook
stars: 21
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Set of tools to calculate degradation responses and degradation related parameters for PV.
lang: Jupyter Notebook
stars: 36
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The aim of the project is to build an open source PV forecast that is free and easy to use.
lang: Jupyter Notebook
stars: 96
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Aims to make the data about trees in Great Britain more accessible and understandable, whilst still maintaining good industry practices.
lang: Jupyter Notebook
stars: 8
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A comprehensive benchmark dataset designed to enhance the development of machine learning models for instance segmentation of agricultural field boundaries.
lang: Jupyter Notebook
stars: 91
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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.).
lang: Jupyter Notebook
stars: 376
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A versatile, dynamic systems-optimization modeling framework developed by the IIASA Energy, Climate, and Environment (ECE) Program since the 1980s.
lang: Jupyter Notebook
stars: 138
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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.
lang: Jupyter Notebook
stars: 46
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A Bayesian inference approach for using terrestrial ecosystem observations to optimize terrestrial carbon cycle model states and processes parameters.
lang: Jupyter Notebook
stars: 9
last activity: