All software
CoeusAI
The CoeusAI QGIS plugin is designed for exploration of multiband geospatial datasets. It lets the user iteratively train and retrain segmentation models in seconds. A combination of Deep learning and traditional machine learning is used, leveraging the best of both methods.
- deep learning
- gis
- Machine learning
- + 2
- Python
ConvolutionalNeuralOperators.jl
Julia package to implement Convolutional Neural Operators
- Julia Package
- Machine learning
- Scientific machine learning
- Julia
CoupledNODE
CoupledNODE.jl is a SciML repository that extends NODEs (Neural Ordinary Differential Equations) to C-NODEs (Coupled Neural ODEs), providing a data-driven approach to modelling solutions for multiscale systems when exact solutions are not feasible.
- Julia Language
- Julia Package
- Machine learning
- + 1
- Julia
- Shell
diffWOFOST
The python package diffWOFOST is a differentiable implementation of WOFOST models using torch, allowing gradients to flow through the simulations for optimization and data assimilation.
- Data Analysis
- deep learning
- High performance computing
- + 2
- Python
document-segementation
Tool for identifying document boundaries and categories from VoC inventories.
- AI
- deep learning
- Machine learning
- + 2
- Jupyter Notebook
- Python
e2e-Dutch
Coreference resolution for Dutch: automatically links references to the same entity in Dutch written texts.
- Machine learning
- Text analysis & natural language processing
- C++
- Python
- Shell
EEG epilepsy diagnosis
R package developed to extract features from multivariate time series from EEG data and feed them into a random forest classifier.
- Machine learning
- R
- Rebol
2025-stereograph
This repository contains pre-trained models, computed results, and analysis code for evaluating machine learning approaches (Random Forests, FCNs, and GNNs) on gamma-ray event reconstruction tasks.
- Astroparticle physics
- cta
- deep learning
- + 2
- Jupyter Notebook
- Shell
GammaLearn
GammaLearn is a collaborative project to apply deep learning to the analysis of low-level Imaging Atmospheric Cherenkov Telescopes such as CTA. It provides a framework to easily train and apply models from a configuration file. Learn more at https://purl.org/gammalearn
- cta
- deep learning
- Gamma-ray telescopes
- + 1
- Dockerfile
- Python
SKAO Science Data Challenge 1 Solution Workflow
The SKA Science Data Challenge 1 (SDC1, https://astronomers.skatelescope.org/ska-science-data-challenge-1/) tasked participants with identifying and classifying sources in synthetic radio images. Here we present an environment and workflow for producing a solution to this challenge that can easil...
- astronomy
- Data
- Machine learning
- + 2
- Dockerfile
- Jupyter Notebook
- Makefile
- + 2
EXCITED Machine Learning Workflow
An open workflow for creating machine learning models for estimating the global biospheric CO2 exchange.
- CO2
- Data Analysis
- geospatial
- + 4
- Jupyter Notebook
- Python
Forecasting the grid emission factor for the Netherlands
A workflow to produce emission factor forecasts for the electricity mix of the Netherlands, up to 7 days ahead.
- electricity grid
- emission factor
- Forecasting
- + 1
- Dockerfile
- Python