Netherlands eScience Center
National platform for the development and application of domain overarching software and methods for the scientific community
aiproteomics
Generate and compare deep learning models for generating synthetic mass spectral libraries
- Machine learning
- mass spectrometry
- phosphoproteomics
- + 1
- Jupyter Notebook
- Python
- Shell
Allelic Variation Explorer
If you are studying how single nucleotide polymorphisms are clustered in genomic samples, then the Allelic Variation Explorer can help you visualize them.
- Visualization
- AngelScript
- Dockerfile
- Python
AMUSE
Combine existing numerical codes in an easy to use Python framework. With AMUSE you can simulate objects such as star clusters, proto-planetary disks and galaxies.
- High performance computing
- Multi-scale & multi model simulations
- Workflow technologies
- Assembly
- C
- C++
- + 23
ANNUBeS
ANNUBeS is a deep learning framework meant to generate synthetic data and train on them neural networks aimed at developing and evaluating animals' training protocols in neuroscience.
- neural networks
- Neuroscience
- Python
Annular
Annular is a setup for running coupled energy system models with the aim of modeling flexibility scheduling and the policy regulations that affect the behavior of flexibility providers.
- Julia
- Jupyter Notebook
- Python
APE
A CLI, Java API and RESTful API for the automated generation of computational pipelines (scientific workflows) from large collections of computational tools.
- Java
- Program synthesis
- Workflow technologies
- ANTLR
- Common Workflow Language
- Java
Arena-Crowds
Arena-Crowds is Python scripts for the data analysis to estimate crowd density based on WiFi positioning.
- Computer networks
- Networks
- Network Science
- Jupyter Notebook
- Python
ARISE biocloud
The Biocloud is the underlying digital back-end infrastructure of the ARISE program.
- Go Template
- HCL
- Jinja
- + 2
asreview-simulation
Command line interface to simulate an ASReview analysis using a variety of prior sampling strategies, classifiers, feature extractors, queriers, balancers, and stopping rules, all of which can be configured to run with custom parameterizations.
- AI
- ASReview
- Automated Systematic Review
- + 4
- Python
AttentionLayer.jl
Implements the Attention mechanism in Julia as a modular Lux layer
- Julia Package
- Machine learning
- Scientific machine learning
- Julia
Baklava
Deploys a Kubernetes cluster and big data services on the cloud.
- Big data
- High performance computing
- Optimized data handling
- Dockerfile
- HCL
- Python
- + 2
haddock3-webapp
Web app to build haddock3 configuration and run it.
- CSS
- Dockerfile
- JavaScript
- + 2