All software

GOLEM

GOLEM is a modelling platform for thermal-hydraulic-mechanical and non-reactive chemical processes in fractured and faulted porous media, based on the MOOSE framework

pycomlink

A python toolbox for deriving rainfall information from commercial microwave link (CML) data.

GSTools

GSTools is a Python-based software suite for solving a wide range of geostatistical problems. GSTools provides methods for generating different classes of correlated random fields; it can perform kriging, variogram estimation and much more.

AMIRIS

AMIRIS is the open Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. It aims at enabling scientists to dissect the complex questions arising with respect to future energy markets, their market design, and energy-related policy instruments.

BrainPrint

Our BrainPrint tools provide shape descriptors of neuroanatomical structures and require a FreeSurfer or FastSurfer segmentation as a pre-processing step. BrainPrint is based on “ShapeDNA” a spectral shape descriptor.

ESMValCore

Materials Learning Algorithms

MALA (Materials Learning Algorithms) is a data-driven framework to accelerate electronic structure calculations based on machine learning. Its purpose is to construct neural-network surrogate models for bypassing computationally expensive steps in state-of-the-art density functional simulations.

XRAYPAC

A software package for modeling x-ray-induced dynamics of matter

BornAgain

Software to simulate and fit neutron and x-ray reflectometry and grazing-incidence small-angle scattering. The latter is computed as usual, using the distorted-wave Born approximation, hence the name. Users can set up arbitrary multilayer samples with interface roughness and embedded nanoparticles.

BlenderProc

A procedural Blender pipeline for photorealistic training image generation

JEMRIS

JEMRIS is a versatile, open-source, multi-purpose MRI simulator. It is well-suited for research and education.

RiboDetector

RiboDetector is designed to rapidly and accurately detect rRNA sequences from metagenomic, metatranscriptomic, and ncRNA sequencing data. It has been optimized for use with both CPUs and GPUs. It outperforms existing software by delivering 10-50x faster runtime and ~10x fewer false classifications.