All software

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.

CellDetection

An open-source Python package, offering advanced AI methods for the identification and segmentation of biomedical objects, such as cells, in image data. Includes trained models that work out-of-the-box for a wide range of use cases.

CellRank

CellRank is a computational framework to study cellular fate decisions based on various types of single-cell genomics data. CellRank scales to large cell numbers, is fully compatible with the scverse ecosystem, and is easy to use.

Earth System Model Evaluation Tool (ESMValTool)

ESMValCore

FastSurfer

FastSurfer is a fast and accurate deep-learning pipeline for the analysis of human brain MRI. FastSurfer provides a fully compatible FreeSurfer alternative for volumetric and surface-based thickness analysis, also supporting sub-mm resolutions, and sub-segmentation of neuroanatomical structures.

FatSegNet

A fully automated deep learning pipeline for adipose tissue (visceral and subcutaneous) segmentation on abdominal Dixon MRI.

FSQC

This package provides quality assurance / quality control scripts for FastSurfer- or FreeSurfer-processed structural MRI data.

hCoCena - horizontal integration and analysis of transcriptomics datasets

hCoCena is an R-package that allows you to comprehensively and easily analyse your transcriptomics data. It also provides the option to integrate several datasets using a network-based integration approach and analyse them jointly. Tutorials and a Docker image exist to help you get to know the tool.

Heat

Heat is a flexible and seamless open-source software for high performance data analytics and machine learning. It provides highly optimized algorithms and data structures for tensor computations using CPUs, GPUs and distributed cluster systems on top of MPI.

iqtools

Collection of code for working with offline complex valued time series data in Python.

KaHyPar

KaHyPar is a fast, high-quality, and scalable algorithm for partitioning graphs and hypergraphs with billions of edges. It finds applications in minimizing communication costs for distributed (hyper)graph computations, quantum circuit simulations, storage sharding in databases, and VLSI design.