Open Software Toolboxes

Open Source Toolboxes 2021-2024

An essential aspect of this CRC’s knowledge transfer is the provision of toolboxes and software codes to the scientific community. CRC members have made significant contributions by providing the following Open Software Toolboxes:


Aidaqc is an automated and simple tool for fast-quality analysis of animal MRI data.

Julearn is a user-oriented machine-learning library. This library provides users with the possibility of testing ML models directly from pandas dataframes while keeping the flexibility of using scikit-learn’s models.

DataLad is a free and open-source distributed data management system that keeps track of one’s data, creates structure, ensures reproducibility, supports collaboration, and integrates with widely used data infrastructure.

This Python-based automated toolbox was developed to compare key locomotor kinematic features across physiological states, perturbations, and species. This development was only possible by synergizing the work on human (Weiss-Blankenhorn, Grefkes-Hermann, Fink; Z03), mouse (Gatto, Korotkova; Z02), and fly (Büschges) locomotion with theoretical and computational expertise (Daun; INF).

    • The Ito lab runs its own Github site ( to transfer computational technology developed in their lab. Computational tools concern aligning (registering) brain samples of many individuals to a common standard template (Parallel-Fiji-CMTK-Registration). This is a key feature for integrating light microscopy and electron microscopy connectome data. This solution is faster and easier to use than the original systems developed elsewhere (i.e., the Univ. Cambridge and HHMI Janalia).

This open-source toolbox allows the calculation of dynamic graphs based on phase synchronization in experimental data. It enables an analysis of the development of network connectivity between multiple recording sites (e.g., in electroencephalography (EEG) or magnetoencephalography (MEG) data) with a high temporal resolution. Furthermore, the toolbox computes several graph metrics (such as cluster dynamics, node degree, HUB nodes) via the Brain Connectivity toolbox (

This open-source toolbox was developed to analyze effective functional connectivity between brain regions within and between different frequency bands. The method can be applied to analyze any network of interacting oscillators as a dynamic system using signals of the network elements, including the coupling between distant brain regions using high-time resolution signals, such as EEG or MEG.