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URPP Adaptive Brain Circuits in Development and Learning (AdaBD)

HDDA Platform

High-dimensional data analysis platform

The project

The high-dimensional data analysis (HDDA) data-science platform is based on the software “dataspace” (dspace), which has been created and developed by Sepp Kollmorgen. Dspace is based on the assumption that a substantial part of data analysis tasks can be broken down to a few primitives. These primitives are made available to combine in a Lego-like setting within a graphical user interface (GUI). All actions performed are automatically logged and expressed as programming code. Code-based workflows and GUI-based workflows can merge seamlessly, enabling analyses without coding but also integration with users’ existing code and analysis pipelines. Data and expansions of dspace can be easily shared. Dspace and dspace documentation is available to researchers of our URPP for download.

During the last years, we significantly improved structure and design of the platform. Projects supported by dspace are ongoing with several research groups of the URPP AdaBD as well as with some additional UZH research groups. Currently, we are improving, refining and testing Dspace expansions enabling the analysis and exploration of light-sheet microscopy data (in particular data produced by the mesoSPIM platform) and of analysis of unimodal and multimodal time series data (e.g. analysis of behavior and neural activity). Further, we are developing and testing a dspace expansion enabling the analysis and exploration of data held in the datajoint format.


Services of the platform

  • Onboarding of new users including discussion of needs with respect to data-analysis/data-science
  • Instruction and training on how to use dspace to meet the user’s needs
  • Download of dspace and dspace documentation, such as video tutorials
  • Integration with other platforms of the URPP, such as the mesoSPIM platform
  • Organization of user meetings and outreach activities
  • Customization/extension of dspace to accomodate new types of data/analysis as well as to increase robustness and ease of use.



Sepp Kollmorgen, PhD
Student assistants (Anna Bickel, Rayen Mahjoub, Elias Salameh)



Schoenfeld G, Kollmorgen S, Lewis C, Bethge P, Reuss AM, Aguzzi A, Mante V, Helmchen F (2021) Dendritic integration of sensory and reward information facilitates learning. bioRxiv,