The Transregional Collaborative Research Centre 161 “Quantitative Methods for Visual Computing” is an interdisciplinary research centre at the University of Stuttgart and the University of Konstanz, funded by Deutsche Forschungsgemeinschaft (DFG) under project number 251654672. Ulm University and Ludwig-Maximilians-Universität München are participating institutions in the second and third funding period. The Max Planck Institute for Biological Cybernetics in Tübingen in was a participating institution in the first funding period.

The goal of SFB/Transregio 161 is establishing the paradigm of quantitative science in the field of visual computing, which is a long-term endeavour requiring a fundamental research effort broadly covering four research areas, namely quantitative models and measures, adaptive algorithms, interaction and applications. In the third funding period, which started in 2023, new research directions are being approached. One is visual explainability, assessing and quantifying how well the users of a visualisation system understand the phenomena shown visually. The second direction targets mixed reality, covering all forms of augmented and virtual reality as a cross-cutting field of various visual computing subfields, irrespective of applied technology. The third research theme aims to bring research results in the world, moving away from experiments in the laboratory and in the wild to openly accessible applications that provide research results, methods, data sets, and other outcomes from SFB/Transregio 161 to a wide range of stakeholders in academia, industry, teaching, and society in general.

In SFB/Transregio 161, approximately 40 scientists in the fields of computer science, visualisation, computer vision, human computer interaction, linguistics and applied psychology are jointly working on improving the quality of future visual computing methods and applications.

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

1 to 10 of 3,637 Results
Hierarchical Data Format - 8.3 GB - MD5: 17a8463cd8bd77b1e9831c8afbeb18e6
An example TDF file obtained after imaging
Rich Text Format - 7.7 KB - MD5: c24fdf932112ce5169ca3a2f50b9421d
The protocol using in the tissue embedding of the samples
ZIP Archive - 16.2 GB - MD5: e61eeb632022e0cebdabcd74de078a88
An example image sequence obtained after reconstruction
Adobe PDF - 115.6 KB - MD5: 126e03f72fa300a433a71b37e3779158
Processing steps and parameters for the reconstruction with STP v1.5.3
Compressed Archive - 31.1 MB - MD5: 56c8698b29c1b94b17d0ab46b2625e52
Brainacle v1.0
application/java-archive - 48.4 KB - MD5: 071badfee7646059973c9b3f40a59784
An ImageJ plugin to support the export of isosurface meshes to Brainacle
ZIP Archive - 1.4 KB - MD5: 69355ea68ad0817fd873491ab91d81ff
Contains: an installer macro for autorun, and two macros for the import and export of coordinates from and to Brainacle. For instructions see Metadata.
Adobe PDF - 56.7 KB - MD5: 30eb4d3cc627fc675c44c25690412270
Steps to prepare an image sequence in ImageJ for volume visualisation in Brainacle
ZIP Archive - 206.3 MB - MD5: 2fc7b880c94319043cca4dec41238dba
Example data for Brainacle
PNG Image - 42.3 KB - MD5: d7fdd93410b4182a35946b95b5945ef7
The figure referenced in the paper, showing the correlations between the two methods for each gross brain region.
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.