Project INF supports the other projects of SFB/Transregio 161 by providing a central approach to data management and an infrastructure for virtual meetings in a large, high-resolution display scenario.
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

11 to 13 of 13 Results
Guidelines on Replication and Research Data Management
Guidelines on Replication and Research Data Management
May 20, 2022
Garkov, Dimitar; Müller, Christoph; Klein, Karsten; Ertl, Thomas; Schreiber, Falk; Task-Force A, 2022, "Guidelines on Replication and Research Data Management", https://doi.org/10.18419/DARUS-2843, DaRUS, V1
This document summarises guidelines for reproducibility in SFB/Transregio 161 by Task Force A (TF-A). It builds upon the data management plan (version 1.0, 2020, https://doi.org/10.18419/darus-632) and focusses on three main points: Provide clear definitions of reproducibility and replicability in the context of TF-A. Coordinate efforts on research...
SFB/Transregio 161 Data Management Plan 2019-2023
SFB/Transregio 161 Data Management Plan 2019-2023
Jul 8, 2020
Müller, Christoph, 2020, "SFB/Transregio 161 Data Management Plan 2019-2023", https://doi.org/10.18419/DARUS-632, DaRUS, V1
The participating universities in SFB/Transregio 161 acknowledge the general importance of research data management as a vital issue for all of their work and provide increasing central support for long-term accessibility and reusability of data, documentation of methods and tools and privacy protection. However, technical and organisational offeri...
Runtime performance measurements of interactive visualisation algorithms
May 28, 2020 - SFB-TRR 161 A02 "Quantifying Visual Computing Systems"
Bruder, Valentin; Müller, Christoph; Frey, Steffen; Ertl, Thomas, 2020, "Runtime performance measurements of interactive visualisation algorithms", https://doi.org/10.18419/DARUS-810, DaRUS, V1
Runtime performance measurements for GPU-based direct volume rendering and GPU-based raycasting of spherical particles on ten different discrete graphics processing units from AMD and NVIDIA. The data set at hand systematically evaluates typical factors influencing performance of interactive visualisations like view-port size, data set size, camera...
Add Data

Log in to create a dataverse or add a dataset.

Log In
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.