SFB/Transregio 161 "Quantitative Methods for Visual Computing"

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3,621 to 3,630 of 3,661 Results

df_holo.tab Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality Tabular Data - 112.3 KB - 13 Variables, 926 Observations - UNF:6:UXU37lC46OcL0ZjIEAzlRA== Dataframe with each participant's preferred IPD, as well as passing distances with respect to each virtual agent (separated by sex) on each trial for both experimental blocks
Inference_CHI22_2.R Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality R Syntax - 9.7 KB - MD5: 4fe9f0ea8b83f11c078b34baa0079992 Data analysis Code
like_data.tab Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality Tabular Data - 2.7 KB - 3 Variables, 252 Observations - UNF:6:PP1EuYfA1WaWRDaJj4iRBA== Dataframe with Likeability Data. Participants' responses for the likeability of the virtual agents recorded on a 5 point Likert-Scale: 1 = "I strongly dislike the agent"; 5 = "I strongly like the agent"
Questionnaire_Demographics_Data_r2.tab Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality Tabular Data - 978 B - 4 Variables, 54 Observations - UNF:6:KI4Fyb6PjhJNuL7/Y10ARQ== Dataframe with demographics data.
vote_perceived_gender.tab Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality Tabular Data - 4.4 KB - 7 Variables, 51 Observations - UNF:6:0Pmp+AHdf7fH9Gdom/V4Dg== Dataframe with perceived gender. Column names "F1", "F2", "M1", "M2", "robot", "Pillar" correspond to the name of the virtual agents used in the study. The order and their appearances are the same as Figure 2 in the manuscript. F1 stands for "Female 1"; F2 stands for "Female 2"; M1 stands for "Male 1", M2 stands for "Male 2". The folder contains t...
SFB/Transregio 161 Data Management Plan 2019-2023 Jul 8, 2020 - SFB-TRR 161 INF "Collaboration Infrastructure" 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...
Data Management Plan.docx Jul 8, 2020 - SFB/Transregio 161 Data Management Plan 2019-2023 MS Word - 153.3 KB - MD5: 2fa6460bf44e5879403a7f94f8d952a4 Source Original source document
Data Management Plan.pdf Jul 8, 2020 - SFB/Transregio 161 Data Management Plan 2019-2023 Adobe PDF - 516.1 KB - MD5: 743f7ce4d555e7dfa10876971aad21bc Data Management Plan of SFB/Transregio 161 for the second funding period.
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...
bat.png May 28, 2020 - Runtime performance measurements of interactive visualisation algorithms PNG Image - 668.2 KB - MD5: a3e7399fe3a20c80bfba9e0c4483cc06 DocumentationExample Sample rendering of the "bat.dat" data set,

df_holo.tab

Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality

Tabular Data - 112.3 KB - 13 Variables, 926 Observations -

Dataframe with each participant's preferred IPD, as well as passing distances with respect to each virtual agent (separated by sex) on each trial for both experimental blocks

Inference_CHI22_2.R

Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality

R Syntax - 9.7 KB -

Data analysis Code

like_data.tab

Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality

Tabular Data - 2.7 KB - 3 Variables, 252 Observations -

Dataframe with Likeability Data. Participants' responses for the likeability of the virtual agents recorded on a 5 point Likert-Scale: 1 = "I strongly dislike the agent"; 5 = "I strongly like the agent"

Questionnaire_Demographics_Data_r2.tab

Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality

Tabular Data - 978 B - 4 Variables, 54 Observations -

Dataframe with demographics data.

vote_perceived_gender.tab

Mar 8, 2022 - Proxemics for Human-Agent Interaction in Augmented Reality

Tabular Data - 4.4 KB - 7 Variables, 51 Observations -

Dataframe with perceived gender. Column names "F1", "F2", "M1", "M2", "robot", "Pillar" correspond to the name of the virtual agents used in the study. The order and their appearances are the same as Figure 2 in the manuscript. F1 stands for "Female 1"; F2 stands for "Female 2"; M1 stands for "Male 1", M2 stands for "Male 2". The folder contains t...

SFB/Transregio 161 Data Management Plan 2019-2023

Jul 8, 2020 - SFB-TRR 161 INF "Collaboration Infrastructure"

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...

Data Management Plan.docx

Jul 8, 2020 - SFB/Transregio 161 Data Management Plan 2019-2023

MS Word - 153.3 KB -

Source

Original source document

Data Management Plan.pdf

Jul 8, 2020 - SFB/Transregio 161 Data Management Plan 2019-2023

Adobe PDF - 516.1 KB -

Data Management Plan of SFB/Transregio 161 for the second funding period.

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...

bat.png

May 28, 2020 - Runtime performance measurements of interactive visualisation algorithms

PNG Image - 668.2 KB -

DocumentationExample

Sample rendering of the "bat.dat" data set,

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