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3,581 to 3,590 of 3,637 Results
Tabular Data - 37.0 KB - 7 Variables, 540 Observations - UNF:6:zLLTaoJyI4/oSsqd1A78/g==
Dataframe with Electrodermal Activity (EDA) Data
Tabular Data - 129.5 KB - 13 Variables, 1068 Observations - UNF:6:1BTACawKEQZOICjP20Y7yw==
Dataframe with minimum/passing distances of each participant
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
R Syntax - 9.7 KB - MD5: 4fe9f0ea8b83f11c078b34baa0079992
Data analysis Code
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"
Tabular Data - 978 B - 4 Variables, 54 Observations - UNF:6:KI4Fyb6PjhJNuL7/Y10ARQ==
Dataframe with demographics data.
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-TRR 161 B05 "Efficient Large Scale Variational 3D Reconstruction" logo
Dec 16, 2021
The central goal of the project is to research and develop high-performance variational methods for large scale 3D reconstruction problems, which are general and accurate while meeting computation time constraints imposed by visual computing applications.
SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings" logo
Dec 16, 2021
High-dimensional data analysis requires dealing with numerous challenges, such as selecting meaningful dimensions, finding relevant projections, and removing noise. As a result, the extraction of relevant and meaningful information from high-dimensional data is a difficult problem. This project aims at advancing the field of quality-metric-driven d...
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