3,571 to 3,580 of 3,637 Results
Jun 21, 2022 -
Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Tabular Data - 163.1 KB - 8 Variables, 39 Observations - UNF:6:VVrs6jWUFW7J3C7Wu5Uflg==
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Jun 21, 2022 -
Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Tabular Data - 116.7 KB - 8 Variables, 28 Observations - UNF:6:yuJS2AQd0ZaBsrEGEnjB7g==
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Jun 21, 2022 -
Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Plain Text - 43.7 KB -
MD5: 642353df65727ff77b01f3a80f2e9612
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ZIP Archive - 380.3 MB -
MD5: 7d44160b07673e160fb83724ac650720
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May 20, 2022 -
Guidelines on Replication and Research Data Management
Adobe PDF - 286.0 KB -
MD5: ea953850363d17e9a9531f9a0d3a1517
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May 2, 2022 -
Design of On-body Tactile Displays to Enhance Situation Awareness in Automated Vehicles
Tabular Data - 1.3 KB - 9 Variables, 21 Observations - UNF:6:YHhX4VFGLE0o6fVxJAlldw==
This datased contains the following information per column: Participant ID, assigned condition (SA-L1 = 1, SA-L3=2), distance, Max Braking intensitiy, TTC, Attention Interference, WM Interference, NASA scores, SART score . |
May 2, 2022 -
Design of On-body Tactile Displays to Enhance Situation Awareness in Automated Vehicles
Adobe PDF - 71.3 KB -
MD5: fcafd668f54f6fc66b2a3471e6dda0c5
Qualitative Questions asked within the Semi-Structured Interview |
7Z Archive - 223.7 MB -
MD5: 43fc85772d525f7cb3ea8a9ee7c00539
Dataset containing data for each participant. Specifically included are :
1. Slope of the EDA tonic in -adaptation.csv ;
2. Raw Electrocardiogram (ECG) data in -ECG.csv;
3. Raw Electroencephalogram (EEG) data in -EEG.csv acquired from Value1 : Fz, P3, P, P4, PO7, Oz and PO8 electrodes.
4. Feedback presented during the N-Back task in -feedback.c... |
Python Source Code - 6.6 KB -
MD5: 0ba2a0a17cec3461196dea43efd8e1d5
Code to preprocess the raw dataset (performance, subjective and Electrodermal activity data) |
Jupyter Notebook - 790.8 KB -
MD5: 89a5f48990f612b286f13e8e9c205074
Code for Statistical analysis of preprocessed data (Jupyter Notebook file) |