Dataset for "RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality"
The aggregated data file containing 140 participants' data collected and analysed in the CHI 2025 paper "RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality".
Each participant completed two half-hour sessions and continuous measures for both sessions were aggregated in 60-second intervals, resulting in 31 rows per session and 62 rows per participant. The measures used in this study fall into three groups:
- Pre-measures include demographic information, gaming experience and preferences, personality and gamer traits, baseline emotions and physiology.
- Exposure measures include Retrospective method emotion and keypoint measures, experience sampling method (ESM) measures, and various physiological measures.
- Post-measures include measures of flow state, intrinsic motivation, multimodal presence, and simulator sickness, and participants' qualitative evaluations of RetroSketch and ESM measures.
Cite this dataset as:
Potts, D.,
Gada, M.,
Gupta, A.,
Goel, K.,
Krzok, K.,
Pate, G.,
Hartley, J.,
Weston-Arnold, M.,
Aylott, J.,
Clarke, C.,
Jicol, C.,
Lutteroth, C.,
2025.
Dataset for "RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality".
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01489.
Export
Data
Retro_140Ps … minute_measures.csv
application/csv (45MB)
Creative Commons: Attribution-Share Alike 4.0
Measures taken during each VR gameplay session of Participants' RetroSketch scores and physiology, sampled at 60-second intervals, as well as ESM scores sampled at 5-minute intervals.
Retro_140Ps … participant_measures.csv
application/csv (293kB)
Creative Commons: Attribution-Share Alike 4.0
Participants' pre-session measures including physiological and emotional baselines, as well as personality traits and characteristics.
Retro_140Ps … session_measures.csv
application/csv (6MB)
Creative Commons: Attribution-Share Alike 4.0
Participants' post session measures after exiting VR. These include flow measures, intrinsic motivation, VR presence, exertion, and simulator sickness.
Code
RetroSketch-Analysis.r
text/plain (96kB)
Creative Commons: Attribution-Share Alike 4.0
R analysis script (v4.4.1) used to analyse the aggregated dataset. This analysis file will only work with the aggregated CSV file made available on restricted access.
digital-retrosketch … 2025.zip
application/zip (232MB)
Creative Commons: Attribution-Share Alike 4.0
A snapshot of the RetroSketch GitHub repository (02/2025) containing the Unity project and source code for the digital version of RetroSketch.
Mixed access regime: Due to the nature of the consent obtained, data provided by participants is available on request to bona fide researchers only.
GitHub repository containing the digital RetroSketch tool made in Unity
Creators
Dominic Potts
University of Bath
Miloni Gada
University of Bath
Aastha Gupta
University of Bath
Kavya Goel
University of Bath
Klaus Philipp Krzok
University of Bath
Genevieve Pate
University of Bath
Joe Hartley
University of Bath
Mark Weston-Arnold
University of Bath
Jakob Aylott
University of Bath
Christopher Clarke
University of Bath
Crescent Jicol
University of Bath
Christof Lutteroth
University of Bath
Contributors
University of Bath
Rights Holder
Documentation
Data collection method:
For the methodology and apparatus of the data collection, please refer to the associated paper.
Technical details and requirements:
The study required participants to play one of five VR games (Assetto Corsa Competizione, Garden of the Sea, Half-Life Alyx, I Expect You To Die, and Red Matter) over two 30 minute sessions using a Vive Pro Eye VR headset and controllers. Physiological measures were collected using the eye tracker in the VR headset (pupillometry), a Shimmer3 GSR+ tethered to a participant's middle and ring finger (EDA), a Polar H10 HR monitor chest strap (HR and HRV), and a Vive face tracker (facial tracking). All physiological measures were sent to a PC (Intel 13900K, Nvidia GTX 4090 and 64GB of DDR5 RAM) running the Unity data collection application over serial and Bluetooth (BLE protocol), which recorded all measures at a sample rate of approximately 40-50 Hz using the EmoSense SDK. R (v4.4.1) was used to analyse the data.
Additional information:
The main aggregate data file consists of the following measures: Meta Data: Participant ID, session number, condition (Retro or Retro_ESM), and game played. Pre-measures: Demographics, baseline emotions, big 5 personality traits (B5), immersive tendencies (ITQ), game genre preference, Brain Hex gamer types, tondello gamer traits, baseline simulator sickness (SSQ), and baseline physiology (calibration). Exposure measures: Retro emotion measures (aggregated for every 60-second interval for both the 'Prior' 60s from the interval and over a 'Window' of 30s before and after the interval), Retro keypoint measures, experience sampling method (ESM) measures, and physiological measures aggregated for the same 60-second intervals (both 'Prior' and 'Window') Post measures: PPL flow state (PPL-FSQ), flow state short (FSS), intrinsic motivation (IMI), multimodal presence (MPS), simulator sickness (SSQ), and qualitative questions comparing RetroSketch and ESM. For further details about the measures, please refer to the associated paper.
Methodology link:
Potts, D., Gada, M., Gupta, A., Goel, K., Krzok, K. P., Pate, G., Hartley, J., Weston-Arnold, M., Aylott, J., Clarke, C., Jicol, C., and Lutteroth, C., 2025. RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality. In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ACM, 1-25. Available from: https://doi.org/10.1145/3706598.3713957.
Funders
Horizon Europe Framework Programme
https://doi.org/10.13039/100018693
EMIL – The European Media and Immersion Lab
101070533
Innovate UK
https://doi.org/10.13039/501100006041
EMIL – The European Media and Immersion Lab
10044904
Royal Society
https://doi.org/10.13039/501100000288
Affective Design Tools for Virtual Reality
INF\R1\221058
Publication details
Publication date: 25 April 2025
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01489
URL for this record: https://researchdata.bath.ac.uk/1489
Related papers and books
Potts, D., Gada, M., Gupta, A., Goel, K., Krzok, K. P., Pate, G., Hartley, J., Weston-Arnold, M., Aylott, J., Clarke, C., Jicol, C., and Lutteroth, C., 2025. RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality. In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ACM, 1-25. Available from: https://doi.org/10.1145/3706598.3713957.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Dominic Potts
Faculty of Science
Computer Science
Research Centres & Institutes
REal and Virtual Environments Augmentation Labs (REVEAL)
