Affective Multimodal Counter-Strike video game dataset (AMuCS) - Public
This dataset consists of data collected from 245 participants. It was recorded at four large LAN events in Switzerland which took place between October 2020 and April 2022. Physiological and behavioural data was recorded from groups of 2 or 4 participants playing a round of team deathmatch in the game CounterStrike: Global Offensive. The gameplay was then continuously self-annotated according to either valence or arousal. *** To gain access to this dataset please follow this procedure: 1. request access using the corresponding button; 2. you need a switch-edu account to register, anyone is entitled to create such an account; 3. download, sign and attach the EULA to the request form; 4. we will then accept your demand upon verification. ***
- Organizational unit
- SIMS - Social Intelligence and Multi-Sensing
- Type
- Dataset
- DOI
- Referenced by the following DOI or ARK
- Keywords
- multimodal, affective computing, video game, physiological signals, electrodermal activity, multiplayer, arousal, valence, ECG, EDA, eyetracking, gameplay
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Datacite metadata
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<datacite:identifier identifierType="DOI">10.26037/yareta:wqmr4bkmrvhc3jkqcscluehjyi</datacite:identifier>
<datacite:creators>
<datacite:creator>
<datacite:creatorName nameType="Personal">Fanourakis, Marios Aristogenis</datacite:creatorName>
<datacite:givenName>Marios Aristogenis</datacite:givenName>
<datacite:familyName>Fanourakis</datacite:familyName>
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<datacite:creator>
<datacite:creatorName nameType="Personal">Chanel, Guillaume</datacite:creatorName>
<datacite:givenName>Guillaume</datacite:givenName>
<datacite:familyName>Chanel</datacite:familyName>
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<datacite:title>Affective Multimodal Counter-Strike video game dataset (AMuCS) - Public</datacite:title>
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<datacite:publisher publisherIdentifier="https://ror.org/01swzsf04" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Université de Genève, Yareta</datacite:publisher>
<datacite:publicationYear>2025</datacite:publicationYear>
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<datacite:subject subjectScheme="keywords">multimodal;affective computing;video game;physiological signals;electrodermal activity;multiplayer;arousal;valence;ECG;EDA;eyetracking;gameplay</datacite:subject>
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<datacite:affiliation xsi:type="datacite:affiliation" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">[UNIGE-CUI] Université de Genève - Centre Universitaire d'Informatique</datacite:affiliation>
<datacite:affiliation xsi:type="datacite:affiliation" affiliationIdentifier="https://ror.org/01swzsf04" affiliationIdentifierScheme="ROR" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">[UNIGE] Université de Genève</datacite:affiliation>
<datacite:affiliation xsi:type="datacite:affiliation" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">[UNIGE-CISA] Université de Genève - Centre Interfacultaire en Sciences Affectives</datacite:affiliation>
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<datacite:contributorName nameType="Personal">Chanel, Guillaume</datacite:contributorName>
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<datacite:dates>
<datacite:date dateType="Issued">2025-05-12T00:00:00Z</datacite:date>
<datacite:date dateType="Collected">2020-10-09T00:00:00Z/2022-04-18T00:00:00Z</datacite:date>
<datacite:date dateType="Created">2025-05-12T13:54:11.874041Z</datacite:date>
<datacite:date dateType="Updated">2026-01-07T15:25:28.75254Z</datacite:date>
<datacite:date dateType="Accepted">2025-06-23T14:49:30.608786456Z</datacite:date>
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<datacite:relatedIdentifier relatedIdentifierType="DOI" relationType="IsReferencedBy">10.36227/techrxiv.170630398.84528625/v1</datacite:relatedIdentifier>
<datacite:relatedIdentifier relatedIdentifierType="DOI" relationType="IsReferencedBy">10.1038/s41597-025-05596-3</datacite:relatedIdentifier>
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<datacite:format>text/csv</datacite:format>
<datacite:format>video/mp4</datacite:format>
<datacite:format>video/x-matroska</datacite:format>
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<datacite:description descriptionType="Abstract">This dataset consists of data collected from 245 participants. It was recorded at four large LAN events in Switzerland which took place between October 2020 and April 2022. Physiological and behavioural data was recorded from groups of 2 or 4 participants playing a round of team deathmatch in the game CounterStrike: Global Offensive. The gameplay was then continuously self-annotated according to either valence or arousal.
***
To gain access to this dataset please follow this procedure:
1. request access using the corresponding button;
2. you need a switch-edu account to register, anyone is entitled to create such an account;
3. download, sign and attach the EULA to the request form;
4. we will then accept your demand upon verification.
***</datacite:description>
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