ArticleScientific data2025
AMuCS: Affective multimodal Counter-Strike video game dataset.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- AMuCS: Affective multimodal Counter-Strike video game dataset.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Video games are a versatile and multi-faceted stimulus which can elicit complex player experiences. As a consequence, several datasets have been curated or created for studying human cognition, behaviours, and physiological responses where video games are the primary stimulus. Many of these datasets have a low number of participants or do not have a rich set of modalities and are always recorded in a laboratory setting. To address these issues, we have recorded 256 participants at LAN events while they played the first person shooter, Counter-Strike: Global Offensive. Our dataset consists of several complementary modalities: physiological signals (ECG, EDA, Respiration), behavioural signals (facial expressions, eyetracking, depth images, seat pressure), computer interaction (keyboard and mouse events, game actions), and stimulus information (gameplay video, game logs). We show that the number of participants in our dataset and the variety of modalities recorded is advantageous for training machine learning models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.