Unveiling the Competitive Esports Physiological, Affective, and Video (CEPAV) Dataset: A Resource for Advancing Affective Science
Affective science is an interdisciplinary field bridging psychology, neuroscience, computer science, and other disciplines to unravel the complexities of emotional experiences and their influence on human behavior. Comprehensive multimodal datasets are crucial for advancing this field, yet they are often challenging and costly to create. This challenge inspired our team to develop and share the Competitive Esports Physiological, Affective, and Video (CEPAV) dataset, a resource designed to support innovative research in affective science.
In creating and sharing the CEPAV dataset, we were mindful of researchers and students who may not have access to the resources required to collect such comprehensive data themselves. For those without large grants or extensive infrastructure, accessing detailed multimodal datasets like ours can be a significant challenge. By openly sharing our work, we hope to provide a valuable resource for piloting analyses, testing initial hypotheses, or developing novel methods. This openness aligns with our commitment to fostering inclusivity and innovation in affective science, empowering researchers at all levels to contribute to the field regardless of their funding or institutional support.
The Study Behind the Dataset
Esports, where highly trained players compete in video games, provided the ideal environment for collecting our data. Gamers perform under intense pressure while seated in controlled settings, enabling real-time monitoring of emotional and physiological responses. This setup allowed us to explore how emotional states influence performance, uncovering valuable insights into the dynamics of affective responses in high-stakes situations.
Our large-scale study involved 300 participants in a three-phase experiment designed to test the effects of a Synergistic Mindsets Intervention (SMI) on performance. Participants were monitored during two laboratory sessions – including esports tournament – and their daily gaming routines. We collected over 750 hours of cardiovascular, behavioral, and video data alongside self-reported measures of gaming-related affect, well-being, and ill-being.
The multimodal nature of the dataset sets it apart, combining physiological signals, behavioral observations, video recordings, and open-text responses. We standardized data from three different devices with varying formats and sampling rates to ensure usability. This process allowed us to create user-friendly files that retain the richness and detail needed for in-depth analysis.
Applications of the CEPAV Dataset
To simplify the use of the dataset, we created two explanatory videos discussing the study’s motivation and methodology, as well as the structure of the dataset. These resources, along with the dataset itself, are freely accessible under a CC-By Attribution 4.0 International license via the Open Science Framework (OSF). The CEPAV dataset offers immense potential for researchers across disciplines:
- Psychology: Investigating the relationship between self-reported emotions and physiological responses.
- Computer Science: Developing machine learning algorithms for automated emotion detection.
- Physics and Mathematics: Applying signal processing techniques and verifying mathematical models.
- Gaming Science: Examining factors affecting performance optimization and exploring the emotional dynamics of esports players.
Publishing Raw Data: Opportunities and Considerations
Finally, while sharing processed data is becoming popular in psychology (e.g., to facilitate the replication of analyses), publishing raw data allows for broader usage and greater flexibility in how datasets can be applied. Raw data provides researchers with the opportunity to explore novel questions, apply alternative analytical methods, or develop innovative tools, significantly expanding the dataset's utility.
However, publishing raw data requires careful planning from the outset of a study. Researchers must ensure that they collect appropriate informed consent from participants, explicitly outlining how their data will be shared and used. This transparency not only protects participants' rights but also aligns with ethical standards, fostering trust and openness in the scientific process.
Fostering Collaboration and Innovation
By sharing the CEPAV dataset, we aim to inspire collaboration and innovation in affective science. This resource is freely available under open science principles, allowing researchers to explore diverse applications, from signal processing to the psychology of performance. We hope it serves as a foundation for new discoveries and advancements in understanding emotions.
We welcome feedback, ideas, and collaborations, and are happy to provide guidance to help maximize the dataset's utility. Feel free to reach out to us at macbehnke@gmail.com.
Follow the Topic
-
Scientific Data
A peer-reviewed, open-access journal for descriptions of datasets, and research that advances the sharing and reuse of scientific data.
Related Collections
With collections, you can get published faster and increase your visibility.
Epidemiological data
Publishing Model: Open Access
Deadline: Mar 27, 2025
Data for epigenetics research
Publishing Model: Open Access
Deadline: Mar 28, 2025
Please sign in or register for FREE
If you are a registered user on Research Communities by Springer Nature, please sign in