SC2EGSet: StarCraft II Esport Replay and Game-state Dataset
Abstract As a relatively new form of sport, esports offers unparalleled data availability. Our work aims to open esports to a broader scientific community by supplying raw and pre-processed files from StarCraft II esports tournaments. These files can be used in statistical and machine learning model...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02510-7 |
| _version_ | 1797453971859701760 |
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| author | Andrzej Białecki Natalia Jakubowska Paweł Dobrowolski Piotr Białecki Leszek Krupiński Andrzej Szczap Robert Białecki Jan Gajewski |
| author_facet | Andrzej Białecki Natalia Jakubowska Paweł Dobrowolski Piotr Białecki Leszek Krupiński Andrzej Szczap Robert Białecki Jan Gajewski |
| author_sort | Andrzej Białecki |
| collection | DOAJ |
| description | Abstract As a relatively new form of sport, esports offers unparalleled data availability. Our work aims to open esports to a broader scientific community by supplying raw and pre-processed files from StarCraft II esports tournaments. These files can be used in statistical and machine learning modeling tasks and compared to laboratory-based measurements. Additionally, we open-sourced and published all the custom tools that were developed in the process of creating our dataset. These tools include PyTorch and PyTorch Lightning API abstractions to load and model the data. Our dataset contains replays from major and premiere StarCraft II tournaments since 2016. We processed 55 “replaypacks” that contained 17930 files with game-state information. Our dataset is one of the few large publicly available sources of StarCraft II data upon its publication. Analysis of the extracted data holds promise for further Artificial Intelligence (AI), Machine Learning (ML), psychological, Human-Computer Interaction (HCI), and sports-related studies in a variety of supervised and self-supervised tasks. |
| first_indexed | 2024-03-09T15:30:35Z |
| format | Article |
| id | doaj.art-d21589c33ca64ffcaaac7a304f94ada0 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-09T15:30:35Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-d21589c33ca64ffcaaac7a304f94ada02023-11-26T12:17:56ZengNature PortfolioScientific Data2052-44632023-09-0110111210.1038/s41597-023-02510-7SC2EGSet: StarCraft II Esport Replay and Game-state DatasetAndrzej Białecki0Natalia Jakubowska1Paweł Dobrowolski2Piotr BiałeckiLeszek KrupińskiAndrzej Szczap3Robert Białecki4Jan Gajewski5Warsaw University of Technology, Electronics and Information TechnologySWPS University, Neurocognitive Research CenterPolish Academy of Sciences, Institute of PsychologyAdam Mickiewicz University in Poznań, Mathematics and Computer ScienceJózef Piłsudski University of Physical Education in Warsaw, Physical EducationJózef Piłsudski University of Physical Education in Warsaw, Physical EducationAbstract As a relatively new form of sport, esports offers unparalleled data availability. Our work aims to open esports to a broader scientific community by supplying raw and pre-processed files from StarCraft II esports tournaments. These files can be used in statistical and machine learning modeling tasks and compared to laboratory-based measurements. Additionally, we open-sourced and published all the custom tools that were developed in the process of creating our dataset. These tools include PyTorch and PyTorch Lightning API abstractions to load and model the data. Our dataset contains replays from major and premiere StarCraft II tournaments since 2016. We processed 55 “replaypacks” that contained 17930 files with game-state information. Our dataset is one of the few large publicly available sources of StarCraft II data upon its publication. Analysis of the extracted data holds promise for further Artificial Intelligence (AI), Machine Learning (ML), psychological, Human-Computer Interaction (HCI), and sports-related studies in a variety of supervised and self-supervised tasks.https://doi.org/10.1038/s41597-023-02510-7 |
| spellingShingle | Andrzej Białecki Natalia Jakubowska Paweł Dobrowolski Piotr Białecki Leszek Krupiński Andrzej Szczap Robert Białecki Jan Gajewski SC2EGSet: StarCraft II Esport Replay and Game-state Dataset Scientific Data |
| title | SC2EGSet: StarCraft II Esport Replay and Game-state Dataset |
| title_full | SC2EGSet: StarCraft II Esport Replay and Game-state Dataset |
| title_fullStr | SC2EGSet: StarCraft II Esport Replay and Game-state Dataset |
| title_full_unstemmed | SC2EGSet: StarCraft II Esport Replay and Game-state Dataset |
| title_short | SC2EGSet: StarCraft II Esport Replay and Game-state Dataset |
| title_sort | sc2egset starcraft ii esport replay and game state dataset |
| url | https://doi.org/10.1038/s41597-023-02510-7 |
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