Chess Multiverse Error & Evaluation Dataset (CMEED v1.0)

🚀 Live Interactive Explorer Explore CMEED through the Chess Multiverse Error Explorer: https://www.chessmultiverse.org/p/chess-multiverse-error-explorer.html The explorer enables interactive investigation of: Error distributions Opening risk profiles Player error fingerprints Tournament pressure effects Position-level error records Large-scale chess analytics The CMEED dataset powers research tools developed through Chess Multiverse. 📊 Dataset Overview The Chess Multiverse Error & Evaluation Dataset (CMEED) is a large-scale open-source chess research dataset containing structured human decision-making errors extracted from official chess broadcasts. Unlike traditional chess databases that primarily focus on games, openings, or engine evaluations, CMEED focuses specifically on player mistakes and decision quality. Each record captures a single: Inaccuracy Mistake Blunder along with: Board position before the move Board position after the move Engine evaluation changes Remaining clock time Player ratings Player titles Opening metadata Tournament metadata Full FEN reconstruction Version 1.0 contains data extracted from official broadcast events spanning January 2026 through May 2026. 📚 Source Data & Provenance CMEED is derived from the official Lichess Broadcast Database. Source broadcasts are distributed under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0) license. The source PGNs contain: Engine evaluations Clock information Opening metadata Tournament metadata Player metadata CMEED transforms these raw broadcast PGNs into a structured research dataset focused on human error behavior. Transformation pipeline: Broadcast PGN ↓ Evaluation Parsing ↓ Error Detection ↓ Position Reconstruction ↓ Metadata Enrichment ↓ CMEED Dataset 📈 Dataset Statistics Metric Value Dataset Version CMEED v1.0 Coverage Period January 2026 – May 2026 Games 106,911 Total Error Records 994,269 Unique Players 32,203 Opening Families 489 Inaccuracies 566,830 Mistakes 195,775 Blunders 231,664 Storage Size ~1.33 GB Format JSON License CC BY-SA 4.0 📦 Monthly Dataset Breakdown Month Error Records January 2026 186,544 February 2026 130,945 March 2026 205,046 April 2026 201,910 May 2026 269,824 Total 994,269 📁 Repository Structure Chess-Multiverse-Error-Evaluation-Dataset-CMEED/ ├── data/ │ ├── cmeed_2026-01.json │ ├── cmeed_2026-02.json │ ├── cmeed_2026-03.json │ ├── cmeed_2026-04.json │ └── cmeed_2026-05.json │ ├── cmeed_v1.parquet ├── README.md ├── CITATION.cff ├── LICENSE ├── CONTRIBUTING.md ├── CODE_OF_CONDUCT.md └── .gitattributes Dataset Formats CMEED is distributed in two formats: Format Description JSON Monthly source datasets Parquet Consolidated research dataset The Parquet release contains all 994,269 error records in a compressed columnar format optimized for analytics, machine learning, and large-scale research workflows. Researchers are encouraged to use the Parquet release for maximum performance. 🗄️ Data Schema (Data Dictionary) Each JSON object represents a single detected player error. Field Type Description error_id String Unique error identifier game_id String Unique game identifier source_file String Source dataset file event String Tournament name broadcast_name String Broadcast title game_url String Original game URL eco String ECO code opening String Opening name date String Game date year Integer Year round String Tournament round board String Board number white String White player black String Black player player String Player committing the error white_elo Integer White rating black_elo Integer Black rating player_elo Integer Player rating white_title String White title black_title String Black title player_fide_id String FIDE identifier result String Game result time_control String Time control move_number Integer Move number error_ply Integer Ply number side String White or Black opening_phase String Opening, Middlegame, Endgame error_type String Inaccuracy, Mistake, Blunder played_move String Move played best_move String Engine recommendation eval_before Float Evaluation before move eval_after Float Evaluation after move eval_change Float Evaluation swing clock_seconds Integer Remaining clock time fen_before String Position before move fen_after String Position after move 🧪 Methodology 1. Data Collection Games were collected from official broadcast PGN archives. 2. Error Extraction Custom CMEED extraction software identifies: Inaccuracies Mistakes Blunders from engine annotations embedded within broadcast PGNs. 3. Position Reconstruction Every game is replayed using python-chess to reconstruct: FEN before move FEN after move for each detected error. 4. Evaluation Tracking For every error record: eval_before eval_after eval_change are extracted and reconstructed from engine evaluations embedded in PGN annotations. 5. Metadata Enrichment Each record is enriched with: Tournament metadata Player metadata Rating information Opening information Clock information Game identifiers 🔁 Reproducibility CMEED was generated using the Chess Multiverse Error Extraction Pipeline. Core technologies: Python python-chess PGN parsing JSON serialization Pipeline: PGN Import ↓ Evaluation Parsing ↓ Error Detection ↓ FEN Reconstruction ↓ Metadata Enrichment ↓ JSON Export 💻 Example Usage Load Dataset import json import pandas as pd with open("data/cmeed_2026-05.json", "r", encoding="utf-8") as f: data = json.load(f) df = pd.DataFrame(data) print(df.head()) Player Error Analysis carlsen = df[df["player"] == "Carlsen, Magnus"] print(carlsen["error_type"].value_counts()) Largest Blunders blunders = df[df["error_type"] == "Blunder"] largest = blunders.sort_values( by="eval_change", ascending=False ) print(largest.head()) Opening Error Analysis opening_errors = ( df.groupby("opening") .size() .sort_values(ascending=False) ) print(opening_errors.head(20)) 🔬 Research Applications CMEED enables research in: Human Error Modeling Chess Performance Analytics Opening Risk Assessment Time Pressure Studies Decision-Making Research Elo-Based Error Prediction Endgame Error Analysis Tournament-Level Statistical Research Cognitive Science Sports Analytics Artificial Intelligence Machine Learning Reinforcement Learning Explainable AI Human-Computer Interaction Chess Education ⚠️ Limitations Coverage is limited to official broadcast events available through the source archive. Error detection depends on engine evaluations embedded within source PGNs. CMEED focuses on inaccuracies, mistakes, and blunders rather than every move played. Version 1.0 covers January 2026 through May 2026 only. Additional tournaments and historical years may be added in future releases. 🤝 Contributing Contributions are welcome. Please read: CONTRIBUTING.md CODE_OF_CONDUCT.md before submitting issues or pull requests. Potential areas include: Additional years Additional tournaments Validation tooling Data quality improvements Research notebooks Visualization tools Position classification systems 📝 Citation If you use CMEED in research, publications, software, educational projects, or derivative datasets, please cite the dataset. Varshney, Sparsh. (2026). Chess Multiverse Error & Evaluation Dataset (CMEED v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.20625716 DOI DOI: https://doi.org/10.5281/zenodo.20625716 👨‍🔬 Lead Researcher & Principal Developer Sparsh Varshney Founder, Chess Multiverse Research Interests: Chess Analytics Human Error Modeling Open Data Science Artificial Intelligence Medical Research Open Science Projects Chess Multiverse https://www.chessmultiverse.org Amidha Ayurveda https://www.amidhaayurveda.com Profiles GitHub: https://github.com/sciencewithsaucee-sudo ORCID: https://orcid.org/0009-0004-7835-0673 ⚖️ License This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). You are free to: Share Adapt Build upon the dataset for any purpose, including commercial use, provided appropriate attribution is given and derivative works are distributed under the same license.