Awesome Data Quality Resources, tools, papers, and projects for ensuring data reliability and effectiveness across traditional data, LLM pretraining/fine-tuning data, multimodal data, and more. Contents Introduction Traditional Data Papers Tools & Projects Data Readiness Assessment Large Language Model Data Pretraining Data Fine-tuning Data LLM Data Management Cognition Engineering & Test-Time Scaling Multimodal Data Papers Tools & Projects Tabular Data Papers Tools & Projects Time Series Data Papers Tools & Projects Graph Data Papers Tools & Projects Data-Centric AI Surveys Data Valuation...
| Stars | 28 |
| Forks | 7 |
| Category | AI Tool |
| License | CC0-1.0 |
| Quality Score | 54.8277508165586/100 |
| Last Updated | 2026-06-17 |
| Created | 2025-03-03 |
| Est. Tokens | ~13k |
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awesome-data-quality is A comprehensive collection of data quality resources, tools, papers, and projects across various data types including traditional data, LLM pretraining/fine-tuning data, multimodal data, and more. Ess. It is categorized as a AI Tool with 28 GitHub stars.
You can find installation instructions and usage details in the awesome-data-quality GitHub repository at github.com/MigoXLab/awesome-data-quality. The project has 28 stars and 7 forks, indicating an active community.
awesome-data-quality is released under the CC0-1.0 license, making it free to use and modify according to the license terms.