Principles and practices of data quality, with a focus on how to assess, monitor, and improve the quality of research data. Understanding and maintaining high data quality is essential for reproducibility, compliance, interoperability and data reuse.
Key Topics
- Dimensions of data quality (accuracy, completeness, consistency, timeliness, etc.)
- Techniques for assessing and improving data quality
- Tools and methods for data validation, cleaning, and error correction
- The impact of poor data quality on research outcomes
Learning Outcomes
- Define the key dimensions of data quality and understand their importance in research
- Apply techniques for data quality assessment, including validation and cleaning methods
- Implement best practices to maintain data quality throughout the data lifecycle
- Identify common data quality issues and implement strategies for their resolution
- Evaluate the impact of poor data quality on research integrity, reproducibility, and publication
