Data Quality

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