Research Data Governance and Quality Management

In modern research, the integrity of your findings depends entirely on the quality of your underlying data. Establishing clear governance and evaluation standards is essential to prevent errors, ensure reproducibility, and meet institutional requirements. This text-based course guides you through the foundational concepts of research data management, helping you design and implement robust governance frameworks. You will transition from unstructured data practices to a structured system that guarantees high-quality, reliable research outputs. What you'll learn: - Understand the core principles of research data governance and lifecycle management - Evaluate data quality using standardized metrics and validation techniques - Implement modern FAIR (Findable, Accessible, Interoperable, Reusable) data principles - Create comprehensive Data Management Plans (DMPs) that align with institutional standards - Manage metadata and documentation to ensure long-term usability and reproducibility - Identify and mitigate ethical, security, and privacy risks in research datasets Starting with foundational definitions and key terminology, this course guides you step-by-step through designing governance frameworks, assessing data quality, and preparing your research data for long-term archiving and sharing. This course is designed for researchers, academic professionals, and data coordinators who are new to formal data governance, with no prior experience required. Enhance the credibility and impact of your research by mastering data quality governance today.