This course introduces health researchers and data professionals to the critical role of metadata in transforming raw data into meaningful, reusable scientific resources. Through a practical microlearning approach, learners will explore how metadata provides the essential context that makes datasets understandable, discoverable, and valuable beyond their original purpose.
The course explains how properly documented data can move from being isolated and underutilized to becoming a powerful asset for global collaboration. Learners will gain a clear understanding of how metadata supports the FAIR principles - making data Findable, Accessible, Interoperable, and Reusable - and why this is essential for modern Open Science practices.
The course also introduces the DDI Codebook Standard, a structured metadata framework widely used to describe survey and health data. Participants will learn the difference between basic documentation and machine-readable metadata, and how structured metadata enables search engines, repositories, and other researchers to efficiently discover and reuse datasets.
By the end of this course, learners will be equipped with foundational and practical DDI Codebook knowledge to improve the visibility, usability, and long-term impact of their health research data.
- Teacher: DDI TWG, MEIRU