Nearly 80 percent of research data currently collected is deemed not reusable, since datasets might be unstructured and do not contain rich metadata. Standardizing research data at the source with ontologies and domain-specific semantic data models allows one to make their collected data FAIR (Findable, Accessible, Interoperable, and Reusable) and machine-readable at the time of collection.
In this presentation, you will:
- Receive an introduction into the FAIR Data Principles
- Learn the benefits of machine-readable data
- Understand how data gathered on electronic Case Report Forms may be made FAIR and machine-readable upon collection