Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable...

Area of Science:

• Public and Global Health
• Data Science
• Information Science

Background:

• Metadata are crucial for enabling Findability, Accessibility, Interoperability, and Reusability (FAIR) data principles.
• Machine-readable metadata descriptions empower users and machines to discover, access, integrate, and reuse digital resources.
• Current population health data metadata lack accessibility and machine-interpretability, hindering data discovery and reuse.

Purpose of the Study:

• To propose a comprehensive framework for rendering population health data machine-readable.
• To enhance the FAIRness of population health data through standardized formats, vocabularies, and protocols.
• To enable seamless data discovery, access, and integration across diverse platforms and research applications.

Main Methods:

• Implemented a 3-stage approach: Data Documentation Initiative (DDI) integration, Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization, and Schema.org/JSON-LD integration.
• DDI Codebook was leveraged for detailed data documentation, ensuring transparency.
• Data were harmonized into OMOP CDM for unified analysis, and Schema.org entities with JSON-LD were used for machine-readable metadata generation.

Main Results:

• The framework significantly enhanced the FAIRness of population health data, improving discoverability via platforms like Google Dataset Search.
• Standardized formats and protocols streamlined data accessibility and integration, fostering collaboration.
• Machine-interpretable metadata enabled efficient data reuse for targeted analyses, maximizing resource value.

Conclusions:

• Adopting machine-readable metadata standards is essential for ensuring the FAIRness of population health data.
• These standards enhance resource visibility, accessibility, and utility, with broad impact, especially in low- and middle-income countries.
• Machine-readable metadata can accelerate research, improve healthcare decision-making, and promote better global health outcomes.