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

David Amadi1, Sylvia Kiwuwa-Muyingo2, Tathagata Bhattacharjee1

  • 1Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

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Summary
This summary is machine-generated.

This study introduces a framework to make population health data machine-readable, enhancing its findability, accessibility, interoperability, and reusability (FAIR). This improves data discovery and integration for better global health outcomes.

Keywords:
DDIData Documentation InitiativeFAIR data principlesJSON-LDJavaScript Object Notation for Linked DataOMOP CDMObservational Medical Outcomes Partnership Common Data Modeldata modelsdata sciencemachine-readable metadatametadatastandardization

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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.