David Amadi is a Data Documentalist with background in public health informatics and extensive expertise in research data management, metadata standards, and the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles. His work focuses on transforming complex research data into machine-readable, interoperable formats and developing metadata frameworks to optimize data sharing, accessibility, and reuse.
David has contributed to major international research initiatives, including the INDEPTH Network and currently the INSPIRE Network, where he supports the FAIR sharing of longitudinal population health data. He is highly skilled in creating Implementation Guides (IGs) for studies, managing standardized data catalog systems, and ensuring compliance with global metadata standards.
Beyond his documentation work, David applies his data science expertise to design and implement data pipelines, harmonize and standardize health data, and deploy the OMOP Common Data Model (CDM). Collaborating with engineers and data scientists, he develops Extract, Transform, and Load (ETL) processes aligned with international standards, supporting reliable and comprehensive data integration for global health research
Affiliations
Teaching
David is committed to capacity building and has conducted several training sessions and workshops, including:
- Training on the OHDSI (Observational Health Data Sciences and Informatics) framework, focusing on implementing the OMOP Common Data Model (CDM) and developing ETL pipelines.
- Building capacity for using metadata standards to implement FAIR principles, empowering researchers to create interoperable and reusable data systems.
- Leading hands-on sessions on metadata documentation, including the development of machine-readable metadata frameworks and standardized catalog system
Research
- Developing metadata-driven frameworks to support the implementation of FAIR data principles, ensuring research data is Findable, Accessible, Interoperable, and Reusable.
- Implementing the OMOP Common Data Model (CDM) to standardize and harmonize health data across diverse research platforms, enabling cross-study analyses and integration.
- Promoting the adoption of international standards in research data management to enhance data accessibility, usability, and interoperability across global health networks.
- Optimizing data integration in health research, particularly for longitudinal and multi-source datasets, to support comprehensive and scalable analyses.
Selected Publications
