Design and Implementation of a Knowledge Graph for Integrating Global Health Data Using Neo4j ↗
Shows how Nouna population, weather, wearable and indoor climate datasets can be linked in a knowledge graph.
Methods, findings & limitations ↗Data & analysis
Represent people, households, observations and environmental events together with their relationships.
Published data-integration methods and continuing research
Measurements & outputs
Research workflow
Map source schemas and relationships while retaining time and provenance.
Use governed identifiers and access controls, then evaluate whether linked queries answer the scientific question.
Interpretation & current limits
Documented applications & research context
Links identify relevant studies and settings. Configuration and implementation stage differ between projects.
Publications & evidence
Read the study-specific methods, results and limitations. The framework paper describes the wider approach.
Shows how Nouna population, weather, wearable and indoor climate datasets can be linked in a knowledge graph.
Methods, findings & limitations ↗Introduces the modular CHEERS framework linking population surveillance, environmental measurements and individual health data.
Methods, findings & limitations ↗Methods in exchange
Source & scope
Content and images draw on Sandra Barteit’s Freiburg lecture, The Impact of Climate Change on Human Health: Lessons from Low- and Middle-Income Countries, slides 17, 60, 61, 62, 63, 64. Publication pages provide the corresponding research evidence. Reviewed 12 November 2026.
Images extracted from the lecture retain the original device or research illustration. Product images identify examples used in the source material.