# Semantics Standards and Tools for Spatial and Contextual Exposome Data

> **NIH NIH R24** · UNIVERSITY OF FLORIDA · 2024 · $652,915

## Abstract

ABSTRACT
An individual’s phenotypes related to their health conditions are associated with the complex interplay between
the individual’s genetics and their exposures to both internal and external environments. Nevertheless, genetics
only account for ~10% of an individual’s health, while the remaining appears to be determined by environmental
factors and gene-environment interactions. To comprehensively understand the causes of diseases and prevent
them, environmental exposures, especially the spatial and contextual exposome—social and ecological
contexts in which the person lives their life (e.g., social capital, climate) or external agents to which the
one is exposed (e.g., environmental pollutants), need to be systematically explored. Nevertheless, the
heterogeneous definitions of the spatial and contextual exposome and the heterogeneity of their data
sources require us to adopt semantic standards using an ontology-driven approach to (1) provide an
unambiguous and consistent understanding of the variables in heterogeneous data sources, and (2) explicitly
express and model the context of the variables and relationships between them.
 On the other hand, the rapid adoption of electronic health record (EHR) systems has made large collections
of real-world data (RWD) that reflect the characteristics and outcomes of the patients being treated in real-world
settings, available for research. The increasing availability of RWD combined with the advancements in
analytical methods, especially artificial intelligence (AI) and machine learning (ML) offer unique opportunities
to generate real-world evidence (RWE). There is also an increasing interest in spatiotemporally linking spatial
and contextual exposome data, especially contextual social determinants of health, to real-world observational
data including RWD to answer various questions on how exposures to environmental factors affect health status,
disease development and outcomes, and health disparities and equity. However, there are key gaps in the
research infrastructure to support these studies.
 Our long-term goal is to develop and disseminate methods and tools to advance spatial and contextual
exposome research with RWD. Responding to RFA-ES-23-002, the objective of this proposal is to develop an
innovative SPATIAL AND CONTEXTUAL EXPOSOME SEMANTIC DATA INTEGRATION SYSTEM (SPACESCANS) with (1)
an ontology-annotated knowledge graph of existing publicly available high-quality spatial and contextual
exposome data from heterogeneous sources, along with (2) a user-friendly data integration tool that can guide
the users to choose (i) the appropriate spatial and contextual exposome variables, and (ii) appropriate
spatiotemporal linkage methods to link exposome data with their RWD, based on their study needs; and (3)
generate analysis-ready (and AI/ML-ready) RWD-exposome linked datasets for downstream analyses.

## Key facts

- **NIH application ID:** 10840037
- **Project number:** 1R24ES036131-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jiang Bian
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $652,915
- **Award type:** 1
- **Project period:** 2024-06-25 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10840037

## Citation

> US National Institutes of Health, RePORTER application 10840037, Semantics Standards and Tools for Spatial and Contextual Exposome Data (1R24ES036131-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10840037. Licensed CC0.

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