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A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

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Enhancing data interoperability in human biomonitoring studies: the HBM data toolkit.

Ruben Peeters¹, Laura Rodriguez Martin², Fen Zhang²

¹VITO Health, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400, Mol, Belgium; KU Leuven, Leuven, Belgium; Leuven.AI, Leuven, Belgium; Flanders Make@KULeuven, Leuven, Belgium.

International Journal of Hygiene and Environmental Health

|September 12, 2025

Summary

This summary is machine-generated.

A new Human Biomonitoring Data Toolkit enhances data integrity and interoperability for FAIR data principles. This secure, browser-based tool supports harmonized analysis across diverse chemical risk assessment studies.

Keywords:

Data harmonization Data quality FAIR data GDPR Human biomonitoring Schema-based tool Validation Web interface

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Area of Science:

Environmental Health Sciences
Computational Biology
Data Science

Background:

Harmonizing heterogeneous Human Biomonitoring (HBM) data is crucial for reliable conclusions and FAIR data principles.
Existing HBM data aggregation methods require optimization for data integrity and interoperability.
The Partnership for the Assessment of Risks from Chemicals (PARC) initiative highlights the need for standardized HBM data handling.

Purpose of the Study:

To introduce the HBM Data Toolkit, designed to optimize data integrity, interoperability, and confidentiality in HBM studies.
To provide a flexible, web-based solution for harmonizing, validating, and analyzing HBM data.
To facilitate the aggregation of HBM data for enhanced chemical risk assessment.

Main Methods:

Development of a 4-module toolkit (harmonization, validation, derived variables, summary statistics) using Python.
Implementation of Pyodide and WebAssembly for secure, local execution within the web browser, eliminating data upload.
Utilization of flexible templates and standardized schemas aligned with HBM4EU and PARC project specifications for data validation and transformation.

Main Results:

The HBM Data Toolkit enables harmonized data storage in the Personal Exposure and Health (PEH) platform.
Validated HBM data are compatible with the Monte Carlo Risk Assessment (MCRA) platform.
The toolkit supports calculation of imputed censored data, standardization/normalization of biomarker data, and generation of summary statistics for visualization and integration into platforms like IPCHEM.

Conclusions:

The HBM Data Toolkit effectively advances data quality, harmonization, and aggregation in Human Biomonitoring studies.
Local execution, user-friendly codebooks, and standardized schemas provide a unified framework for consistent HBM data analysis.
The toolkit supports robust interpretation of diverse HBM studies, contributing to improved chemical risk assessment.