The DataHarmonizer: a tool for faster data harmonization, validation, aggregation and analysis of pathogen genomics...

Area of Science:

• Genomic epidemiology
• Public health informatics
• Bioinformatics

Background:

• Pathogen genomics is vital for public health but requires contextual data (metadata) for interpretation.
• Inconsistent data capture and encoding across different databases hinder data integration and reuse.
• The COVID-19 pandemic highlighted the need for standardized data sharing in infectious disease surveillance.

Purpose of the Study:

• To introduce DataHarmonizer, a tool for harmonizing, validating, and transforming pathogen genomics contextual data.
• To address challenges in data interpretation, integration, and reusability for public health initiatives.
• To facilitate secure and efficient data sharing for national and international surveillance efforts.

Main Methods:

• Developed DataHarmonizer as a template-driven, web browser-based spreadsheet application.
• Utilized JavaScript for in-browser validation and enabled offline functionality for enhanced data security.
• Created templates for SARS-CoV-2 contextual data, aligning with international specifications.

Main Results:

• DataHarmonizer successfully harmonized SARS-CoV-2 contextual data for national surveillance in Canada.
• The tool enhances the effectiveness, fidelity, and usability of contextual data capture.
• Templates are being developed for broader applications, including One Health and foodborne pathogens.

Conclusions:

• DataHarmonizer improves the interoperability and reusability of pathogen genomics data globally.
• Standardized contextual data is crucial for effective public health responses to current and future emergencies.
• The tool's adaptability supports diverse data management needs beyond the initial COVID-19 focus.