The Minimal Pathogen Agnostic Contextual Data Specification defines an international, ontology-based minimal metadata standard for public health and One Health genomic surveillance. Designed for use across pathogens, sequencing types, and global initiatives, the framework supports interoperable, timely, and privacy-conscious data sharing for both single isolate and metagenomic sequencing. By standardizing essential fields such as sample identifiers, geographic location, collection date, organism, and sequencing purpose, this specification enhances traceability, comparability, and decision-making in public health emergencies while promoting FAIR data principles.

The PHA4GE QC Contextual Data Tags Specification provides standardized, ontology-based annotations for labeling public health pathogen sequence datasets with known quality control (QC) issues. Designed to improve transparency, discoverability, and responsible reuse of lower-quality genomic data, the specification defines five structured QC fields—including controlled vocabulary for quality determinations and issues—that can be included in public repository submissions such as NCBI SRA. Organism-agnostic and sequencing technique-agnostic, these FAIR-aligned tags support training, validation, and optimization of public health genomics workflows while enhancing communication between data submitters and users.

The PHA4GE SARS-CoV-2 Contextual Data Specification provides a standardized, open-source framework for collecting, structuring, and sharing high-quality metadata to support COVID-19 genomic surveillance. Developed by the Public Health Alliance for Genomic Epidemiology (PHA4GE), the package includes a color-coded Excel collection template, ontology-mapped controlled vocabularies, JSON schema, reference guides, and detailed submission protocols for GISAID, NCBI, and ENA. Designed to promote FAIR data principles, interoperability, and global data sharing, this specification enhances the consistency, usability, and public health impact of SARS-CoV-2 sequence metadata.

The hAMRonization Workflow is a proof-of-concept pipeline designed to harmonize antimicrobial resistance (AMR) detection outputs from multiple bioinformatics tools into a single, standardized report. By running leading AMR gene detection tools—including AMRFinderPlus, RGI, ResFinder, SRST2, DeepARG, and more—against genomic assemblies or sequencing reads, the workflow uses hAMRonization parsers to collate results into a unified, interoperable format. Built with Snakemake and available via Conda or containerized environments (Docker/Podman), this workflow streamlines comparative AMR analysis and supports reproducible, FAIR-aligned genomic surveillance.

The PHA4GE Genotyping Contextual Data Specification is a draft, ontology-based framework designed to standardize how microbial genotyping methods and results are reported and shared. Developed by the PHA4GE Data Structures Working Group, it provides machine-readable, FAIR-aligned attributes that improve the consistency, comparability, and interoperability of genotype data across laboratories, public repositories, and public health platforms. By harmonizing genotyping metadata—including methods, databases, software, and confidence values—the specification reduces duplication of effort and enhances searchable, reusable genomic surveillance data.

The Primer Schemes repository is a versioned, community-driven resource for standardized tiled amplicon primer scheme definitions used in pathogen sequencing. Designed to eliminate ambiguity in naming and versioning, it promotes FAIR data principles by improving the findability, accessibility, interoperability, and reusability of primer schemes and associated sequencing data. With machine-readable indexing, structured scheme specifications, and validation via the Primaschema tool, this repository supports consistent, transparent, and reproducible genomic surveillance workflows across pathogens including SARS-CoV-2, MPXV, and Nipah virus.

The HPAI Contextual Data Specification is a draft, ontology-based framework designed to standardize and harmonize contextual data for Highly Pathogenic Avian Influenza (HPAI) virus surveillance. Developed in collaboration with PHA4GE, it supports consistent, interoperable, and FAIR data collection through DataHarmonizer templates, field and term reference guides, and curation SOPs. This evolving specification enables improved data quality, integration, and global collaboration across public health, food, environmental, and host-specific surveillance efforts.

During the 2022 and 2024 global Mpox outbreaks, a standardized contextual data specification was developed to support public health genomic surveillance of MPXV. The specification defines ontology-based fields and controlled vocabularies for harmonized capture of sample metadata, epidemiological, clinical, laboratory, and methodological information, with emphasis on geo-temporal context, data provenance, and sampling strategy. Implemented within the open-source DataHarmonizer platform, the MPXV specification enables structured curation, validation, and transformation of surveillance data and is currently in use in Canada, with international applicability and extensibility to other pathogens.

This publication presents the PHA4GE wastewater contextual data specification, an ISO-compatible, ontology-based standard developed with global partners to support interoperable wastewater genomic surveillance. Implemented through open-source tools and shared frameworks, the specification enables harmonised data integration and serves as a model for broader environmental and metagenomic surveillance standards.

This publication describes the development of standardized contextual data quality-control (QC) tags by PHA4GE to support the responsible sharing and reuse of lower-quality or purpose-specific genomic datasets. Implemented using ontologies and adopted by public health networks such as FDA’s GenomeTrakr, the tags improve dataset discoverability, interpretation, and transparency across public repositories.

This publication describes the development of a standardized output specification and the hAMRonization tool to harmonise antimicrobial resistance (AMR) detection results across diverse bioinformatic tools. Developed with international public health laboratories, hAMRonization enables interoperable, unified AMR reporting and supports scalable integration into genomic surveillance workflows.

PHA4GE has developed an AMR gene detection output standard to address inconsistencies across existing tools and reference databases. Supporting parsers and automated pipelines enable harmonised outputs, benchmarking, and improved reuse of AMR surveillance data.

PHA4GE developed a SARS-CoV-2 contextual data specification package, implemented through a structured collection template and supporting protocols, to harmonise and support submission of genomic and contextual data to public repositories. Adoption of the standard improves data interoperability, reuse, and integration, and is supported by NCBI’s BioSample database.

This publication describes the development of an open, harmonised SARS-CoV-2 contextual data specification extending the INSDC pathogen package. The specification supports interoperable metadata collection, data submission to public repositories, and improved reuse and integration of genomic data for COVID-19 surveillance and research.

This publication describes the development of an open, harmonised SARS-CoV-2 contextual data specification created by PHA4GE to support interoperable metadata collection and submission to public repositories. Implemented through standardised templates, protocols, and tools, the specification improves data consistency, reuse, and integration, and is now supported by NCBI’s BioSample database to enhance global COVID-19 genomic surveillance.