Disclaimer: This repository is in an early draft stage and is actively being developed. Expect frequent changes, updates, and potentially significant revisions as the project progresses. Feedback and contributions are welcome, but please be aware of the evolving nature of the specification at this stage.
- About
- What are ontologies and how do they improve data quality?
- The HPAI Contextual Data Specification Package
- Contacts
- License
- Acknowledgements
This draft data specification harmonizes contextual data to support the monitoring of Highly Pathogenic Avian Influenza virus. Developed in collaboration with Public Health Alliance for Genomic Epidemiology (PHA4GE), it provides standardized, ontology-based fields and terms aimed at facilitating comprehensive, accurate, and consistent data collection. Currently implemented through the DataHarmonizer tool, the specification is designed to be adaptable for use in other tools. Supporting resources include detailed field and reference guides, as well as SOPs for data curation and new term requests.
We encourage feedback and contributions to improve the specification. However, please note that this is a work in progress, and the structure and content are likely to change as the project evolves. If you would like to contribute or propose changes, please open an issue or submit a pull request, or alternatively contact Emma Griffiths at [email protected]
Labs collect, encode and store information in different ways. They use different fields, terms and formats, they categorize variables in different ways, and the meanings of words change depending on the focus of the organization (think of the word “plant”. To someone in agriculture, “plant” could mean an organism that carries out photosynthesis, while a food regulator might understand the word “plant” to mean a factory where food products are made). This variability makes comparing, integrating and analyzing data generated by different organizations like trying to compare apples, oranges and bananas, which is difficult to do.
Ontologies are collections of controlled vocabulary that are arranged in a hierarchy, where all the terms are linked using logical relationships. Ontologies are open source and meant to represent “universal truth” as much as possible (so not tied to one organization’s vocabulary of use case). Ontologies encode synonyms, which enables mapping between the specific languages used by different organizations, and every term in the ontology is assigned a globally unique and persistent identifier. Using ontology terms to standardize HPAI contextual data not only helps make data more interoperable by using a common language, it also helps to make contextual data FAIR (Findable, Accessible, Interoperable, Reusable).
This specification is currently implemented via a DataHarmonizer validation template, with accompanying Field and Term reference guides (which provide definitions and additional specific guidance) and a curation Standard Operating Procedure (SOP). Please note, this specification is not only available in the DataHarmonizer and can be implemented in any data capture tool, please refer to the field and term reference guides for the data types and picklists.
New terms and/or term changes can be requested through GitHub using the issue request forms, with additional guidance on how to do so outline in the New Term Request (NTR) SOP. This resources are available in the files of this repository and listed below under Package Contents.
Please note that development of the specification is dynamic and it will be updated periodically to address user needs. Versioning is done in the format of x.y.z.
x = Field level changes
y = Term value / ID level changes
z = Definition, guidance, example, formatting, or other uncategorized changes
Descriptions of changes are provided in release notes for every new version.
- Pathogen Genomics Package (HPAI)
- Template schema files can be found as
.yaml/.json/.tsvunder pathogen-genomics-package/templates/HPAI
- Template schema files can be found as
- DataHarmonizer App
- The DataHarmonizer is a standardized browser-based spreadsheet editor and validator.
- Instructions on "Getting Started" downloading and using the application can be found under DataHarmonizer Instructions and SOP below.
- Further information about application functionality can be found on the DataHarmonizer Wiki.
The HPAI contextual data specification has been subset into four different use case templates. These are for environmental samples, food samples, wastewater samples and host specific samples.
XLSX version
Master Field and Term Reference Guide
PDF version
- PDF version to be added
- Online version to be added
For more information and/or assistance, contact Emma Griffiths at [email protected] or submit a repository issue request.
Pending / To Be Determined
Brought to you by The Centre for Infectious disease Genomics and One Health and Public Health Alliance for Genomic Epidemiology(PHA4GE)