A machine actionable OORF schema using LinkML
Important
Project Status & Disclaimer:
- Community Initiative (not endorsed by the OECD): This machine-actionable schema is not endorsed by the OECD. It is an effort initiated by the toxicology community within PARC (Partnership for the Assessment of Risks from Chemicals) to formalize and FAIRify the published OECD Omics Reporting Framework (OORF).
- Work in Progress: This repository is actively under development. See src/maoorf/schema/README.md for details on the modular architecture and how to define custom reporting modules.
- AI-Assisted Development: The initial translation of the OECD OORF Excel template and guidance document into modular LinkML was assisted by generative AI (Google Gemini / Antigravity) under human direction and is actively undergoing domain-expert review and curation.
https://eu-parc.github.io/maoorf
- docs/ - mkdocs-managed documentation
- elements/ - generated schema documentation
- examples/ - Examples of using the schema
- project/ - project files (these files are auto-generated, do not edit)
- src/ - source files
- maoorf
- schema/ -- LinkML schema (edit this)
- datamodel/ -- generated Python datamodel
- maoorf
- tests/ - Python tests
- data/ - Example data
There are several pre-defined command-recipes available.
They are written for the command runner just.
To list all pre-defined commands, run just or just --list.
This machine-actionable schema implements/derives:
OECD (2023), OECD Omics Reporting Framework (OORF): Guidance on reporting elements for the regulatory use of omics data from laboratory-based toxicology studies, OECD Series on Testing and Assessment, No. 390, OECD Publishing, Paris, https://doi.org/10.1787/6bb2e6ce-en.
To download the reporting template and guidance pdf to the inputs/ folder, run just fetch-oorf-specs.
This project uses the template linkml-project-copier.
The modular LinkML architecture, extraction of reporting elements, descriptions, and provider notes from the official OECD OORF (2023) Excel template and Guidance PDF, and the conversion of example datasets were developed with the assistance of generative AI (Google Gemini via Antigravity). All architectural decisions, modeling constraints, and validation workflows are directed and reviewed by the project maintainers, who take full responsibility for the curated specification.