> For the complete documentation index, see [llms.txt](https://openchemfacts.gitbook.io/openchemfacts-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://openchemfacts.gitbook.io/openchemfacts-docs/method.md).

# Method

## Key principles 

OpenChemFacts aims to make scientifically robust methods for chemical environmental assessment more accessible and reproducible.

The platform relies on internationally recognized models and consensus approaches whenever possible. Raw data from multiple sources are harmonized and transformed into higher-level indicators through transparent and documented workflows.

The methodology is guided by four key principles:

* **Scientific robustness** – preference is given to established methods and consensus models developed by regulatory and scientific communities.
* **Transparency** – assumptions, transformations, and calculation procedures are documented whenever possible.
* **Reproducibility** – data processing workflows are implemented programmatically to ensure consistent and repeatable results.
* **Openness** – methodologies and software components developed within OpenChemFacts are maintained as open-source projects whenever possible.

## Implementation

OpenChemFacts workflows are primarily developed in Python and implemented through reproducible data pipelines.

Software components and methodologies developed by the project are maintained within the OpenChemFacts GitHub organization : <https://github.com/OpenChemFacts>

Depending on the specific workflow, computations may rely on Jupyter notebooks, Python packages, or dedicated processing pipelines.