This is the CERN innovation test page and here the instructions to get the best repository ever!

• CERN Science for Open Data (CS4OD): webpage and repository

• Check if there already exists a respository associated to your project

• click on new
• add Repository name
• add Description. Description must be the project's full name
• be sure the repository is set to Private
• flag Add a README.md file,
• flag Add .gitignore and choose the desired programming language
• flag Choose a license and choose the desired license (choose MIT License if unknown)
• Create repository
• add repository topic: procedure. Choose between:
  • ML (classical machine learning)
  • QML (quantum machine learning)
  • QTI (quantum technology initiative)
  • ST (classical statistics)
• use main branch for production-ready state only
• create develop branch for the latest delivered development changes for the next release
• create your development branch where each contributor works on a daily basis Use correct branching methods for a clean GitHub tree (docs and example)

Full description of the project

• Description of the project
• How to install
  • definition of virtual environment (anaconda/venv) used
  • instruction to install the package (requirements.txt or setup.cfg etc)
  • instruction how to run the code
• Quick start: minimal working example / tutorials / demos

• requirements.txt or environment.yaml(for conda) or setup.cfg + pyproject.toml or setup.py(setuptools)
• src/packagename folder with source files
• formatting: production code must be formatted with Black
• function annotations: augment all functions and modules with dosctrings

• bibliography.md: Zenodo link to external papers and datasets used
• Semantic versioning: comply with semver.org and apache.org
• documentation: using readthedocs and simple formatting rules. Please, use one of the following two standards: Google's docstring or Numpy's docstring.

• citation policy: how to use and cite the code (e.g. BibTex reference)