Documenting R&D Progress using jupyter-book - and feel safe for the next performance audit

Jupyter Book combines daily work environment, documentation, and R&D progress tracking in one solution, making it ideal for performance audits

Key components include:

• Well-organized file system structure
• Automated workflow for documentation
• Integration with Python ecosystem
• Support for Markdown and Jupyter notebooks
• Non-destructive processing workflow

Documentation structure follows:

• Root directory for each component
• Separate sections for simulations, tests, and studies
• Changelog and README files
• Directory structure for photos, videos, and supplementary materials

Evaluation process:

• Reference notebooks that can be reused
• Standardized data processing workflows
• Automated rebuilding of documentation when new tests/studies are added
• Support for interactive data visualization (bokeh)

Technical implementation details:

• Uses Sphinx under the hood
• Simple pip installation process
• Config.yaml file defines book structure
• Table of contents auto-generated from directory structure
• Extension support for additional features (videos, custom formatting)

Best practices:

• Use isolated Python environments
• Keep raw data separate from processing
• Implement version control compatible workflow
• Maintain clear changelog documentation
• Automate documentation rebuilding process

Raw data management:

• JSON-encoded pandas DataFrames
• Separate storage for measurement data
• Reproducible processing pipelines
• Support for various data formats and visualizations