init commit

.github/workflows/documentation.yml

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+name: Deploy Documentation
+
+on:
+  push:
+    branches:
+      - master
+      - main
+
+permissions:
+  contents: write
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
+        with:
+          python-version: 3.x
+      - uses: actions/cache@v2
+        with:
+          path: .cache
+          key: ${{ github.ref }}
+      - run: pip install mkdocs-material
+      - run: mkdocs gh-deploy --force

docs/index.md

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+# EnzymeML Training Course
+
+## ℹ️ About
+The EnzymeML Training Course is a collaborative project between multiple biocatalysis groups. The aim of this project is to jointly develop computational tools, data standards, and best practices for the analysis of enzyme-catalyzed reactions. Furthermore, the project provides training for Python-based data analysis of experimental data.
+
+## 🚀 Motivation
+Current state of data analysis and implications on reproducibility and scalability...
+
+## 📚 Stages
+The project is structured into three stages, dividing the task of implementing generic analysis workflows for biocatalytic data:  
+
+### 1. 🛠️ Making experimental data accessible
+Development of generic Python tools, allowing reading in unprocessed data from various analytical sources such as spectrophotomety, chromatograph, or NMR. The tools facilitate the preprocessing of the data, concentration calculation, and the generation of EnzymeML files.
+
+### 2. 📈 Data Analysis
+Depending on the needs of the project members, existing Python tools will be used or extended to analyze the data. Experimental questions 
+Development of Python-based tools for the analysis of enzyme-catalyzed reactions.  
+
+### 3. 🤝 In-person Training Course
+Development of training materials for the use of the data standard and the Python-based tools.

docs/installation.md

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+# Installation of tools
+
+For the installation of the tools, we recommend using the following Docker image, which contains all the necessary tools and dependencies. The Docker image is based on the official Python image and comes with installs JupyterLab locally in your machine:

docs/status.md

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+# Project Status
+
+## Implemented Spectrophotometers
+- [x] SpectraMax M2
+- [x] Magellan
+- [ ] Infinite M200
+
+## Implemented Chromatography Systems
+- [ ] Agilent 1100

mkdocs.yml

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+site_name: EnzymeML Training Course
+
+nav:
+  - Home: index.md
+  - Project Status: status.md
+  - Installation: installation.md
+
+theme:
+  name: material
+  features:
+    - navigation.tabs
+    - navigation.sections
+    - toc.integrate
+    - navigation.top
+    - search.suggest
+    - search.highlight
+    - content.tabs.links
+    - content.code.select
+    - content.code.copy
+    - content.code.annotation
+  palette: 
+    # Palette toggle for light mode
+    - scheme: default
+      toggle:
+        icon: material/brightness-7 
+        name: Switch to dark mode
+
+    # Palette toggle for dark mode
+    - scheme: slate
+      toggle:
+        icon: material/brightness-4
+        name: Switch to light mode
+
+
+markdown_extensions:
+  - pymdownx.highlight:
+      anchor_linenums: true
+      line_spans: __span
+      pygments_lang_class: true
+  - pymdownx.inlinehilite
+  - pymdownx.snippets
+  - pymdownx.superfences
+  - tables
+  - attr_list
+  - pymdownx.emoji:
+      emoji_index: !!python/name:material.extensions.emoji.twemoji
+      emoji_generator: !!python/name:material.extensions.emoji.to_svg
+  - def_list
+  - pymdownx.tasklist:
+      custom_checkbox: true

requirements.txt

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+mkdocs==1.5.3
+mkdocs-material==9.5.9
+mkdocs-material-extensions==1.3.1