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+# Reproducible research software development using Python
+
+
+## Big-picture goal
+
+This is a hands-on course on research software engineering.  Since most
+participants use Python in their work, we will use Python as the example
+language. We will work with an example project and go through all the steps of
+a typical software project.  Once we have seen the building blocks, we will try
+to apply them to own projects. Workshop participants will receive and also
+learn to give constructive code feedback.
+
+
+## Prerequisites
+
+:::{prereq} Preparation
+1. Get a **GitHub account** following [these instructions](https://coderefinery.github.io/installation/github/).
+1. You will need a **text editor**. If you don't have a favorite one, we recommend
+   [VS Code](https://coderefinery.github.io/installation/vscode/).
+1. **If you prefer to work in the terminal** and not in VS Code, set up these two (skip this if you use VS Code):
+   - [Git in the terminal](https://coderefinery.github.io/installation/git-in-terminal/)
+   - [SSH or HTTPS connection to GitHub from terminal](https://coderefinery.github.io/installation/ssh/)
+1. **One of these two software environments** (if you are not sure which one to
+   choose or have no preference, choose Conda):
+   - {ref}`conda`
+   - {ref}`venv` (Snakemake is not available in this environment)
+1. **Optional** and only on Linux: [Apptainer](https://apptainer.org/) following
+   [these instructions](https://apptainer.org/docs/admin/1.3/installation.html#install-from-pre-built-packages).
+:::
+
+
+## Schedule
+
+:::{note}
+The schedule will very soon contain links to lesson material and exercises.
+:::
+
+
+### Day 1 (Sep 16)
+
+- 13:00-13:30 (0.5h) - **Welcome and introduction**
+  - Motivation (reproducibility, robustness, distribution, improvement, trust, etc.)
+  - Practical information (tools, communication, breaks, etc.)
+  - What will learn and achieve from this course?
+  - Example Python project we will use and develop
+
+- 13:30-14:45 (1.25h) - **Introduction to version control with Git and GitHub (1/2)**
+  - Creating a repository and porting your project to Git and GitHub
+  - Basic commands
+
+- 15:00-16:30 (1.5h) - **Introduction to version control with Git and GitHub (2/2)**
+  - Branching and merging
+  - Recovering from typical mistakes
+
+- 16:45-18:00 (1.25h) - **Documentation**
+  - In-code documentation including docstrings
+  - Writing good README files
+  - Markdown
+  - Sphinx
+  - Building documentation with GitHub Actions
+  - Jupyter Notebooks
+
+
+### Day 2 (Sep 17)
+
+- 09:00-10:30 (1.5h) - **Collaborative version control and code review (1/2)**
+  - Practice code review using issues and pull requests
+  - Forking workflow
+  - Contributing changes to projects of others
+
+- 10:45-12:15 (1.5h) - **Collaborative version control and code review (2/2)**
+  - Organization strategies
+  - Merge vs. rebase
+  - Conflict resolution
+
+- 16:45-18:00 (1.25h) - **Debriefing and Q&A**
+  - Participants work on their projects
+  - Together we study actual codes that participants wrote or work on
+  - Constructively we discuss possible improvements
+  - Give individual feedback on code projects
+
+
+### Day 3 (Sep 18)
+
+- 09:00-10:30 (1.5h) - **Automated testing**
+  - Unit tests
+  - End-to-end tests
+  - pytest
+  - GitHub Actions
+
+- 10:45-12:15 (1.5h) - **How to make the project more reusable?**
+  - Tracking dependencies with requirements.txt and environment.yml
+  - Recording environments in containers
+
+- 13:00-14:45 (1.75h) - **Code quality and good practices**
+  - Naming (and other) conventions, project organization, modularity
+  - Refactoring (explained through examples)
+  - Design patterns: functional design vs. object-oriented design
+  - How to design your code before writing it
+  - Structuring larger software projects in a modular way
+  - Command-line interfaces
+  - Workflows with Snakemake
+
+- 15:00-16:30 (1.5h) - **How to release and publish your code**
+  - Packaging the code
+  - Licenses
+  - Sharing the code via PyPI
+  - Publishing the code via Zenodo
+
+- 16:45-18:00 (1.25h) - **Debriefing and Q&A**
+  - Participants work on their projects
+  - Together we study actual codes that participants wrote or work on
+  - Constructively we discuss possible improvements
+  - Give individual feedback on code projects
+
+
+### Extra material if we have time
+
+- Profiling memory and CPU usage
+- Strategies for parallelization
+
+
+```{toctree}
+:maxdepth: 1
+:caption: Software environment
+:hidden:
+
+installation/conda
+installation/virtual-environment
+```
+
+```{toctree}
+:maxdepth: 1
+:caption: Reference
+:hidden:
+
+All lessons <https://coderefinery.org/lessons/>
+CodeRefinery <https://coderefinery.org/>
+Reusing <https://coderefinery.org/lessons/reusing/>
+```
diff --git a/content/installation/conda.md b/content/installation/conda.md
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+(conda)=
+
+# Conda environment
+
+A Conda environment is an isolated software environment that is used to manage dependencies for a project
+and you decide where it is located.
+
+You will need a `environment.yml` file that documents the dependencies:
+```yaml
+name: coderefinery
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - python >= 3.10
+  - black
+  - click
+  - flit
+  - ipywidgets
+  - isort
+  - jupyterlab
+  - jupyterlab_code_formatter
+  - jupyterlab-git
+  - matplotlib
+  - myst-parser
+  - nbdime
+  - numpy
+  - pandas
+  - pytest
+  - pytest-cov
+  - scalene
+  - seaborn
+  - snakemake-minimal
+  - sphinx
+  - sphinx-autoapi
+  - sphinx-autobuild
+  - sphinx_rtd_theme >= 2.0
+  - vulture
+  - scikit-image
+```
+
+
+## Before you create a virtual environment
+
+1. Create a new directory for this course.
+1. In this directory, create an `environment.yml` file and copy-paste the dependencies above into it.
+
+
+## Choose the tool to manage the environment
+
+If you are already using one of these tools, please continue using the tool that you like and know.
+If you are new to this, **we recommend using Miniconda or Miniforge**.
+
+- [Anaconda](https://docs.anaconda.com/anaconda/install/)
+  - Advantages: easy to install, easy to use, good for beginners
+  - Disadvantages: large download, installs more than we will need, license restrictions
+- [Miniconda](https://docs.anaconda.com/miniconda/)
+  - Advantages: small size, installs only what you need
+  - Disadvantages: no graphical interface, license restrictions
+- [Miniforge](https://github.com/conda-forge/miniforge)
+  - Advantages: small size, no license restrictions
+  - Disadvantages: no graphical interface
+- [Micromamba](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html)
+  - Advantages: fast, small size
+  - Disadvantages: no graphical interface
+- [Pixi](https://pixi.sh/latest/)
+  - Advantages: fast and new
+  - Disadvantages: new and less tested and not documented here
+
+
+## Creating the virtual environment
+
+1. Open your terminal shell (e.g. Bash or Zsh).
+2. Activate `conda` using `conda activate` or `source ~/miniconda3/bin/activate`.
+3. Run the following command:
+   ```console
+   $ conda env create --file environment.yml
+   ```
+4. Make sure that you see "coderefinery" in the output when you ask for a list of all available environments:
+   ```console
+   $ conda env list
+   ```
+
+
+## How to verify that this worked
+
+(this will be added)
diff --git a/content/installation/virtual-environment.md b/content/installation/virtual-environment.md
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+(venv)=
+
+# Virtual environment
+
+A virtual environment is an isolated software environment that is used to manage dependencies for a project
+and you decide where it is located.
+
+You will need a `requirements.txt` file that documents the dependencies:
+```
+black
+click
+flit
+ipywidgets
+isort
+jupyterlab
+jupyterlab-code-formatter
+jupyterlab-git
+matplotlib
+myst-parser
+nbdime
+numpy
+pandas
+pytest
+pytest-cov
+scalene
+seaborn
+sphinx
+sphinx-autoapi
+sphinx-autobuild
+sphinx_rtd_theme >= 2.0
+vulture
+scikit-image
+```
+
+
+## Before you create a virtual environment
+
+1. Create a new directory for this course.
+1. In this directory, create a `requirements.txt` file and copy-paste the dependencies above into it.
+
+
+## Creating the virtual environment
+
+Now create a virtual environment in this directory either using [pip and
+venv](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/)
+(more traditional and safer) or using [uv](https://docs.astral.sh/uv/) (more modern but also less tested):
+
+:::::{tabs}
+
+::::{group-tab} pip and venv
+Create a new virtual environment and activate it:
+```console
+$ python3 -m venv coderefinery-environment
+$ source coderefinery-environment/bin/activate
+```
+
+Install the dependencies into the environment:
+```console
+$ python3 -m pip install -r requirements.txt
+```
+::::
+
+
+::::{group-tab} uv
+Create a new virtual environment and activate it:
+```console
+$ uv venv coderefinery-environment
+$ source coderefinery-environment/bin/activate
+```
+
+Install the dependencies into the environment:
+```console
+$ uv pip install -r requirements.txt
+```
+::::
+
+:::::
+
+
+## How to verify that this worked
+
+(this will be added)