NF4Science course template

- Stub including index, orientation and method overview (part 1)

Author: vdauwera

docs/nf4_science/course-template/00_orientation.md

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+# Orientation
+
+The training environment contains all the software, code and data necessary to work through this training course, so you don't need to install anything yourself.
+However, you do need a (free) account to log in, and you should take a few minutes to familiarize yourself with the interface.
+
+If you have not yet done so, please the [Environment Setup](../../envsetup/) mini-course before going any further.
+
+## Materials provided
+
+Throughout this training course, we'll be working in the `nf4-science/[course-name]/` directory, which you need to move into when you open the training workspace.
+This directory contains all the code files, test data and accessory files you will need.
+
+Feel free to explore the contents of this directory; the easiest way to do so is to use the file explorer on the left-hand side of the training workspace in the VSCode interface.
+Alternatively, you can use the `tree` command.
+Throughout the course, we use the output of `tree` to represent directory structure and contents in a readable form, sometimes with minor modifications for clarity.
+
+Here we generate a table of contents to the second level down:
+
+```bash
+tree . -L 3
+```
+
+If you run this inside `nf4-science/[course-name]`, you should see the following output:
+
+```console title="Directory contents"
+.
+├── data
+│   ├── ...
+│   └── ...
+├── nextflow.config
+├── [course-name].nf
+└── solutions
+    ├── modules
+    │   ├── ....nf
+    │   └── ....nf
+    ├── [course-name]-x.y.nf
+    └── [course-name]-x.y.nf
+
+```
+
+!!!note
+
+    Don't worry if this seems like a lot; we'll go through the relevant pieces at each step of the course.
+    This is just meant to give you an overview.
+
+**Here's a summary of what you should know to get started:**
+
+- **The `[course-name].nf` file** is the outline if the workflow script we will work to develop.
+
+- **The file `nextflow.config`** is a configuration file that sets minimal environment properties. You can ignore it for now.
+
+- **The `data` directory** contains input data and related resources:
+
+  - _thing_ called `filename` [details].
+
+- **The `solutions` directory** contains the completed workflow scripts and modules that result from each step of the course.
+  They are intended to be used as a reference to check your work and troubleshoot any issues.
+  The number in the filename corresponds to the step of the relevant part of the course.
+
+!!!tip
+
+    If for whatever reason you move out of this directory, you can always run this command to return to it:
+
+    ```bash
+    cd /workspaces/training/nf4-science/[course-name]
+    ```
+
+Now, to begin the course, click on the arrow in the bottom right corner of this page.

docs/nf4_science/course-template/01_method.md

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+# Part 1: Method overview and manual testing
+
+[High level summary of what the analysis aims to achieve.]
+
+There are multiple valid methods for performing this type of analysis.
+For this course, we are following the method described [here](url) by [authors] at [affiliation](url).
+
+Our goal is therefore to develop a workflow that implements the following processing steps: [brief enumeration].
+
+<figure class="excalidraw">
+--8<-- "docs/nf4_science/[course-name]/img/[filename]"
+</figure>
+
+- **[TOOL]:** [Summary of analysis step]
+
+However, before we dive into writing any workflow code, we are going to try out the commands manually on some test data.
+The tools we need are not installed in the GitHub Codespaces environment, so we'll use them via containers.
+
+For more detailed explanations about where to find containers and how they work, see the [Hello Containers](../../hello_nextflow/05_hello_containers.md) chapter in the beginners' course.
+
+[TODO: consider making a note about Seqera containers]
+
+!!! note
+
+    Make sure you're in the `nf4-science/[course-name]` directory.
+    The last part of the path shown when you type `pwd` should be `[course-name]`.
+
+---
+
+## 1. [Name of analysis step]
+
+We're going to pull a container image that has [relevant tools] installed, spin it up interactively and run the [analysis] commands on one of the example data files.
+
+### 1.1. Pull the `[tool]` container
+
+First, we use `docker pull` to download the container image.
+
+```bash
+docker pull community.wave.seqera.io/library/[container]
+```
+
+This gives you the following console output as the system downloads the image:
+
+```console title="Output"
+[console output]
+```
+
+The image has been downloaded to your computer, but it's not active yet.
+We need to actually spin it up (=make it run interactively).
+
+### 1.2. Spin up the container
+
+Let's spin up the container using `docker run -it` to run it interactively.
+
+```bash
+docker run -it -v ./data:/data community.wave.seqera.io/library/[container]
+```
+
+Your prompt will change to something like `(base) root@b645838b3314:/tmp#`, which indicates that you are now _inside_ the container.
+
+The `-v ./data:/data` part of the command will enable us to access the contents of the `data/` directory from inside the container.
+
+```bash
+ls /data/[thing]
+```
+
+```console title="Output"
+[console output]
+```
+
+You are now ready to try running [analysis or tool].
+
+### 1.3. Run the [analysis] command
+
+Now, we can run `[tool]` to [do analysis].
+
+[optional explanations about command options]
+
+```bash
+[command]
+```
+
+This should run very quickly:
+
+```console title="Output"
+[console output]
+```
+
+[adapt depending on tool behavior] You can find the output files in the same directory as the original data:
+
+```bash
+ls /data/[path]
+```
+
+```console title="Output"
+console output
+```
+
+[some explanation of the output; provide contents preview if applicable]
+
+### 1.4. Save the output files
+
+[adapt depending on tool behavior; delete if not necessary]
+
+Output files that were created _inside_ the container will be inaccessible to future work, so let's move these to a new directory.
+
+```bash
+mkdir /data/[path]
+mv [things]* /data/[path]
+```
+
+Now you will be able to use those files as inputs for subsequent analysis steps.
+
+### 1.5. Exit the container
+
+```bash
+exit
+```
+
+[Concluding statement]
+
+[For the first step: Now we're going to repeat this process for the other steps we plan to implement in our pipeline.]
+
+---
+
+## 2. [Name of second analysis step]
+
+[repeat as needed]
+
+[At final step: call out the final outputs]
+
+---
+
+### Takeaway
+
+You have reviewed the analysis method and tested all the individual commands interactively in the relevant containers.
+
+### What's next?
+
+Learn how to wrap those same commands into a multi-step workflow that uses containers to execute the work.

docs/nf4_science/course-template/index.md

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+---
+title: Nextflow for [course-name]
+hide:
+  - toc
+---
+
+# Nextflow for [course-name]
+
+This training course is intended for researchers in [main field] and related fields who are interested in developing or customizing data analysis pipelines.
+It builds on the [Hello Nextflow](../../hello_nextflow/) beginner training and demonstrates how to use Nextflow in the specific context of [course-name] analysis.
+
+Specifically, this course demonstrates how to implement a simple [course name or analysis] pipeline to [high-level summary].
+
+Let's get started! Click on the "Open in GitHub Codespaces" button below to launch the training environment (preferably in a separate tab), then read on while it loads.
+
+[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/nextflow-io/training?quickstart=1&ref=master)
+
+## Learning objectives
+
+By working through this course, you will learn how to apply foundational Nextflow concepts and tooling to a typical [course-name] use case.
+
+By the end of this workshop you will be able to:
+
+- Write a linear workflow to apply basic [course-name or applicable terms] methods
+- Handle domain-specific files such as [examples] appropriately
+- Handle [type of data] data
+- Leverage Nextflow's dataflow paradigm to parallelize [analysis]
+- [add as applicable] using relevant channel operators [and other features as applicable]
+
+<!-- TODO
+[optional advanced add-ons]
+- Configure pipeline execution and manage resource allocations
+- Handle metadata propagation
+- Implement per-step and end-to-end pipeline tests that handle analysis-specific idiosyncrasies appropriately
+-->
+
+## Prerequisites
+
+The course assumes some minimal familiarity with the following:
+
+- Tools and file formats commonly used in this scientific domain
+- Experience with the command line
+- Foundational Nextflow concepts and tooling covered in the [Hello Nextflow](../../hello_nextflow/) beginner training.
+
+For technical requirements and environment setup, see the [Environment Setup](../../envsetup/) mini-course.
+
+<!-- TODO
+For courses with the advanced add-ons, document the relevant prerequisites, eg:
+- Part X assumes familiarity with [Side Quest](url) or [Collection](url)
+-->