diff --git a/doc.zih.tu-dresden.de/docs/access/jupyterhub.md b/doc.zih.tu-dresden.de/docs/access/jupyterhub.md
index 3a9751596fb9b3d6815fcb078d13782598e1cf07..e4d81db3f0dadbe46add42a586035bb8f0c70845 100644
--- a/doc.zih.tu-dresden.de/docs/access/jupyterhub.md
+++ b/doc.zih.tu-dresden.de/docs/access/jupyterhub.md
@@ -261,156 +261,24 @@ With these **standard environments** we have tried to integrate a set of compati
\*\* R is loaded from the [module system](../software/modules.md)
-### Creating and Using a Custom Environment
-!!! info
-
- Interactive code interpreters which are used by Jupyter notebooks are called
- *kernels*. Creating and using your own kernel has the benefit that you can
- install your own preferred Python packages and use them in your notebooks.
-
-We currently have two different architectures at ZIH systems.
-Build your kernel environment on the **same architecture** that you want to use
-later on with the kernel. In the examples below, we use the name
-"my-kernel" for our user kernel. We recommend to prefix your kernels
-with keywords like `haswell`, `ml`, `romeo`, `venv`, `conda`. This way, you
-can later recognize easier how you built the kernel and on which hardware it
-will work. Depending on that hardware, allocate resources:
-
-=== "Nodes with x86_64 (Intel) CPU"
- Use **one srun command** of these:
-
- ```console
- maria@login$ srun --partition=haswell64 --pty --ntasks=1 --cpus-per-task=2 \
- --mem-per-cpu=2541 --time=08:00:00 bash -l
- maria@login$ srun --partition=gpu2 --pty --ntasks=1 --cpus-per-task=2 \
- --mem-per-cpu=2541 --time=08:00:00 bash -l
- ```
-=== "Nodes with x86_64 (AMD) CPU"
- Use **one srun command** of these:
-
- ```console
- maria@login$ srun --partition=romeo --pty --ntasks=1 --cpus-per-task=3 \
- --mem-per-cpu=1972 --time=08:00:00 bash -l
- maria@login$ srun --partition=alpha --gres=gpu:1 --pty --ntasks=1 \
- --cpus-per-task=6 --mem-per-cpu=10312 --time=08:00:00 bash -l
- ```
-=== "Nodes with ppc64le CPU"
- ```console
- maria@login$ srun --pty --partition=ml --ntasks=1 --cpus-per-task=2 --mem-per-cpu=1443 \
- --time=08:00:00 bash -l
- ```
-
-When creating a virtual environment in your home directory, you got to decide
-to either use "Python virtualenv" or "conda environment".
-
-!!! note
- Please keep in mind that Python virtualenv is the preferred way to create a Python
- virtual environment.
- For working with conda virtual environments, it may be necessary to configure your shell
- as described in [Python virtual environments](../software/python_virtual_environments.md#conda-virtual-environment)
-
-#### Python Virtualenv
-
-```console
-marie@compute$ module load Python/3.8.6-GCCcore-10.2.0
-Module Python/3.8.6-GCCcore-10.2.0 and 11 dependencies loaded.
-marie@compute$ mkdir user-kernel # please use workspaces!
-marie@compute$ cd user-kernel
-marie@compute$ virtualenv --system-site-packages my-kernel
-created virtual environment CPython3.8.6.final.0-64 in 5985ms
- creator CPython3Posix(dest=[...]/my-kernel, clear=False, global=True)
- seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=[...])
- added seed packages: pip==20.2.3, setuptools==50.3.0, wheel==0.35.1
- activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
-marie@compute$ source my-kernel/bin/activate
-(my-kernel) marie@compute$ pip install ipykernel
-Collecting ipykernel
-[...]
-Successfully installed [...] ipykernel-6.9.1 ipython-8.0.1 [...]
-(my-kernel) marie@compute$ pip install --upgrade pip
-(my-kernel) marie@compute$ python -m ipykernel install --user --name my-kernel --display-name="my kernel"
-Installed kernelspec my-kernel in .../.local/share/jupyter/kernels/my-kernel
-(my-kernel) marie@compute$ pip install [...] # now install additional packages for your notebooks
-(my-kernel) marie@compute$ deactivate
-```
-
-!!! warning
-
- Depending on the Python module you have loaded for creating your virtual environment, you should
- select the apropriate [Standard environment](#standard-environments). For example, you could
- select `scs5_gcccore-10.2.0_python-3.8.6`, when you want to use `my-kernel`. Furthermore,
- ensure, that you pre-load the same modules via [Spawner Options](#start-a-session) that you used
- for creating your kernel.
-
-#### Conda Environment
-
-Load the needed module depending on partition architecture:
-
-=== "x86 nodes (e.g. partition `haswell`, `gpu2`)"
- ```console
- marie@compute$ module load Anaconda3
- ```
-=== "PowerPC nodes (partition `ml`)"
- ```console
- marie@ml$ module load PythonAnaconda
- ```
-
-!!! hint
- For working with conda virtual environments, it may be necessary to configure your shell as
- described in
- [Python virtual environments](../software/python_virtual_environments.md#conda-virtual-environment).
-
-Continue with environment creation, package installation and kernel
-registration:
-
-```console
-marie@compute$ mkdir user-kernel # please use workspaces!
-marie@compute$ conda create --prefix $HOME/user-kernel/my-kernel python=3.8.6
-Collecting package metadata: done
-Solving environment: done
-[...]
-marie@compute$ conda activate $HOME/user-kernel/my-kernel
-marie@compute$ conda install ipykernel
-Collecting package metadata: done
-Solving environment: done
-[...]
-marie@compute$ python -m ipykernel install --user --name my-kernel --display-name="my kernel"
-Installed kernelspec my-kernel in [...]
-marie@compute$ conda install [..] # now install additional packages for your notebooks
-marie@compute$ conda deactivate
-```
-
-Now you can start a new session and your kernel should be available.
-
-**JupyterLab**: Your kernels are listed on the launcher page:
-
-
-{: align="center"}
-
-You can switch kernels of existing notebooks in the menu:
-
-
-{: align="center"}
-
-**Classic Jupyter notebook**: Your kernel is listed in the New menu:
-
-
-{: align="center"}
-
-You can switch kernels of existing notebooks in the kernel menu:
-
-
-{: align="center"}
-
-!!! note
- Both python venv and conda virtual environments will be mention in the same
- list.
### Loading Modules
You have now the option to preload modules from the [module system](../software/modules.md).
Select multiple modules that will be preloaded before your notebook server
starts. The list of available modules depends on the module environment you want
-to start the session in (`scs5` or `ml`). The right module environment will be
+to start the session in (`scs5`, `hiera` or `ml`). The right module environment will be
chosen by your selected partition.
+
+### Custom Kernels
+
+As you might have noticed, after launching Jupyter**Lab**,
+there are several boxes with icons therein visible in the `Launcher`.
+Each box therein represents a so called 'Kernel'
+(note that these are not to be confused with Operating System Kernel,
+but similarly provide basic functionality for running your use cases,
+e.g. Python or R)
+
+You can find further documentation on creating your own Kernels [here](/access/jupyterhub_custom_environments)
+
diff --git a/doc.zih.tu-dresden.de/docs/access/jupyterhub_custom_environments.md b/doc.zih.tu-dresden.de/docs/access/jupyterhub_custom_environments.md
new file mode 100644
index 0000000000000000000000000000000000000000..8fdd3dc1ceb86ddce40a409c49367fef70018d61
--- /dev/null
+++ b/doc.zih.tu-dresden.de/docs/access/jupyterhub_custom_environments.md
@@ -0,0 +1,147 @@
+# Creating and Using a Custom Environment for JupyterHub
+
+!!! info
+
+ Interactive code interpreters which are used by Jupyter notebooks are called
+ *kernels*. Creating and using your own kernel has the benefit that you can
+ install your own preferred Python packages and use them in your notebooks.
+
+We currently have two different architectures at ZIH systems.
+Build your kernel environment on the **same architecture** that you want to use
+later on with the kernel. In the examples below, we use the name
+"my-kernel" for our user kernel. We recommend to prefix your kernels
+with keywords like `haswell`, `ml`, `romeo`, `venv`, `conda`. This way, you
+can later recognize easier how you built the kernel and on which hardware it
+will work. Depending on that hardware, allocate resources:
+
+## Preliminary Steps
+
+=== "Nodes with x86_64 (Intel) CPU"
+ Use **one srun command** of these:
+
+ ```console
+ maria@login$ srun --partition=haswell64 --pty --ntasks=1 --cpus-per-task=2 \
+ --mem-per-cpu=2541 --time=08:00:00 bash -l
+ maria@login$ srun --partition=gpu2 --pty --ntasks=1 --cpus-per-task=2 \
+ --mem-per-cpu=2541 --time=08:00:00 bash -l
+ ```
+=== "Nodes with x86_64 (AMD) CPU"
+ Use **one srun command** of these:
+
+ ```console
+ maria@login$ srun --partition=romeo --pty --ntasks=1 --cpus-per-task=3 \
+ --mem-per-cpu=1972 --time=08:00:00 bash -l
+ maria@login$ srun --partition=alpha --gres=gpu:1 --pty --ntasks=1 \
+ --cpus-per-task=6 --mem-per-cpu=10312 --time=08:00:00 bash -l
+ ```
+=== "Nodes with ppc64le CPU"
+ ```console
+ maria@login$ srun --pty --partition=ml --ntasks=1 --cpus-per-task=2 --mem-per-cpu=1443 \
+ --time=08:00:00 bash -l
+ ```
+
+When creating a virtual environment in your home directory, you got to decide
+to either use "Python virtualenv" or "conda environment".
+
+!!! note
+ Please keep in mind that Python virtualenv is the preferred way to create a Python
+ virtual environment.
+ For working with conda virtual environments, it may be necessary to configure your shell
+ as described in [Python virtual environments](../software/python_virtual_environments.md#conda-virtual-environment)
+
+## Python Virtualenv
+
+```console
+marie@compute$ module load Python/3.8.6-GCCcore-10.2.0
+Module Python/3.8.6-GCCcore-10.2.0 and 11 dependencies loaded.
+marie@compute$ mkdir user-kernel # please use workspaces!
+marie@compute$ cd user-kernel
+marie@compute$ virtualenv --system-site-packages my-kernel
+created virtual environment CPython3.8.6.final.0-64 in 5985ms
+ creator CPython3Posix(dest=[...]/my-kernel, clear=False, global=True)
+ seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=[...])
+ added seed packages: pip==20.2.3, setuptools==50.3.0, wheel==0.35.1
+ activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
+marie@compute$ source my-kernel/bin/activate
+(my-kernel) marie@compute$ pip install ipykernel
+Collecting ipykernel
+[...]
+Successfully installed [...] ipykernel-6.9.1 ipython-8.0.1 [...]
+(my-kernel) marie@compute$ pip install --upgrade pip
+(my-kernel) marie@compute$ python -m ipykernel install --user --name my-kernel --display-name="my kernel"
+Installed kernelspec my-kernel in .../.local/share/jupyter/kernels/my-kernel
+(my-kernel) marie@compute$ pip install [...] # now install additional packages for your notebooks
+(my-kernel) marie@compute$ deactivate
+```
+
+!!! warning
+
+ Depending on the Python module you have loaded for creating your virtual environment, you should
+ select the apropriate [Standard environment](#standard-environments). For example, you could
+ select `scs5_gcccore-10.2.0_python-3.8.6`, when you want to use `my-kernel`. Furthermore,
+ ensure, that you pre-load the same modules via [Spawner Options](#start-a-session) that you used
+ for creating your kernel.
+
+## Conda Environment
+
+Load the needed module depending on partition architecture:
+
+=== "x86 nodes (e.g. partition `haswell`, `gpu2`)"
+ ```console
+ marie@compute$ module load Anaconda3
+ ```
+=== "PowerPC nodes (partition `ml`)"
+ ```console
+ marie@ml$ module load PythonAnaconda
+ ```
+
+!!! hint
+ For working with conda virtual environments, it may be necessary to configure your shell as
+ described in
+ [Python virtual environments](../software/python_virtual_environments.md#conda-virtual-environment).
+
+Continue with environment creation, package installation and kernel
+registration:
+
+```console
+marie@compute$ mkdir user-kernel # please use workspaces!
+marie@compute$ conda create --prefix $HOME/user-kernel/my-kernel python=3.8.6
+Collecting package metadata: done
+Solving environment: done
+[...]
+marie@compute$ conda activate $HOME/user-kernel/my-kernel
+marie@compute$ conda install ipykernel
+Collecting package metadata: done
+Solving environment: done
+[...]
+marie@compute$ python -m ipykernel install --user --name my-kernel --display-name="my kernel"
+Installed kernelspec my-kernel in [...]
+marie@compute$ conda install [..] # now install additional packages for your notebooks
+marie@compute$ conda deactivate
+```
+
+Now you can start a new session and your kernel should be available.
+
+**JupyterLab**: Your kernels are listed on the launcher page:
+
+
+{: align="center"}
+
+You can switch kernels of existing notebooks in the menu:
+
+
+{: align="center"}
+
+**Classic Jupyter notebook**: Your kernel is listed in the New menu:
+
+
+{: align="center"}
+
+You can switch kernels of existing notebooks in the kernel menu:
+
+
+{: align="center"}
+
+!!! note
+ Both python venv and conda virtual environments will be mention in the same
+ list.