diff --git a/README.md b/README.md
index a5317e05f1e48cc3fb03b304fa684bb502b95431..8decf00b29f91e0a3a014bbf57e92aff12c5e035 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 
 MLAir (Machine Learning on Air data) is an environment that simplifies and accelerates the creation of new machine 
 learning (ML) models for the analysis and forecasting of meteorological and air quality time series. You can find the
-docs [here](http://toar.pages.jsc.fz-juelich.de/mlair/docs/).
+docs [here](https://esde.pages.jsc.fz-juelich.de/machine-learning/mlair/docs/).
 
 [[_TOC_]]
 
@@ -34,7 +34,7 @@ HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-sy
 * Installation of **MLAir**:
     * Either clone MLAir from the [gitlab repository](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git) 
       and use it without installation (beside the requirements) 
-    * or download the distribution file ([current version](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-1.5.0-py3-none-any.whl)) 
+    * or download the distribution file ([current version](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.0.0-py3-none-any.whl)) 
       and install it via `pip install <dist_file>.whl`. In this case, you can simply import MLAir in any python script 
       inside your virtual environment using `import mlair`.
 
@@ -89,7 +89,7 @@ The installation on Windows is not tested yet.
 In this section, we show three examples how to work with MLAir. Note, that for these examples MLAir was installed using
 the distribution file. In case you are using the git clone it is required to adjust the import path if not directly
 executed inside the source directory of MLAir. There is also a downloadable 
-[Jupyter Notebook](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/supplement/Examples_from_manuscript.ipynb) 
+[Jupyter Notebook](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/supplement/Examples_from_manuscript.ipynb) 
 provided in that you can run the following examples. Note that this notebook still requires an installation of MLAir.
 
 ## Example 1
diff --git a/docs/_source/installation.rst b/docs/_source/installation.rst
index 7da1f25c28c42d302c31d050d06340e0c0f95e11..6ac4937e6a729c12e54007aa32f0e59635289fdd 100644
--- a/docs/_source/installation.rst
+++ b/docs/_source/installation.rst
@@ -27,7 +27,7 @@ Installation of MLAir
 * Install all requirements from `requirements.txt <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/requirements.txt>`_
   preferably in a virtual environment
 * Either clone MLAir from the `gitlab repository <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git>`_
-* or download the distribution file (`current version <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-1.5.0-py3-none-any.whl>`_)
+* or download the distribution file (`current version <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.0.0-py3-none-any.whl>`_)
   and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply
   import MLAir in any python script inside your virtual environment using :py:`import mlair`.