work on

sphinx-doc/getting_started/InstallationGuide.rst

@@ -5,20 +5,14 @@ SaQC is written in Python, so the easiest way to set up your system to use SaQC
 for your needs is using the Python Package Index (PyPI). It might be a good idea
 to install SaQC into its own virtual environment. 
 
-Contents
---------
-
-#. :ref:`Set up a virtual environment <getting_started/InstallationGuide:1. system requirements>`
-#. :ref:`Set up a virtual environment <getting_started/InstallationGuide:2. set up a virtual environment>`
-#. :ref:`Get SaQC <getting_started/InstallationGuide:3. get saqc>`
 
-1. System Requirements
-----------------------
+System Requirements
+-------------------
 SaQC is tested to run with Python version from 3.7 to 3.9 on 64-bit operating systems (Linux and Windows).
 
 
-2. Set up a virtual environment
---------------------------------
+Set up a virtual environment
+-----------------------------
 
 The exact process depends on your operating system and python version/distribution.
 The following sections should get you started on UNIX-like Systems and Windows.
@@ -50,8 +44,8 @@ On Windows-systems
    ./activate
 
 
-3. Get SaQC
------------
+Get SaQC
+--------
 
 We currently distribute SaQC via the `Python Package Index (PyPI) <https://pypi.org/>`_
 or through our `GitLab-repository <https://git.ufz.de/rdm-software/saqc>`_.

sphinx-doc/index.rst

@@ -10,15 +10,26 @@
    Package API <moduleAPIs/saqcInit>
 
 
-
-Tutorials and Topics
-====================
+Getting Started
+===============
 
 .. toctree::
+   :caption: Getting started
    :maxdepth: 1
+   :hidden:
 
    getting_started/InstallationGuide
+   getting_started/TutorialAPI
    getting_started/GettingStarted
+
+Cookbooks
+=========
+
+.. toctree::
+   :caption: Cookbooks
+   :maxdepth: 1
+   :hidden:
+
    cook_books/DataRegularisation
    cook_books/OutlierDetection
    cook_books/MultivariateFlagging

sphinx-doc/misc/title.rst

@@ -77,8 +77,8 @@ Features
 .. list-table::
 
     * - |pyLogo| |csvConfig|
-      - * get SaQC from PyPI
-        * use SaQC as a commandline application and configure your flagging pipelines via plain .csv files
+      - * :ref:`get and install SaQC <getting_started/InstallationGuide:installation guide>`
+        * use SaQC as a commandline application and configure your pipelines via plain text
         * or use the SaQC python API, enabling integration in your python processing script
     * - |sacRaw|
       - * easily load data from multiple sources, concatenating them in a SaQC object
@@ -90,10 +90,8 @@ Features
       - * automatically keep track of flagging history and flags significance for every datapoint
         * define and use custom schemes to translate your flags to and from SaQC
     * - |sacProc|
-      - * modifyyour data, by interpolations, corrections and transformations
+      - * modify your data by interpolations, corrections and transformations
         * calculate data products, such as residues or outlier scores
         * automatically keep track of labeling history and label significance
     * - |sacMV|
       - * apply multivariate flagging function
-
-