diff --git a/README.md b/README.md
index 3145579448b0a1b341f933e012fb9fe72c6643a2..a188c5cb6af17664063fa5ddc2f6cde70d383595 100644
--- a/README.md
+++ b/README.md
@@ -136,13 +136,25 @@ and then install the prerequisites in requirement.txt
 
 ## How to use Shire
 
-### Necessary manual preparations
-
 ### Gui version
+#### Necessary manual preparations
+
+keys_to_include-file: 
+  - format: csv-file
+  - list of feature names to be included as features in the mapping processing
+  - feature names have to match feature names provided in the data_summary-file 
+
+data_summary-file:
+  - format: csv-file
+  - table with the columns providing information on path where the dataset is stored, feature name, list of no data values, boolen parameter whether the dataset is categorical  
+  - no column headers should be included
+  - all fields have to be provided
+
+#### Running Shire
 
 Shire can be intuitively started by running shire.py which launches the gui. The gui queries important user-defined parameters and data-specific information in order to then automatically create training and prediction datasets and/or create the susceptibility map using existing input datasets. 
 
-The initial window (see Fig. 1) see covers the basic information on the resulting map:
+The initial window (see Fig. 1) covers the basic information on the resulting map:
 ![Semantic description of image](images/general_settings.png)Fig.1: GUI for defining the general settings for the mapping project
 
 - _What do you want to do?_: Here it can be defined what should be done in this run. The options can either be ticked individually or in various combinations. _Training dataset_ and _prediction dataset_ launch the training and prediction dataset generation, _Map generation_ launches the susceptibilty map generation using already existing input datasets