diff --git a/README.md b/README.md
index 53d37c4..6780a4f 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,7 @@ N.B.: The ID codes of origins and destinations have to match the IDs of your cen
 Clone this repository, and run the tool by typing in the following command:
 
 ```
-python bundle_edges.py -c /path/to/centroids.csv -id ID_COLUMN -ew /path/to/edges.csv -o /path/to/output.gpkg
+python bundle_edges.py -c /path/to/centroids.csv -id ID_COLUMN -e /path/to/edges.csv -o /path/to/output.gpkg
 ```
 
 If you want to adjust some parameters of the bundling, such as weights or bundling threshold use the flags `-ew` for edge weights (default is 2), and `-t` for bundling threshold (default is 2). The edge weights dictate how powerful the "gravity" of long edges are. The bundling threshold sets the distance limit for how many times longer the bundled edges can be compared to straight line distances, flows that are longer than the threshold are not bundled but remain as straight line geometries in the output.
@@ -59,6 +59,13 @@ Please note, the script expects the coordinates to be in WGS84 [(EPSG:4326)](htt
 
 We have provided two test CSV files that demonstrate the data structure of the required CSV files. These files can be found under the `example_data` directory.
 
+To run the analysis on the example data use the following command:
+
+```
+python bundle_edges.py -c example_data/NUTS3_centroids.csv -id NUTS_ID -e example_data/NUTS3_edges.csv -o NUTS3_output.gpkg
+```
+
+
 ## References
 Wallinger, M., Archambault, D., Auber, D., Nöllenburg, M., & Peltonen, J. (2022). Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach. IEEE Transactions on Visualization and Computer Graphics, 28(1), 313–323. https://doi.org/10.1109/TVCG.2021.3114795