diff --git a/examples/aggregation_zones_example.ipynb b/examples/aggregation_zones_example.ipynb
index 152d80dc..967e01c8 100644
--- a/examples/aggregation_zones_example.ipynb
+++ b/examples/aggregation_zones_example.ipynb
@@ -58,8 +58,8 @@
     "from pathlib import Path\n",
     "import os \n",
     "#Load aggregation zones as GeoDataFrames\n",
-    "exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones_example\" / \"exposure\" / \"buildings.gpkg\")\n",
-    "base_zone=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones_example\" / \"aggregation_zones\" / \"base_zones.gpkg\")\n",
+    "exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"exposure\" / \"buildings.gpkg\")\n",
+    "base_zone=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"aggregation_zones\" / \"base_zones.gpkg\")\n",
     "\n",
     "m = base_zone.explore(column = 'ZONE_BASE')\n",
     "m = exposure.explore(m=m, color = '#FFFACD')\n",
@@ -179,7 +179,7 @@
     "#Let's read the yaml file with the required information \n",
     "#and set up the FIAT model for the two test cases. \n",
     "\n",
-    "with open(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones_example\" / \"config_aggregation.yml\", 'r') as file:\n",
+    "with open(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"config_aggregation.yml\", 'r') as file:\n",
     "    config_aggregation = yaml.safe_load(file)\n",
     "\n",
     "print(json.dumps(config_aggregation, indent=4, sort_keys=False))"
@@ -208,7 +208,7 @@
    "outputs": [],
    "source": [
     "# Set up Fiat Model\n",
-    "root = Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones_example\"\n",
+    "root = Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"fiat_model\"\n",
     "\n",
     "#If case exist\n",
     "if Path(config_aggregation[\"cases\"][\"test1_single_aggregation\"][\"new_root\"]).exists():\n",
@@ -320,10 +320,10 @@
    "outputs": [],
    "source": [
     "#Load *.csv into dataframe\n",
-    "df_single_aggregation = pd.read_csv(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones_example\" / \"output\" / \"aggregation_zones_test1\" / \"exposure\" / \"exposure.csv\")\n",
+    "df_single_aggregation = pd.read_csv(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"output\" / \"aggregation_zones_test1\" / \"exposure\" / \"exposure.csv\")\n",
     "\n",
     "#Load original exposure geopackage into GeoDataFrame\n",
-    "new_exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones_example\" / \"output\" / \"aggregation_zones_test1\" / \"exposure\" / \"buildings.gpkg\")\n",
+    "new_exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"output\" / \"aggregation_zones_test1\" / \"exposure\" / \"buildings.gpkg\")\n",
     "\n",
     "#Merge dataframe with GeoDataFrame\n",
     "merged_gdf = new_exposure.merge(df_single_aggregation, left_on='Object ID', right_on='Object ID', how='inner')\n",
@@ -354,10 +354,10 @@
    "outputs": [],
    "source": [
     "#Load *.csv into dataframe\n",
-    "df_multiple_aggregation = pd.read_csv(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones_example\" / \"output\" / \"aggregation_zones_test2\" / \"exposure\" / \"exposure.csv\")\n",
+    "df_multiple_aggregation = pd.read_csv(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"output\" / \"aggregation_zones_test2\" / \"exposure\" / \"exposure.csv\")\n",
     "\n",
     "#Load original exposure geopackage into GeoDataFrame\n",
-    "new_exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones_example\" / \"output\" / \"aggregation_zones_test2\" / \"exposure\" / \"buildings.gpkg\")\n",
+    "new_exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"output\" / \"aggregation_zones_test2\" / \"exposure\" / \"buildings.gpkg\")\n",
     "\n",
     "#Merge dataframe with GeoDataFrame\n",
     "merged_gdf_multiple = new_exposure.merge(df_multiple_aggregation, left_on='Object ID', right_on='Object ID', how='inner')"