diff --git a/misc/prototype_mapshaper.py b/misc/prototype_mapshaper.py
index 49360a81..f8cd2e3f 100644
--- a/misc/prototype_mapshaper.py
+++ b/misc/prototype_mapshaper.py
@@ -1,9 +1,10 @@
+# IGN DATASET
 from cartiflette.s3 import upload_s3_raw
 from cartiflette.pipeline import crossproduct_parameters_production
 from cartiflette.pipeline import mapshaperize_split_from_s3, mapshaperize_merge_split_from_s3
 from cartiflette.download.download import _download_sources
 
-path_within_bucket = "test-download11"
+path_within_bucket = "test-download16"
 
 
 # DATA RETRIEVING STEP =========================
@@ -11,7 +12,7 @@
 # IGN DATASET
 path_bucket_adminexpress = upload_s3_raw(
     path_within_bucket=path_within_bucket,
-    year = 2022
+    year=2022
 )
 
 path_bucket_cog_commune = upload_s3_raw(
@@ -76,8 +77,95 @@
     vectorfile_format="csv",
     path_within_bucket=path_within_bucket
     )
+from cartiflette.s3 import upload_s3_raw
+from cartiflette.pipeline import crossproduct_parameters_production
+from cartiflette.pipeline import mapshaperize_split_from_s3, mapshaperize_merge_split_from_s3
+from cartiflette.download.download import _download_sources
+
+path_within_bucket = "test-download11"
+
+
+# DATA RETRIEVING STEP =========================
+
+# IGN DATASET
+path_bucket_adminexpress = upload_s3_raw(
+    path_within_bucket=path_within_bucket,
+    year = 2022
+)
+
+path_bucket_cog_commune = upload_s3_raw(
+    provider='Insee',
+    dataset_family='COG',
+    source="COMMUNE",
+    territory="france_entiere",
+    borders="DATASET_INSEE_COG_COMMUNE_FRANCE_ENTIERE_2022",
+    year=2022,
+    crs=None,
+    vectorfile_format="csv",
+    path_within_bucket=path_within_bucket
+    )
+
+# DEPARTEMENT (FOR COMMON NAMES)
+path_bucket_cog_departement = upload_s3_raw(
+    provider='Insee',
+    dataset_family='COG',
+    source="DEPARTEMENT",
+    territory="france_entiere",
+    borders="DATASET_INSEE_COG_DEPARTEMENT_FRANCE_ENTIERE_2022",
+    year=2022,
+    crs=None,
+    vectorfile_format="csv",
+    path_within_bucket=path_within_bucket
+    )
+
+# REGIONS (FOR COMMON NAMES)
+path_bucket_cog_region = upload_s3_raw(
+    provider='Insee',
+    dataset_family='COG',
+    source="REGION",
+    territory="france_entiere",
+    borders="DATASET_INSEE_COG_REGION_FRANCE_ENTIERE_2022",
+    year=2022,
+    crs=None,
+    vectorfile_format="csv",
+    path_within_bucket=path_within_bucket
+    )
+
+# TABLE PASSAGE COMMUNES, DEP, REGIONS
+path_bucket_tagc_appartenance = upload_s3_raw(
+    provider='Insee',
+    dataset_family='TAGC',
+    source="APPARTENANCE",
+    territory="france_entiere",
+    borders="table-appartenance-geo-communes-22",
+    year=2022,
+    crs=None,
+    vectorfile_format="xlsx",
+    path_within_bucket=path_within_bucket
+    )
+
+path_bucket_tagc_passage = upload_s3_raw(
+    provider='Insee',
+    dataset_family='TAGC',
+    source="PASSAGE",
+    territory="france_entiere",
+    borders="table_passage_geo2003_geo2023",
+    year=2023,
+    crs=None,
+    vectorfile_format="xlsx",
+    path_within_bucket=path_within_bucket
+    )
+
 
 # PUTTING ALL METADATA TOGETHER
+path_tagc = fs.ls(path_bucket_tagc_appartenance)[0]
+path_bucket_cog_departement = fs.ls(path_bucket_cog_departement)[0]
+
+with fs.open(path_tagc, mode = "rb") as remote_file:
+    tagc = pd.read_excel(remote_file, skiprows=5, dtype_backend="pyarrow")
+
+with fs.open(path_bucket_cog_departement, mode = "rb") as remote_file:
+    cog_dep = pd.read_csv(remote_file, dtype_backend="pyarrow")