diff --git a/times_reader/__main__.py b/times_reader/__main__.py
index 67fb273..9340d08 100644
--- a/times_reader/__main__.py
+++ b/times_reader/__main__.py
@@ -77,13 +77,14 @@ def convert_xl_to_times(
         transforms.generate_all_regions,
         transforms.capitalise_attributes,
         transforms.apply_fixups,
-        transforms.extract_commodity_groups,
+        transforms.generate_commodity_groups,
         transforms.fill_in_missing_pcgs,
         transforms.generate_top_ire,
         transforms.include_tables_source,
         transforms.merge_tables,
         transforms.apply_more_fixups,
         transforms.process_years,
+        transforms.complete_commodity_groups,
         transforms.process_uc_wildcards,
         transforms.process_wildcards,
         transforms.convert_aliases,
diff --git a/times_reader/config/times_mapping.txt b/times_reader/config/times_mapping.txt
index d707da6..bec9585 100644
--- a/times_reader/config/times_mapping.txt
+++ b/times_reader/config/times_mapping.txt
@@ -84,7 +84,7 @@ SHAPE[J,AGE,VALUE] = ~FI_T(Other_Indexes,Year,VALUE,Attribute:SHAPE)
 STGIN_BND[REG,DATAYEAR,PRC,COM,TSLVL,BD,VALUE] = ~FI_T(Region,Year,TechName,CommName,TimeSlice,LimType,VALUE,Attribute:STGIN_BND)
 STG_EFF[REG,DATAYEAR,PRC,VALUE] = ~FI_T(Region,Year,TechName,VALUE,Attribute:STG_EFF)
 STG_LOSS[REG,DATAYEAR,PRC,TSLVL,VALUE] = ~FI_T(Region,Year,TechName,TimeSlice,VALUE,Attribute:STG_LOSS)
-TOP[REG,PRC,COM,IO] = COMM_GROUPS(Region,TechName,CommName,IO)
+TOP[REG,PRC,COM,IO] = TOPOLOGY(Region,TechName,CommName,IO)
 TOP_IRE[ALL_REG,COM,ALL_R,C,PRC] = TOP_IRE(Origin,IN,Destination,OUT,TechName)
 TS_GROUP[REG,TSLVL,TS_GROUP] = TimeSlicesGroup(Region,TSLVL,TS_GROUP)
 TS_MAP[REG,PARENT,TS_MAP] = TimeSliceMap(Region,Parent,TimesliceMap)
diff --git a/times_reader/transforms.py b/times_reader/transforms.py
index 0789a21..7ac09d1 100644
--- a/times_reader/transforms.py
+++ b/times_reader/transforms.py
@@ -1144,7 +1144,7 @@ def apply_fixups_table(table: datatypes.EmbeddedXlTable):
     return [apply_fixups_table(table) for table in tables]
 
 
-def extract_commodity_groups(
+def generate_commodity_groups(
     config: datatypes.Config,
     tables: List[datatypes.EmbeddedXlTable],
 ) -> List[datatypes.EmbeddedXlTable]:
@@ -1253,7 +1253,7 @@ def name_comm_group(df):
             range="",
             filename="",
             uc_sets={},
-            tag="COMM_GROUPS",
+            tag="TOPOLOGY",
             dataframe=comm_groups,
         )
     )
@@ -1274,6 +1274,23 @@ def name_comm_group(df):
     return tables
 
 
+def complete_commodity_groups(
+    config: datatypes.Config, tables: Dict[str, DataFrame]
+) -> Dict[str, DataFrame]:
+    """
+    Complete the list of commodity groups
+    """
+
+    commodities = generate_topology_dictionary(tables)["commodities_by_name"].rename(
+        columns={"commname": "commoditygroup"}
+    )
+    cgs_in_top = tables["TOPOLOGY"]["commoditygroup"].to_frame()
+    commodity_groups = pd.concat([commodities, cgs_in_top])
+    tables["COMM_GROUPS"] = commodity_groups.drop_duplicates(keep="first").reset_index()
+
+    return tables
+
+
 def generate_top_ire(
     config: datatypes.Config,
     tables: List[datatypes.EmbeddedXlTable],
@@ -1285,7 +1302,7 @@ def generate_top_ire(
     veda_set_ext_reg_mapping = {"IMP": "IMPEXP", "EXP": "IMPEXP", "MIN": "MINRNW"}
     dummy_process_cset = [["NRG", "IMPNRGZ"], ["MAT", "IMPMATZ"], ["DEM", "IMPDEMZ"]]
     veda_process_sets = utils.single_table(tables, "VedaProcessSets").dataframe
-    com_map = utils.single_table(tables, "COMM_GROUPS").dataframe
+    com_map = utils.single_table(tables, "TOPOLOGY").dataframe
 
     ire_prc = pd.DataFrame(columns=["region", "techname"])
     for table in tables:
@@ -1370,7 +1387,7 @@ def expand_pcg_from_suffix(df):
         else:
             df = table.dataframe.copy()
             df["primarycg"] = df.apply(expand_pcg_from_suffix, axis=1)
-            default_pcgs = utils.single_table(tables, "COMM_GROUPS").dataframe.copy()
+            default_pcgs = utils.single_table(tables, "TOPOLOGY").dataframe.copy()
             default_pcgs = default_pcgs.loc[
                 default_pcgs["DefaultVedaPCG"] == 1,
                 ["region", "techname", "commoditygroup"],
@@ -2264,11 +2281,11 @@ def rename_cgs(
 
 
 def apply_more_fixups(
-    config: datatypes.Config, input: Dict[str, DataFrame]
+    config: datatypes.Config, tables: Dict[str, DataFrame]
 ) -> Dict[str, DataFrame]:
     output = {}
     # TODO: This should only be applied to processes introduced in BASE
-    for table_type, df in input.items():
+    for table_type, df in tables.items():
         if table_type == datatypes.Tag.fi_t:
             index = df["attribute"] == "STOCK"
             # Temporary solution to include only processes defined in BASE
@@ -2281,6 +2298,7 @@ def apply_more_fixups(
                         i_reg_prc = i_reg & (df["techname"] == process)
                         if any(i_reg_prc):
                             extra_rows.append(["NCAP_BND", region, process, "UP", 0, 2])
+                        # TODO: TIMES already handles this. Drop?
                         if len(df[i_reg_prc]["year"].unique()) == 1:
                             year = df[i_reg_prc]["year"].unique()[0]
                             i_attr = (
@@ -2312,6 +2330,15 @@ def apply_more_fixups(
                             ),
                         ]
                     )
+        # TODO: Handle defaults in a general way.
+        # Use uc_n value if uc_desc is missing
+        elif table_type == datatypes.Tag.uc_t:
+            for uc_n in df["uc_n"].unique():
+                index = df["uc_n"] == uc_n
+                if all(df["uc_desc"][index].isna()):
+                    # Populate the first row only
+                    if any(index):
+                        df.at[list(index).index(True), "uc_desc"] = uc_n
 
         output[table_type] = df