diff --git a/VERSION b/VERSION
index 4e379d2b..a211f863 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-0.0.2
+0.0.3-a.1
diff --git a/redcap_bridge/tests/test_utils.py b/redcap_bridge/tests/test_utils.py
index 2965d68d..48d109b4 100644
--- a/redcap_bridge/tests/test_utils.py
+++ b/redcap_bridge/tests/test_utils.py
@@ -1,4 +1,4 @@
-from redcap_bridge.utils import compress_record
+from redcap_bridge.utils import compress_record, conversion_to_odml_table_descriptor
 from diglab_utils.test_utils import test_directory, initialize_test_dir
 
 
@@ -13,3 +13,9 @@ def test_compressedCSV(initialize_test_dir):
             exp = exp_file.read()
             assert res == exp
 
+
+def test_conversion_to_odml_table_descriptor(initialize_test_dir):
+    test_dir = test_directory / 'testfiles_redcap' / 'descriptors'
+
+    conversion_to_odml_table_descriptor(test_dir / 'Vision4Action_DATA_2023-04-13_1110.csv', session_number=5)
+
diff --git a/redcap_bridge/utils.py b/redcap_bridge/utils.py
index 32881b30..f4c3f7dd 100644
--- a/redcap_bridge/utils.py
+++ b/redcap_bridge/utils.py
@@ -81,3 +81,32 @@ def remove_columns(csv_file, compressed_file=None):
 def exportCSVtoXLS(csv_file, compressed_file=None):
     read_file = pd.read_csv(csv_file, na_filter=False, dtype='str')
     read_file.to_excel(r'Path', index=None, header=True)
+
+def conversion_to_odml_table_descriptor(full_elabbook_csv, session_number):
+    """
+    Create odml descriptor file based on the full elabbook csv file
+    Args:
+        full_elabbook_csv: path to the full csv file
+        session_number: number of the session you want to create a descriptor csv file for
+    Returns:
+        descriptor_elabbook_csv: csv descriptor file of the specific session given
+    """
+    df = pd.read_csv(full_elabbook_csv)
+
+    df_ses = df.loc[df['ses_number'] == session_number]
+    cols_to_melt = df_ses.columns[df_ses.columns.get_loc('ethical_protocol_id'):]
+
+    df_melted = df_ses.melt(id_vars='record_id', value_vars=cols_to_melt)
+    df_melted = df_melted.rename(columns={'variable': 'Property name'})
+
+    value_types = df_melted['value'].apply(type).astype(str)
+
+    # Extract only type of the value
+    value_types = value_types.str.split("'").str[1]
+
+    # Add the Type value corresponding to the type of the value
+    df_melted['Type'] = value_types
+
+    print(df_melted.to_string())
+
+