diff --git a/ersilia/core/tracking.py b/ersilia/core/tracking.py
index eefd5c76d..f61464f15 100644
--- a/ersilia/core/tracking.py
+++ b/ersilia/core/tracking.py
@@ -14,7 +14,36 @@
 TEMP_FILE_LOGS = os.path.abspath("")
 
 
+def create_csv(output_df):
+    """
+    This function takes in the output dataframe from the model run and returns
+    a new temporary csv file that will later be passed to CDD vault. The CSV
+    file has two columns: the first column is the input molecules and the
+    second column is the ISO-formatted time of the run.
+
+    :param file: The output dataframe from the model run
+    :return: A new temporary csv file
+    """
+
+    new_df = output_df[['input']].copy()
+    current_time = datetime.now().isoformat()
+
+    new_df['time'] = current_time
+    csv_file = tempfile.NamedTemporaryFile(mode="w", suffix=".csv")
+    new_df.to_csv(csv_file.name, index=False)
+    
+    return csv_file
+
+
 def log_files_metrics(file):
+    """
+    This function will log the number of errors and warnings in the log files.
+
+    :param file: The log file to be read
+    :return: None (writes to file)
+    """
+
+
     error_count = 0
     warning_count = 0
 
@@ -218,6 +247,15 @@ def get_file_sizes(self, input_df, output_df):
         }
 
     def check_types(self, resultDf, metadata):
+        """
+        This class is responsible for checking the types of the output dataframe against the expected types.
+        This includes checking the shape of the output dataframe (list vs single) and the types of each column.
+
+        :param resultDf: The output dataframe
+        :param metadata: The metadata dictionary
+        :return: A dictionary containing the number of mismatched types and a boolean for whether the shape is correct
+        """
+
         typeDict = {"float64": "Float", "int64": "Int"}
         count = 0
 
@@ -237,7 +275,8 @@ def check_types(self, resultDf, metadata):
         else:
             print("Output is correct shape.")
             correct_shape = True
-
+ 
+        print(resultDf)
         print("Output has", count, "mismatched types.\n")
 
         return {"mismatched_types": count, "correct_shape": correct_shape}
@@ -284,5 +323,7 @@ def track(self, input, result, meta):
         json_object = json.dumps(json_dict, indent=4)
         write_persistent_file(json_object)
 
+        create_csv(result_dataframe)
+
         # Upload run stats to s3
         upload_to_s3(json_dict)