diff --git a/src/hdf5_ops.py b/src/hdf5_ops.py
index 3dccd37..4943cde 100644
--- a/src/hdf5_ops.py
+++ b/src/hdf5_ops.py
@@ -31,20 +31,16 @@ class HDF5DataOpsManager():
     """
     def __init__(self, file_path, mode = 'r+') -> None:
 
+        # Class attributes
         if mode in ['r','r+']:
             self.mode = mode
         self.file_path = file_path        
         self.file_obj = None
         #self._open_file()
-        self.list_of_datasets = []        
+        self.dataset_metadata_df = None        
 
     # Define private methods   
 
-
-    def _collect_dataset_names(self, name, obj, list_of_datasets):
-        if isinstance(obj, h5py.Dataset):
-            list_of_datasets.append(name)
-
     # Define public methods
 
     def open_file(self): 
@@ -56,16 +52,23 @@ class HDF5DataOpsManager():
             self.file_obj.flush()  # Ensure all data is written to disk
             self.file_obj.close()
             self.file_obj = None
+  
+    def load_dataset_metadata(self):
 
-    def retrieve_dataframe_of_dataset_names(self):
+        def __get_datasets(name, obj, list_of_datasets):
+            if isinstance(obj,h5py.Dataset):
+                list_of_datasets.append(name)  
+                #print(f'Adding dataset: {name}') #tail: {head} head: {tail}')
         list_of_datasets = []
-        self.file_obj.visititems(lambda name, obj: self._collect_dataset_names(name, obj, list_of_datasets))
+        with h5py.File(self.file_path,'r') as file:
+            list_of_datasets = []
+            file.visititems(lambda name, obj: __get_datasets(name, obj, list_of_datasets))
 
-        dataset_df = pd.DataFrame({'dataset_name': list_of_datasets})
-        dataset_df['parent_instrument'] = dataset_df['dataset_name'].apply(lambda x: x.split('/')[-3])
-        dataset_df['parent_file'] = dataset_df['dataset_name'].apply(lambda x: x.split('/')[-2])
+        dataset_metadata_df = pd.DataFrame({'dataset_name': list_of_datasets})
+        dataset_metadata_df['parent_instrument'] = dataset_metadata_df['dataset_name'].apply(lambda x: x.split('/')[-3])
+        dataset_metadata_df['parent_file'] = dataset_metadata_df['dataset_name'].apply(lambda x: x.split('/')[-2])
 
-        return dataset_df
+        self.dataset_metadata_df = dataset_metadata_df
     
     def read_dataset_as_dataframe(self,dataset_name):
         """ 
@@ -371,25 +374,6 @@ def read_dataset_from_hdf5file(hdf5_file_path, dataset_path):
         #metadata_df = pd.DataFrame.from_dict(data, orient='columns')        
     return df
 
-def list_datasets_in_hdf5file(hdf5_file_path):
-
-    def get_datasets(name, obj, list_of_datasets):
-        if isinstance(obj,h5py.Dataset):
-            list_of_datasets.append(name)  
-            #print(f'Adding dataset: {name}') #tail: {head} head: {tail}')
-
-
-    with h5py.File(hdf5_file_path,'r') as file:
-        list_of_datasets = []
-        file.visititems(lambda name, obj: get_datasets(name, obj, list_of_datasets))
-
-    dataset_df = pd.DataFrame({'dataset_name':list_of_datasets})
-
-    dataset_df['parent_instrument'] = dataset_df['dataset_name'].apply(lambda x: x.split('/')[-3])
-    dataset_df['parent_file'] = dataset_df['dataset_name'].apply(lambda x: x.split('/')[-2])
-
-    return dataset_df
-
 def get_parent_child_relationships(file: h5py.File):
 
     nodes = ['/']
@@ -423,29 +407,6 @@ def get_parent_child_relationships(file: h5py.File):
     return nodes, parent, values 
 
 
-
-def construct_attributes_dict(attrs_obj):
-
-    attr_dict = {}
-    for key, value in attrs_obj.items():
-        attr_dict[key] = {}
-        if not key in ['file_list','filtered_file_list']:
-            
-            if utils.is_structured_array(value):
-               #for subattr in value.dtype.names:
-                   #attr_dict[key][subattr] = make_dtype_yaml_compatible(value[subattr])  
-                attr_dict[key] = utils.to_serializable_dtype(value)             
-            else:
-                attr_dict[key] = utils.to_serializable_dtype(value) # {"rename_as" : key,
-                              #"value" : utils.to_serializable_dtype(value)
-                             #}
-
-            #if isinstance(value,str):
-            #    value.replace('\\','\\\\')
-
-
-    return attr_dict
-
 def __print_metadata__(name, obj, folder_depth, yaml_dict): 
 
     # TODO: should we enable deeper folders ?
@@ -459,7 +420,8 @@ def __print_metadata__(name, obj, folder_depth, yaml_dict):
             #attr_dict = {}
             group_dict = {}
 
-            attr_dict = construct_attributes_dict(obj.attrs)
+            # Convert attribute dict to a YAML/JSON serializable dict
+            attr_dict = {key: utils.to_serializable_dtype(val) for key, val in obj.attrs.items()}
 
             #for key, value in obj.attrs.items():
                 #print (key, value.dtype)
@@ -482,9 +444,11 @@ def __print_metadata__(name, obj, folder_depth, yaml_dict):
             #print(name)
 
             yaml_dict[obj.name] = group_dict
-        elif isinstance(obj, h5py.Dataset):            
+        elif isinstance(obj, h5py.Dataset):   
+            # Convert attribute dict to a YAML/JSON serializable dict
+            attr_dict = {key: utils.to_serializable_dtype(val) for key, val in obj.attrs.items()}         
             parent_name = '/'.join(name_to_list[:-1])
-            yaml_dict[parent_name]["datasets"][name_head] = {"rename_as": name_head ,"attributes": construct_attributes_dict(obj.attrs)}
+            yaml_dict[parent_name]["datasets"][name_head] = {"rename_as": name_head ,"attributes": attr_dict}
             #print(yaml.dump(group_dict,sort_keys=False))
 
         #elif len(obj.name.split('/')) == 3:
@@ -522,8 +486,8 @@ def serialize_metadata(input_filename_path, folder_depth: int = 4, output_format
     
     # Open the HDF5 file and extract metadata
     with h5py.File(input_filename_path, 'r') as f:
-        # Construct attributes dictionary and top-level structure
-        attrs_dict = construct_attributes_dict(f.attrs)
+        # Convert attribute dict to a YAML/JSON serializable dict
+        attrs_dict = {key: utils.to_serializable_dtype(val) for key, val in f.attrs.items()} 
         yaml_dict[f.name] = {
             "name": f.name,
             "attributes": attrs_dict,