diff --git a/gklearn/dataset/dataset.py b/gklearn/dataset/dataset.py
index f0f02e8..faca89b 100644
--- a/gklearn/dataset/dataset.py
+++ b/gklearn/dataset/dataset.py
@@ -117,6 +117,8 @@ class Dataset(object):
 
 		if 'extra_params' in DATASET_META[ds_name]:
 			kwargs = DATASET_META[ds_name]['extra_params']
+		else:
+			kwargs = {}
 
 		self._graphs, self._targets, label_names = DataLoader(ds_file, filename_targets=fn_targets, **kwargs).data
 
diff --git a/gklearn/dataset/file_managers.py b/gklearn/dataset/file_managers.py
index 3a6e831..9a804f5 100644
--- a/gklearn/dataset/file_managers.py
+++ b/gklearn/dataset/file_managers.py
@@ -131,22 +131,37 @@ class DataLoader():
 	def load_from_xml(self, filename, dir_dataset=None):
 		import xml.etree.ElementTree as ET
 
-		if dir_dataset is not None:
-			dir_dataset = dir_dataset
-		else:
-			dir_dataset = dirname(filename)
-		tree = ET.parse(filename)
-		root = tree.getroot()
+		def load_one_file(filename, data, y, label_names):
+			tree = ET.parse(filename)
+			root = tree.getroot()
+			for graph in root.iter('graph') if root.find('graph') is not None else root.iter('print'): # "graph" for ... I forgot; "print" for datasets GREC and Web.
+				mol_filename = graph.attrib['file']
+				mol_class = graph.attrib['class']
+				g, l_names = self.load_gxl(dir_dataset + '/' + mol_filename)
+				data.append(g)
+				self._append_label_names(label_names, l_names)
+				y.append(mol_class)
+
 		data = []
 		y = []
 		label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
-		for graph in root.iter('graph'):
-			mol_filename = graph.attrib['file']
-			mol_class = graph.attrib['class']
-			g, l_names = self.load_gxl(dir_dataset + '/' + mol_filename)
-			data.append(g)
-			self._append_label_names(label_names, l_names)
-			y.append(mol_class)
+
+		if isinstance(filename, str):
+			if dir_dataset is not None:
+				dir_dataset = dir_dataset
+			else:
+				dir_dataset = dirname(filename)
+			load_one_file(filename, data, y, label_names)
+
+
+		else: # filename is a list of files.
+			if dir_dataset is not None:
+				dir_dataset = dir_dataset
+			else:
+				dir_dataset = dirname(filename[0])
+
+			for fn in filename:
+				load_one_file(fn, data, y, label_names)
 
 		return data, y, label_names
 
@@ -505,32 +520,45 @@ class DataLoader():
 		for node in root.iter('node'):
 			dic[node.attrib['id']] = index
 			labels = {}
-			for attr in node.iter('attr'):
+			for attr in node.iter('attr'): # for datasets "GREC" and "Monoterpens".
 				labels[attr.attrib['name']] = attr[0].text
+			for attr in node.iter('attribute'): # for dataset "Web".
+				labels[attr.attrib['name']] = attr.attrib['value']
 			g.add_node(index, **labels)
 			index += 1
 
 		for edge in root.iter('edge'):
 			labels = {}
-			for attr in edge.iter('attr'):
+			for attr in edge.iter('attr'): # for datasets "GREC" and "Monoterpens".
 				labels[attr.attrib['name']] = attr[0].text
+			for attr in edge.iter('attribute'): # for dataset "Web".
+				labels[attr.attrib['name']] = attr.attrib['value']
 			g.add_edge(dic[edge.attrib['from']], dic[edge.attrib['to']], **labels)
 
 		# get label names.
 		label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
+		# @todo: possible loss of label names if some nodes miss some labels.
 		for node in root.iter('node'):
-			for attr in node.iter('attr'):
-				if attr[0].tag == 'int': # @todo: this maybe wrong, and slow.
+			for attr in node.iter('attr'): # for datasets "GREC" and "Monoterpens".
+				if attr[0].tag == 'int' or attr.attrib['name'] == 'type': # @todo: this maybe wrong, and slow. "type" is for dataset GREC; "int" is for dataset "Monoterpens".
 					label_names['node_labels'].append(attr.attrib['name'])
 				else:
 					label_names['node_attrs'].append(attr.attrib['name'])
+
+			for attr in node.iter('attribute'): # for dataset "Web".
+				label_names['node_attrs'].append(attr.attrib['name'])
+				# @todo: is id useful in dataset "Web"? is "FREQUENCY" symbolic or not?
 			break
+
 		for edge in root.iter('edge'):
-			for attr in edge.iter('attr'):
-				if attr[0].tag == 'int': # @todo: this maybe wrong, and slow.
+			for attr in edge.iter('attr'): # for datasets "GREC" and "Monoterpens".
+				if attr[0].tag == 'int' or attr.attrib['name'] == 'frequency' or 'type' in attr.attrib['name']: # @todo: this maybe wrong, and slow. "frequency" and "type" are for dataset GREC; "int" is for dataset "Monoterpens".
 					label_names['edge_labels'].append(attr.attrib['name'])
 				else:
 					label_names['edge_attrs'].append(attr.attrib['name'])
+
+			for attr in edge.iter('attribute'): # for dataset "Web".
+				label_names['edge_attrs'].append(attr.attrib['name'])
 			break
 
 		return g, label_names
@@ -546,7 +574,10 @@ class DataLoader():
 		tree = ET.parse(filename)
 		root = tree.getroot()
 		index = 0
-		g_id = root.find(xmlns + 'molecule').attrib['id']
+		if root.tag == xmlns + 'molecule':
+			g_id = root.attrib['id']
+		else:
+			g_id = root.find(xmlns + 'molecule').attrib['id']
 		g = nx.Graph(filename=basename(filename), name=g_id)
 		dic = {}  # used to retrieve incident nodes of edges
 		for atom in root.iter(xmlns + 'atom'):
@@ -561,13 +592,14 @@ class DataLoader():
 		for bond in root.iter(xmlns + 'bond'):
 			labels = {}
 			for key, val in bond.attrib.items():
-				if key != 'atomRefs2':
+				if key != 'atomRefs2' and key != 'id': # "id" is in dataset "ACE".
 					labels[key] = val
 			n1, n2 = bond.attrib['atomRefs2'].strip().split(' ')
 			g.add_edge(dic[n1], dic[n2], **labels)
 
 		# get label names.
 		label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
+		# @todo: possible loss of label names if some nodes miss some labels.
 		for key, val in g.nodes[0].items():
 			try:
 				float(val)
diff --git a/gklearn/dataset/metadata.py b/gklearn/dataset/metadata.py
index ed3af41..27ccf3b 100644
--- a/gklearn/dataset/metadata.py
+++ b/gklearn/dataset/metadata.py
@@ -206,7 +206,8 @@ GREYC_META = {
 		'domain': 'small molecules',
 		'train_valid_test': ['trainset_0.ds', None, 'testset_0.ds'],
 		'stereoisomerism': False,
-		'load_files': [],
+		'load_files': ['dataWithOutsider.ds'],
+		'extra_params': {'gformat': 'cml'}
 	},
 	'Vitamin_D': {
 		'database': 'greyc',
@@ -250,7 +251,7 @@ IAM_META = {
 		'url': 'https://iapr-tc15.greyc.fr/IAM/GREC.zip',
 		'domain': None,
 		'train_valid_test': ['data/test.cxl','data/train.cxl', 'data/valid.cxl'],
-		'load_files': [],
+		'load_files': [['data/test.cxl','data/train.cxl', 'data/valid.cxl']],
 	},
 	'Web': {
 		'database': 'iam',
@@ -268,7 +269,7 @@ IAM_META = {
 		'url': 'https://iapr-tc15.greyc.fr/IAM/Web.zip',
 		'domain': None,
 		'train_valid_test': ['data/test.cxl', 'data/train.cxl', 'data/valid.cxl'],
-		'load_files': [],
+		'load_files': [['data/test.cxl','data/train.cxl', 'data/valid.cxl']],
 	},
 }