diff --git a/MANIFEST.in b/MANIFEST.in
index 3576c69a..ae40a204 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,5 +1,5 @@
 include LICENSE.txt
 include README.md
 include requirements.txt
-include bdi-kit/resource/*.json
-include bdi-kit/resource/*.csv
\ No newline at end of file
+include bdikit/resource/*.json
+include bdikit/resource/*.csv
\ No newline at end of file
diff --git a/README.md b/README.md
index 071ebd85..80a9c89b 100644
--- a/README.md
+++ b/README.md
@@ -19,8 +19,6 @@ To install the latest development version:
 pip install git+https://github.com/VIDA-NYU/bdi-kit@devel
 ```
 
-Download the pre-trained model for mapping recommendations from [here](https://drive.google.com/file/d/1YdCTd-kUMjDJaltQwXN4X9ezTCsfjyft/view) (we show how to use it in our examples).
-
 
 ## Documentation
 See our examples [here](https://github.com/VIDA-NYU/bdi-kit/tree/devel/examples).
diff --git a/bdikit/api.py b/bdikit/api.py
index 61cc0509..fdd31590 100644
--- a/bdikit/api.py
+++ b/bdikit/api.py
@@ -43,7 +43,7 @@ def load_dataset(self, dataset_path):
     def reduce_scope(self):
         self.scope_manager = ScopeReducingManager(self.dataset, self.global_table)
         self.reduced_scope = self.scope_manager.reduce()
-        return plot_reduce_scope(self.reduced_scope, self.dataset)
+        plot_reduce_scope(self.reduced_scope, self.dataset)
 
     def map_columns(self, algorithm='SimFloodAlgorithm'):
         self.column_manager = ColumnMappingManager(self.dataset, self.global_table, algorithm)
diff --git a/bdikit/visualization/mappings.py b/bdikit/visualization/mappings.py
index e5a56163..a3dd5178 100644
--- a/bdikit/visualization/mappings.py
+++ b/bdikit/visualization/mappings.py
@@ -7,13 +7,12 @@
 
 def plot_reduce_scope(reduced_scope, dataset):
     scope_explorer = ScopeReducerExplorer(dataset, reduced_scope)
-    return scope_explorer.explore()
+    display(scope_explorer.explore())
 
 def plot_column_mappings(column_mappings):
     column_mappings_df = pd.DataFrame(column_mappings.items(), columns=['Original Column', 'Target Column'])
     display(column_mappings_df)
 
-
 def plot_value_mappings(value_mappings, include_unmatches=True):
     sorted_results = sorted(value_mappings.items(), key=lambda x: x[1]['coverage'], reverse=True)