diff --git a/datasets/dataset_generic.py b/datasets/dataset_generic.py
index 32da3967..d212c8e5 100755
--- a/datasets/dataset_generic.py
+++ b/datasets/dataset_generic.py
@@ -15,7 +15,7 @@
 from utils.utils import generate_split, nth
 
 def save_splits(split_datasets, column_keys, filename, boolean_style=False):
-	splits = [split_datasets[i].slide_data['slide_id'] for i in range(len(split_datasets))]
+	splits = [split_datasets[i].slide_data['slide_id'].astype(str) for i in range(len(split_datasets))]
 	if not boolean_style:
 		df = pd.concat(splits, ignore_index=True, axis=1)
 		df.columns = column_keys
@@ -188,7 +188,7 @@ def set_splits(self,start_from=None):
 
 	def get_split_from_df(self, all_splits, split_key='train'):
 		split = all_splits[split_key]
-		split = split.dropna().reset_index(drop=True)
+		split = split.dropna().reset_index(drop=True).astype(self.slide_data['slide_id'].dtype)
 
 		if len(split) > 0:
 			mask = self.slide_data['slide_id'].isin(split.tolist())
@@ -203,7 +203,7 @@ def get_merged_split_from_df(self, all_splits, split_keys=['train']):
 		merged_split = []
 		for split_key in split_keys:
 			split = all_splits[split_key]
-			split = split.dropna().reset_index(drop=True).tolist()
+			split = split.dropna().reset_index(drop=True).astype(self.slide_data['slide_id'].dtype).tolist()
 			merged_split.extend(split)
 
 		if len(split) > 0:
@@ -244,7 +244,8 @@ def return_splits(self, from_id=True, csv_path=None):
 		
 		else:
 			assert csv_path 
-			all_splits = pd.read_csv(csv_path, dtype=self.slide_data['slide_id'].dtype)  # Without "dtype=self.slide_data['slide_id'].dtype", read_csv() will convert all-number columns to a numerical type. Even if we convert numerical columns back to objects later, we may lose zero-padding in the process; the columns must be correctly read in from the get-go. When we compare the individual train/val/test columns to self.slide_data['slide_id'] in the get_split_from_df() method, we cannot compare objects (strings) to numbers or even to incorrectly zero-padded objects/strings. An example of this breaking is shown in https://github.com/andrew-weisman/clam_analysis/tree/main/datatype_comparison_bug-2021-12-01.
+			#all_splits = pd.read_csv(csv_path, dtype=self.slide_data['slide_id'].dtype)  # Without "dtype=self.slide_data['slide_id'].dtype", read_csv() will convert all-number columns to a numerical type. Even if we convert numerical columns back to objects later, we may lose zero-padding in the process; the columns must be correctly read in from the get-go. When we compare the individual train/val/test columns to self.slide_data['slide_id'] in the get_split_from_df() method, we cannot compare objects (strings) to numbers or even to incorrectly zero-padded objects/strings. An example of this breaking is shown in https://github.com/andrew-weisman/clam_analysis/tree/main/datatype_comparison_bug-2021-12-01.
+			all_splits = pd.read_csv(csv_path, dtype=object) 
 			train_split = self.get_split_from_df(all_splits, 'train')
 			val_split = self.get_split_from_df(all_splits, 'val')
 			test_split = self.get_split_from_df(all_splits, 'test')