diff --git a/boom/data/prepare_splits_10k.py b/boom/data/prepare_splits_10k.py
index 7d61acf..80fa013 100644
--- a/boom/data/prepare_splits_10k.py
+++ b/boom/data/prepare_splits_10k.py
@@ -66,10 +66,18 @@ def prepare_splits(
     # Extract the hof values
     hof_values = np.array(hof_values).reshape(-1, 1)
 
-    density_KDE = KernelDensity(kernel="gaussian", bandwidth="scott").fit(
+    # sklearn's "scott" assumes sigma=1, so we compute it manually:
+    # h = n^(-1/(d+4)) * sigma
+    
+    n_samples_density, n_features_density = density_values.shape
+    scott_bw_density = n_samples_density ** (-1.0 / (n_features_density + 4)) * np.std(density_values)
+    density_KDE = KernelDensity(kernel="gaussian", bandwidth=scott_bw_density).fit(
         density_values
     )
-    hof_KDE = KernelDensity(kernel="gaussian", bandwidth="scott").fit(hof_values)
+
+    n_samples_hof, n_features_hof = hof_values.shape
+    scott_bw_hof = n_samples_hof ** (-1.0 / (n_features_hof + 4)) * np.std(hof_values)
+    hof_KDE = KernelDensity(kernel="gaussian", bandwidth=scott_bw_hof).fit(hof_values)
 
     density_kde_scores = density_KDE.score_samples(density_values)
     hof_kde_scores = hof_KDE.score_samples(hof_values)
diff --git a/boom/data/prepare_splits_qm9.py b/boom/data/prepare_splits_qm9.py
index 8c1465f..e6c8a0a 100644
--- a/boom/data/prepare_splits_qm9.py
+++ b/boom/data/prepare_splits_qm9.py
@@ -168,7 +168,12 @@ def prepare_splits_qm9(
         property_values = np.array(property_values).reshape(-1, 1).astype(np.float64)
 
         print("Starting Kernel Density Estimation for " + property_name)
-        property_KDE = KernelDensity(kernel="gaussian", bandwidth="scott").fit(
+        # sklearn's "scott" assumes sigma=1, so we compute it manually:
+        # h = n^(-1/(d+4)) * sigma
+
+        n_samples, n_features = property_values.shape
+        scott_bandwidth = n_samples ** (-1.0 / (n_features + 4)) * np.std(property_values)
+        property_KDE = KernelDensity(kernel="gaussian", bandwidth=scott_bandwidth).fit(
             property_values
         )
         print("Kernel Density Estimation Done!")