diff --git a/poc/datasets/variable_choice_datasets.py b/poc/datasets/variable_choice_datasets.py
index 24438a06..0830821f 100644
--- a/poc/datasets/variable_choice_datasets.py
+++ b/poc/datasets/variable_choice_datasets.py
@@ -87,7 +87,6 @@ def __init__(
         )
         super().__init__(torch.tensor(x).float(), torch.tensor(y_true).float())
 
-    # TODO (next) adapt this, write some tests and understand it
     def make_globular_pareto_choices(
         self,
         n_instances: int,
@@ -160,17 +159,19 @@ def make_randn_pareto_choices(
             cluster_size: int,
             n_features: int,
             n_objects: int,
-            center: float,
+            center: np.array,
         ):
             """Generate random objects from a d-dimensional isometric normal distribution.
 
             This should be the easiest possible Pareto-problem, since the model can learn
             a latent-utility which scores how likely a point is on the front (independent
             of the other points)."""
+            # Generate a single cluster, uniformly at random.
             X = self.random_state.randn(cluster_size, n_objects, n_features)
             Y = np.empty((cluster_size, n_objects), dtype=bool)
             for i in range(cluster_size):
                 Y[i] = pareto_front(X[i])
+            # Return the shifted cluster with its Pareto front.
             return X + center, Y
 
         X = np.empty((n_instances, n_objects, n_features))
@@ -179,12 +180,17 @@ def make_randn_pareto_choices(
             center = sample_unit_ball(
                 n_inst=1, n_features=n_features, radius=cluster_spread
             )
+            # Cluster of points sampled uniformly at random and shifted to
+            # center. Center is some point on the unit ball (cluster_spread).
             x, y = make_randn_pareto_choices(
                 cluster_size=cluster_size,
                 n_features=n_features,
                 n_objects=n_objects,
                 center=center,
             )
+            # TODO(next) how do clusters and instances relate? Is each cluster
+            # one instance? Then why the n_instances / cluster_size in the for
+            # loop?
             X[i * cluster_size : (i + 1) * cluster_size] = x
             Y[i * cluster_size : (i + 1) * cluster_size] = y
         return X, Y