diff --git a/tests/data/test_dataset.py b/tests/data/test_dataset.py
index a589bd3a..1e8feef3 100644
--- a/tests/data/test_dataset.py
+++ b/tests/data/test_dataset.py
@@ -1,6 +1,11 @@
+import pytest
+from typing import Tuple, List
 import torch
+from random import randint
 from torch.utils.data import DataLoader
 from continuiti.data.selfsupervised import SelfSupervisedOperatorDataset
+from continuiti.data import MaskedOperatorDataset
+from continuiti.transforms import Normalize
 
 
 def test_dataset():
@@ -32,3 +37,150 @@ def test_dataset():
         assert y.shape[1] == coordinate_dim
         assert v.shape[1] == num_channels
         assert (v == f(y)).all()
+
+
+class TestMaskedDataset:
+    observations = 19
+    batch_size = 2
+    x_dim = 3
+    u_dim = 5
+    y_dim = 7
+    v_dim = 11
+    max_sensors = 13
+    max_evaluations = 17
+
+    @pytest.fixture
+    def random_masked_tensors(
+        self,
+    ) -> Tuple[
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+    ]:
+        x = torch.rand(self.observations, self.x_dim, self.max_sensors)
+        u = torch.rand(self.observations, self.u_dim, self.max_sensors)
+        ipt_mask = torch.rand(self.observations, self.max_sensors) > 0.25
+
+        y = torch.rand(self.observations, self.y_dim, self.max_evaluations)
+        v = torch.rand(self.observations, self.v_dim, self.max_evaluations)
+        opt_mask = torch.rand(self.observations, self.max_evaluations) > 0.25
+
+        return x, u, ipt_mask, y, v, opt_mask
+
+    @pytest.fixture
+    def random_lists(
+        self,
+    ) -> Tuple[
+        List[torch.Tensor], List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]
+    ]:
+        n_sensors = [randint(1, self.max_sensors) for _ in range(self.observations)]
+        x = [torch.rand(self.x_dim, n) for n in n_sensors]
+        u = [torch.rand(self.u_dim, n) for n in n_sensors]
+
+        n_observations = [
+            randint(1, self.max_evaluations) for _ in range(self.observations)
+        ]
+        y = [torch.rand(self.y_dim, n) for n in n_observations]
+        v = [torch.rand(self.v_dim, n) for n in n_observations]
+
+        return x, u, y, v
+
+    @pytest.fixture
+    def random_masked_dataset(self, random_masked_tensors):
+        x, u, ipt_mask, y, v, opt_mask = random_masked_tensors
+        return MaskedOperatorDataset(
+            x=x, u=u, y=y, v=v, ipt_mask=ipt_mask, opt_mask=opt_mask
+        )
+
+    def test_can_initialize_tensor(self, random_masked_tensors):
+        x, u, ipt_mask, y, v, opt_mask = random_masked_tensors
+        dataset = MaskedOperatorDataset(
+            x=x, u=u, y=y, v=v, ipt_mask=ipt_mask, opt_mask=opt_mask
+        )
+        assert isinstance(dataset, MaskedOperatorDataset)
+
+    def test_can_initalize_list(self, random_lists):
+        x, u, y, v = random_lists
+        dataset = MaskedOperatorDataset(x=x, u=u, y=y, v=v)
+        assert isinstance(dataset, MaskedOperatorDataset)
+
+    def test_dataset_length(self, random_masked_dataset):
+        assert len(random_masked_dataset) == self.observations
+
+    def test_masking(self, random_masked_dataset, random_masked_tensors):
+        _, _, ipt_mask, _, _, opt_mask = random_masked_tensors
+
+        for (
+            (
+                _,
+                _,
+                _,
+                _,
+                ipt_mask_i,
+                opt_mask_i,
+            ),
+            ipt_mask_gt,
+            opt_mask_gt,
+        ) in zip(iter(random_masked_dataset), ipt_mask, opt_mask):
+            assert torch.all(ipt_mask_i == ipt_mask_gt)
+            assert torch.all(opt_mask_i == opt_mask_gt)
+
+    def test_unmasked_samples(self, random_masked_dataset):
+        for x, u, y, v, _, _ in random_masked_dataset:
+            assert not torch.allclose(x, torch.zeros(x.shape))
+            assert not torch.allclose(u, torch.zeros(u.shape))
+            assert not torch.allclose(y, torch.zeros(y.shape))
+            assert not torch.allclose(v, torch.zeros(v.shape))
+
+    def test_batch_masking(self, random_masked_dataset):
+        dataloader = DataLoader(
+            random_masked_dataset,
+            shuffle=True,
+            batch_size=self.batch_size,
+            drop_last=True,
+        )
+
+        for x, u, y, v, ipt_mask, opt_mask in dataloader:
+            assert (
+                len(x)
+                == len(u)
+                == len(y)
+                == len(v)
+                == len(ipt_mask)
+                == len(opt_mask)
+                == self.batch_size
+            )
+
+    def test_masked_and_transform(self, random_masked_tensors):
+        x, u, ipt_mask, y, v, opt_mask = random_masked_tensors
+        dataset = MaskedOperatorDataset(
+            x=x,
+            u=u,
+            y=y,
+            v=v,
+            ipt_mask=ipt_mask,
+            opt_mask=opt_mask,
+            x_transform=Normalize(
+                torch.ones(self.x_dim, 1) / 2.0,
+                torch.ones(self.x_dim, 1) * torch.sqrt(torch.tensor([1 / 12])),
+            ),
+            u_transform=Normalize(
+                torch.ones(self.u_dim, 1) / 2.0,
+                torch.ones(self.u_dim, 1) * torch.sqrt(torch.tensor([1 / 12])),
+            ),
+            y_transform=Normalize(
+                torch.ones(self.y_dim, 1) / 2.0,
+                torch.ones(self.y_dim, 1) * torch.sqrt(torch.tensor([1 / 12])),
+            ),
+            v_transform=Normalize(
+                torch.ones(self.v_dim, 1) / 2.0,
+                torch.ones(self.v_dim, 1) * torch.sqrt(torch.tensor([1 / 12])),
+            ),
+        )
+        dataloader = DataLoader(dataset, batch_size=self.batch_size)
+
+        for x, u, y, v, ipt_mask, opt_mask in dataloader:
+            assert True