diff --git a/bayesflow/datasets/disk_dataset.py b/bayesflow/datasets/disk_dataset.py
index f94200dc8..d776bfe82 100644
--- a/bayesflow/datasets/disk_dataset.py
+++ b/bayesflow/datasets/disk_dataset.py
@@ -37,6 +37,7 @@ def __init__(
         adapter: Adapter | None,
         stage: str = "training",
         augmentations: Mapping[str, Callable] | Callable = None,
+        shuffle: bool = True,
         **kwargs,
     ):
         """
@@ -67,6 +68,8 @@ def __init__(
 
             Note - augmentations are applied before the adapter is called and are generally
             transforms that you only want to apply during training.
+        shuffle : bool, optional
+            Whether to shuffle the dataset at initialization and at the end of each epoch. Default is True.
         **kwargs
             Additional keyword arguments passed to the base `PyDataset`.
         """
@@ -79,8 +82,9 @@ def __init__(
         self.stage = stage
 
         self.augmentations = augmentations
-
-        self.shuffle()
+        self._shuffle = shuffle
+        if self._shuffle:
+            self.shuffle()
 
     def __getitem__(self, item) -> dict[str, np.ndarray]:
         if not 0 <= item < self.num_batches:
@@ -108,7 +112,8 @@ def __getitem__(self, item) -> dict[str, np.ndarray]:
         return batch
 
     def on_epoch_end(self):
-        self.shuffle()
+        if self._shuffle:
+            self.shuffle()
 
     @property
     def num_batches(self):
diff --git a/bayesflow/datasets/offline_dataset.py b/bayesflow/datasets/offline_dataset.py
index 075e5135b..3b91c5f22 100644
--- a/bayesflow/datasets/offline_dataset.py
+++ b/bayesflow/datasets/offline_dataset.py
@@ -24,6 +24,7 @@ def __init__(
         *,
         stage: str = "training",
         augmentations: Mapping[str, Callable] | Callable = None,
+        shuffle: bool = True,
         **kwargs,
     ):
         """
@@ -51,6 +52,8 @@ def __init__(
 
             Note - augmentations are applied before the adapter is called and are generally
             transforms that you only want to apply during training.
+        shuffle : bool, optional
+            Whether to shuffle the dataset at initialization and at the end of each epoch. Default is True.
         **kwargs
             Additional keyword arguments passed to the base `PyDataset`.
         """
@@ -69,8 +72,9 @@ def __init__(
         self.indices = np.arange(self.num_samples, dtype="int64")
 
         self.augmentations = augmentations
-
-        self.shuffle()
+        self._shuffle = shuffle
+        if self._shuffle:
+            self.shuffle()
 
     def __getitem__(self, item: int) -> dict[str, np.ndarray]:
         """
@@ -122,7 +126,8 @@ def num_batches(self) -> int | None:
         return int(np.ceil(self.num_samples / self.batch_size))
 
     def on_epoch_end(self) -> None:
-        self.shuffle()
+        if self._shuffle:
+            self.shuffle()
 
     def shuffle(self) -> None:
         """Shuffle the dataset in-place."""