diff --git a/pina/data/data_module.py b/pina/data/data_module.py
index e8bf702..f5f7d98 100644
--- a/pina/data/data_module.py
+++ b/pina/data/data_module.py
@@ -176,7 +176,7 @@ class Collator:
 
     def _collate_graph_dataset(self, data_list):
         """
-        Function used to collate the data when the dataset is a
+        Function used to collate data when the dataset is a
         :class:`~pina.data.dataset.PinaGraphDataset`.
 
         :param data_list: Elememts to be collated.
@@ -187,7 +187,6 @@ class Collator:
         :raises RuntimeError: If the data is not a
             :class:`~torch_geometric.data.Data` or a :class:`~pina.graph.Graph`.
         """
-
         if isinstance(data_list[0], LabelTensor):
             return LabelTensor.cat(data_list)
         if isinstance(data_list[0], torch.Tensor):
@@ -201,14 +200,13 @@ class Collator:
 
     def __call__(self, batch):
         """
-        Perform the collation of the data points fetched from the dataset.
-        The behavoior of the function is set based on the batching strategy
-        during class initialization.
+        Perform the collation of data fetched from the dataset. The behavoior
+        of the function is set based on the batching strategy during class
+        initialization.
 
         :param batch: List of retrieved data or sampled indices.
         :type batch: list[int] | list[dict]
-        :return: Dictionary containing the data points fetched from the dataset,
-            collated.
+        :return: Dictionary containing colleted data fetched from the dataset.
         :rtype: dict
         """
 
@@ -223,12 +221,10 @@ class PinaSampler:
 
     def __new__(cls, dataset, shuffle):
         """
-        Instantiate the sampler based on the environment in which the code is
-        running.
+        Instantiate and initialize the sampler.
 
-        :param PinaDataset dataset: The dataset to be sampled.
-        :param bool shuffle: whether to shuffle the dataset or not before
-            sampling.
+        :param PinaDataset dataset: The dataset from which to sample.
+        :param bool shuffle: Whether to shuffle the dataset.
         :return: The sampler instance.
         :rtype: torch.utils.data.Sampler
         """
@@ -267,18 +263,18 @@ class PinaDataModule(LightningDataModule):
         pin_memory=False,
     ):
         """
-        Initialize the object, creating datasets based on the input problem.
+        Initialize the object and creating datasets based on the input problem.
 
         :param AbstractProblem problem: The problem containing the data on which
             to create the datasets and dataloaders.
-        :param float train_size: Fraction or number of elements in the training
-            split. It must be in the range [0, 1].
-        :param float test_size: Fraction or number of elements in the test
-            split. It must be in the range [0, 1].
-        :param float val_size: Fraction or number of elements in the validation
-            split. It must be in the range [0, 1].
+        :param float train_size: Fraction of elements in the training split. It
+        must be in the range [0, 1].
+        :param float test_size: Fraction of elements in the test split. It must
+        be in the range [0, 1].
+        :param float val_size: Fraction of elements in the validation split. It
+        must be in the range [0, 1].
         :param batch_size: The batch size used for training. If `None`, the
-            entire dataset is used per batch.
+            entire dataset is returned in a single batch.
         :type batch_size: int | None
         :param bool shuffle: Whether to shuffle the dataset before splitting.
             Default True.
@@ -289,7 +285,7 @@ class PinaDataModule(LightningDataModule):
         :param int num_workers: Number of worker threads for data loading.
             Default 0 (serial loading).
         :param bool pin_memory: Whether to use pinned memory for faster data
-            transfer to GPU. (Default False).
+            transfer to GPU. Default False.
 
         :raises ValueError: If at least one of the splits is negative.
         :raises ValueError: If the sum of the splits is different from 1.
@@ -370,7 +366,7 @@ class PinaDataModule(LightningDataModule):
         If the stage is "fit", the training and validation datasets are created.
         If the stage is "test", the testing dataset is created.
 
-        :param str stage: The stage for which to perform the splitting.
+        :param str stage: The stage for which to perform the dataset setup.
 
         :raises ValueError: If the stage is neither "fit" nor "test".
         """
@@ -534,10 +530,10 @@ class PinaDataModule(LightningDataModule):
 
     def find_max_conditions_lengths(self, split):
         """
-        Define the maximum length of the conditions.
+        Define the maximum length for each conditions.
 
-        :param dict split:  The splits of the dataset.
-        :return: The maximum length of the conditions.
+        :param dict split: The split of the dataset.
+        :return: The maximum length per condition.
         :rtype: dict
         """
 
diff --git a/pina/data/dataset.py b/pina/data/dataset.py
index 9b7ff87..0e24d22 100644
--- a/pina/data/dataset.py
+++ b/pina/data/dataset.py
@@ -75,7 +75,7 @@ class PinaDatasetFactory:
 class PinaDataset(Dataset, ABC):
     """
     Abstract class for the PINA dataset. It defines the common interface for
-    the :class:`~pina.data.dataset.PinaTensorDataset` and
+    :class:`~pina.data.dataset.PinaTensorDataset` and
     :class:`~pina.data.dataset.PinaGraphDataset` classes.
     """
 
@@ -83,9 +83,8 @@ class PinaDataset(Dataset, ABC):
         self, conditions_dict, max_conditions_lengths, automatic_batching
     ):
         """
-        Initialize a :class:`~pina.data.dataset.PinaDataset` instance by storing
-        the providedconditions dictionary, the maximum number of conditions to
-        consider, and the automatic batching flag.
+        Initialize :class:`~pina.data.dataset.PinaDataset` instance by storing
+        the provided conditions dictionary, and the automatic batching flag.
 
         :param dict conditions_dict: Dictionary containing the conditions with
             data.