diff --git a/src/pymovements/datasets/gaze_on_faces.py b/src/pymovements/datasets/gaze_on_faces.py
index 45722a86..131549b8 100644
--- a/src/pymovements/datasets/gaze_on_faces.py
+++ b/src/pymovements/datasets/gaze_on_faces.py
@@ -48,33 +48,62 @@ class GazeOnFaces(DatasetDefinition):
 
     Attributes
     ----------
-    name : str
+    name: str
         The name of the dataset.
 
-    mirrors : dict[str, tuple[str, ...]]
+    has_files: dict[str, bool]
+        Indicate whether the dataset contains 'gaze', 'precomputed_events', and
+        'precomputed_reading_measures'.
+
+    mirrors: dict[str, tuple[str, ...]]
         A tuple of mirrors of the dataset. Each entry must be of type `str` and end with a '/'.
 
-    resources : dict[str, tuple[dict[str, str], ...]]
-        A tuple of dataset resources. Each list entry must be a dictionary with the following keys:
+    resources: dict[str, tuple[dict[str, str], ...]]
+        A tuple of dataset gaze_resources. Each list entry must be a dictionary with the following
+        keys:
         - `resource`: The url suffix of the resource. This will be concatenated with the mirror.
         - `filename`: The filename under which the file is saved as.
         - `md5`: The MD5 checksum of the respective file.
 
-    experiment : Experiment
+    extract: dict[str, bool]
+        Decide whether to extract the data.
+
+    experiment: Experiment
         The experiment definition.
 
-    filename_format : dict[str, str]
+    filename_format: dict[str, str]
         Regular expression which will be matched before trying to load the file. Namedgroups will
         appear in the `fileinfo` dataframe.
 
-    filename_format_dtypes : dict[str, dict[str, type]]
+    filename_format_dtypes: dict[str, dict[str, type]]
         If named groups are present in the `filename_format`, this makes it possible to cast
         specific named groups to a particular datatype.
 
-    column_map : dict[str, str]
+    trial_columns: list[str]
+            The name of the trial columns in the input data frame. If the list is empty or None,
+            the input data frame is assumed to contain only one trial. If the list is not empty,
+            the input data frame is assumed to contain multiple trials and the transformation
+            methods will be applied to each trial separately.
+
+    time_column: Any
+        The name of the timestamp column in the input data frame. This column will be renamed to
+        ``time``.
+
+    time_unit: Any
+        The unit of the timestamps in the timestamp column in the input data frame. Supported
+        units are 's' for seconds, 'ms' for milliseconds and 'step' for steps. If the unit is
+        'step' the experiment definition must be specified. All timestamps will be converted to
+        milliseconds.
+
+    pixel_columns: list[str]
+        The name of the pixel position columns in the input data frame. These columns will be
+        nested into the column ``pixel``. If the list is empty or None, the nested ``pixel``
+        column will not be created.
+
+    column_map: dict[str, str]
         The keys are the columns to read, the values are the names to which they should be renamed.
 
-    custom_read_kwargs : dict[str, dict[str, Any]]
+    custom_read_kwargs: dict[str, dict[str, Any]]
         If specified, these keyword arguments will be passed to the file reading function.
 
     Examples