OneStop

class pymovements.datasets.OneStop(name: str = 'OneStop', *, long_name: str = 'OneStop: A 360-Participant English Eye Tracking Dataset with Different Reading Regimes', mirrors: dict[str, Sequence[str]] = <factory>, resources: ResourceDefinitions = <factory>, experiment: Experiment | None = <factory>, extract: dict[str, bool] | None = None, custom_read_kwargs: dict[str, dict[str, Any]] | None = None, column_map: dict[str, str] | None = None, trial_columns: list[str] | None = None, time_column: str | None = None, time_unit: str | None = None, pixel_columns: list[str] | None = None, position_columns: list[str] | None = None, velocity_columns: list[str] | None = None, acceleration_columns: list[str] | None = None, distance_column: str | None = None, filename_format: dict[str, str] | None = None, filename_format_schema_overrides: dict[str, dict[str, type]] | None = None)

OneStop dataset [Berzak et al., 2025].

OneStop Eye Movements (in short OneStop) is an English corpus of eye movements in reading with 360 L1 participants, 2.6 million word tokens and 152 hours of eye tracking data recorded with an EyeLink 1000 Plus eye tracker. OneStop comprises four sub-corpora with eye movement recordings from paragraph reading.

To filter the data by reading regime or trial type, use the following column values:

For ordinary reading trials, set question_preview to False. For information seeking trials, set question_preview to True. To exclude repeated reading trials, set repeated_reading_trial to False. To include only repeated reading trials, set repeated_reading_trial to True. To exclude practice trials, set practice_trial to False.

For more information please consult [Berzak et al., 2025].

name

The name of the dataset.

Type:

str

long_name

The entire name of the dataset.

Type:

str

resources

A list 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.

Type:

ResourceDefinitions

filename_format

Regular expression, which will be matched before trying to load the file. Namedgroups will appear in the fileinfo dataframe.

Type:

dict[str, str] | None

filename_format_schema_overrides

If named groups are present in the filename_format, this makes it possible to cast specific named groups to a particular datatype.

Type:

dict[str, dict[str, type]] | None

custom_read_kwargs

If specified, these keyword arguments will be passed to the file reading function. (default: None)

Type:

dict[str, dict[str, Any]] | None

Examples

Initialize your Dataset object with the OneStop definition:

>>> import pymovements as pm
>>>
>>> dataset = pm.Dataset("OneStop", path='data/OneStop')

Download the dataset resources:

>>> dataset.download()

Load the data into memory:

>>> dataset.load()

Methods

__init__([name, long_name, mirrors, ...])
from_yaml(path)	Load a dataset definition from a YAML file.
to_dict(*[, exclude_private, exclude_none])	Return dictionary representation.
to_yaml(path, *[, exclude_private, exclude_none])	Save a dataset definition to a YAML file.

Attributes

acceleration_columns
column_map
custom_read_kwargs
distance_column
extract
filename_format
filename_format_schema_overrides
has_resources	Checks for resources in resources.
long_name
name
pixel_columns
position_columns
time_column
time_unit
trial_columns
velocity_columns
resources
mirrors
experiment