A studyforrest.org dataset extension

Eye movement events for the Forrest Gump movie

Two groups of participants (each n=15) watched this movie. One in a lab setup, another one in a MRI scanner. The original data are described in Hanke et al. (2016, http://www.nature.com/articles/sdata201692). This dataset contains eye movements results of fixations, saccades, post-saccadic oscillations, and pursuit events. Details of the detection procedure are available in:

Asim H. Dar, Adina S. Wagner & Michael Hanke (2019). REMoDNaV: Robust Eye Movement Detection for Natural Viewing

For more information about the project visit: http://studyforrest.org

Dataset content

For each participant and recording run in the original dataset, two files are provided in this dataset:

• sub-??_task-movie_run-?_events.tsv
• sub-??_task-movie_run-?_events.png

The TSV files are BIDS-compliant event (text) files that contain one detected eye movement event per line. For each event the following properties are given (in columns):

• onset: start time of an even, relative to the start of the recording (in seconds)
• duration: duration of an event (in seconds)
• label: event type label, known labels are:
  • FIXA: fixation
  • PURS: pursuit
  • SACC/ISAC: saccade
  • LPSO/ILPS: low-velocity post-saccadic oscillation
  • HPSO/IHPS: high-velocity post-saccadic oscillation
• start_x, start_y: the gaze coordinate at the start of an event (in pixels)
• end_x, end_y: the gaze coordinate at the end of an event (in pixels)
• amp: movement amplitude of an event (in degrees)
• peak_vel: peak velocity of an event (in degrees/second)
• med_vel: median velocity of an event (in degrees/second)
• avg_vel: mean peak velocity of an event (in degrees/second)

The PNG files contain a visualization of the detected events together with the gaze coordinate time series, for visual quality control. The algorithm parameters are also rendered into the picture.

How to obtain the dataset

This repository is a DataLad dataset. It provides fine-grained data access down to the level of individual files, and allows for tracking future updates. In order to use this repository for data retrieval, DataLad is required. It is a free and open source command line tool, available for all major operating systems, and builds up on Git and git-annex to allow sharing, synchronizing, and version controlling collections of large files. You can find information on how to install DataLad at handbook.datalad.org/en/latest/intro/installation.html.

Get the dataset

A DataLad dataset can be cloned by running

datalad clone <url>

Once a dataset is cloned, it is a light-weight directory on your local machine. At this point, it contains only small metadata and information on the identity of the files in the dataset, but not actual content of the (sometimes large) data files.

Retrieve dataset content

After cloning a dataset, you can retrieve file contents by running

datalad get <path/to/directory/or/file>

This command will trigger a download of the files, directories, or subdatasets you have specified.

DataLad datasets can contain other datasets, so called subdatasets. If you clone the top-level dataset, subdatasets do not yet contain metadata and information on the identity of files, but appear to be empty directories. In order to retrieve file availability metadata in subdatasets, run

datalad get -n <path/to/subdataset>

Afterwards, you can browse the retrieved metadata to find out about subdataset contents, and retrieve individual files with datalad get. If you use datalad get <path/to/subdataset>, all contents of the subdataset will be downloaded at once.

Stay up-to-date

DataLad datasets can be updated. The command datalad update will fetch updates and store them on a different branch (by default remotes/origin/master). Running

datalad update --merge

will pull available updates and integrate them in one go.

Find out what has been done

DataLad datasets contain their history in the git log. By running git log (or a tool that displays Git history) in the dataset or on specific files, you can find out what has been done to the dataset or to individual files by whom, and when.

More information

More information on DataLad and how to use it can be found in the DataLad Handbook at handbook.datalad.org. The chapter "DataLad datasets" can help you to familiarize yourself with the concept of a dataset.