Annotations of low-level perceptual confounds in the research cut of the audio-visual movie "Forrest Gump" and its audio-description

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

Content

• annotation/

  Frame-wise (40 milliseconds) annotations of auditory and visual low-level confounds for each stimulus segment of the audio-description and audio-visual movie (audio-description: e.g. fg_ad_seg0_rms.tsv; movie: e.g. fg_av_ger_seg0_rms.tsv). One file of tab-separated values for every confound (providing onset, duration, and value of confound):

  • audio/*_rms.tsv: root-mean square power (a.k.a. volume)
  • audio/*_lrdiff.tsv: left-right volume difference
  • visual/*_brmean.tsv: mean brightness of a movie frame
  • visual/*_brlr.tsv: difference in brightness left minus right half of each movie frame
  • visual/*_brud.tsv: difference in brightness upper half minus lower half of each movie frame (a.k.a. "bring me that horizon")
  • visual/*_phash.tsv.: perceptual hash of each movie frame (computed by the phash function of imagehash v4.1.0)
  • visual/*_normdiff.tsv: normalized perceptual difference of each movie frame in respect to its previous movie frame

• code/

  Code to extract the information from the stimulus segments, compute the output values, and write the tab-separated values files.

• inputs/

  The segmented stimulus media files (Matroska Multimedia Container) of the audio-description and audio-visual movie as used during fMRI scanning. Not publicly accessible.

How to obtain the data files

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 upon 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/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 the 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.

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.