This is an extension of the studyforrest project, all participants previously volunteered for the audio-only Forrest Gump study. The datset is structured in BIDS format, details of the files and metadata can be found at:
Ayan Sengupta, Falko R. Kaule, J. Swaroop Guntupalli, Michael B. Hoffmann, Christian Häusler, Jörg Stadler, Michael Hanke. An extension of the studyforrest dataset for vision research. (submitted for publication)
Michael Hanke, Nico Adelhöfer, Daniel Kottke, Vittorio Iacovella, Ayan Sengupta, Falko R. Kaule, Roland Nigbur, Alexander Q. Waite, Florian J. Baumgartner & Jörg Stadler. Simultaneous fMRI and eye gaze recordings during prolonged natural stimulation – a studyforrest extension. (submitted for publication)
For more information about the project visit: http://studyforrest.org
The dataset is available for download from OpenFMRI (accession number ds000113d).
Alternatively, the studyforrest phase 2 repository on GitHub provides access as a DataLad 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 up to the level of single files. 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.
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.
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.
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 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.