This dataset consists of empirical 3T fMRI data recorded at three spatial resolutions (1.4 mm, 2 mm, and 3 mm isotropic voxel size) for orientation decoding in visual cortex — in order to test hypotheses on the strength and spatial scale of orientation discriminating signals. This is an extension of the studyforrest project. All seven participants previously volunteered for the audio-only and the audio-visual Forrest Gump study. Five of the seven participants also participated in a matching study using identical protocols, but 7T data acquisition (with the same the three spatial resolutions used here, plus a 0.8 mm acquisition). The dataset is compliant with the BIDS data description standard (http://bids.neuroimaging.io). A detailed description can be found in:
Sengupta, A., Speck, O., Yakupov, R., Kanowski, M., Tempelmann, C., Pollmann, S. & Hanke, M. (2018) The effect of acquisition resolution on orientation decoding from V1: comparison of 3T and 7T. bioRxiv.
For more information about the project visit: http://studyforrest.org
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.
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.
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 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.