Note
Go to the end to download the full example code
Download the data set¶
In this script, we download the data set from Wasabi or GIN. No account is required.
Cite this data set¶
This tutorial is based on publicly available data published on GIN. If you publish any work using this data set, please cite the original publication [1], and the data set [2].
Download¶
# path of the data directory
from voxelwise_tutorials.io import get_data_home
directory = get_data_home(dataset="shortclips")
print(directory)
We will only use the first subject in this tutorial, but you can run the same
analysis on the four other subjects. Uncomment the lines in DATAFILES to
download more subjects.
We also skip the stimuli files, since the dataset provides two preprocessed feature spaces to perform voxelwise modeling without requiring the original stimuli.
from voxelwise_tutorials.io import download_datalad
DATAFILES = [
"features/motion_energy.hdf",
"features/wordnet.hdf",
"mappers/S01_mappers.hdf",
# "mappers/S02_mappers.hdf",
# "mappers/S03_mappers.hdf",
# "mappers/S04_mappers.hdf",
# "mappers/S05_mappers.hdf",
"responses/S01_responses.hdf",
# "responses/S02_responses.hdf",
# "responses/S03_responses.hdf",
# "responses/S04_responses.hdf",
# "responses/S05_responses.hdf",
# "stimuli/test.hdf",
# "stimuli/train_00.hdf",
# "stimuli/train_01.hdf",
# "stimuli/train_02.hdf",
# "stimuli/train_03.hdf",
# "stimuli/train_04.hdf",
# "stimuli/train_05.hdf",
# "stimuli/train_06.hdf",
# "stimuli/train_07.hdf",
# "stimuli/train_08.hdf",
# "stimuli/train_09.hdf",
# "stimuli/train_10.hdf",
# "stimuli/train_11.hdf",
]
source = "https://gin.g-node.org/gallantlab/shortclips"
for datafile in DATAFILES:
local_filename = download_datalad(datafile, destination=directory,
source=source)