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Git annex windows5/25/2023 ![]() Requirement already satisfied: iso8601 in /Users/lukechang/anaconda3/lib/python3.7/site-packages (from datalad) (0.1.12) Requirement already satisfied: jsmin in /Users/lukechang/anaconda3/lib/python3.7/site-packages (from datalad) (2.2.2) Requirement already satisfied: fasteners in /Users/lukechang/anaconda3/lib/python3.7/site-packages (from datalad) (0.15) Requirement already satisfied: GitPython>=2.1.12 in /Users/lukechang/anaconda3/lib/python3.7/site-packages (from datalad) (3.1.0) Requirement already satisfied: keyring>=8.0 in /Users/lukechang/anaconda3/lib/python3.7/site-packages (from datalad) (21.4.0) ![]() ![]() Requirement already satisfied: chardet>=3.0.4 in /Users/lukechang/anaconda3/lib/python3.7/site-packages (from datalad) (3.0.4) Requirement already satisfied: appdirs in /Users/lukechang/anaconda3/lib/python3.7/site-packages (from datalad) (1.4.3) Requirement already satisfied: msgpack in /Users/lukechang/anaconda3/lib/python3.7/site-packages (from datalad) (1.0.0) Requirement already satisfied: datalad in /Users/lukechang/anaconda3/lib/python3.7/site-packages (0.12.6) If you are on Debian/Ubuntu we recommend enabling the NeuroDebian repository and installing with apt-get.įor more installation options, we recommend reading the DataLad installation instructions. If you are using OSX, we recommend installing git-annex using homebrew package manager. ![]() Unfortunately, it currently requires manually installing the git-annex dependency, which is not automatically installed using pip. Installing Datalad on Mac and Unix Operating Systems #ĭataLad can be easily installed using pip. We encourage the interested reader to read the very comprehensive DataLad User Handbook for more details and troubleshooting. We will only be covering a few basic DataLad functions to get and drop data. As these datasets are large, this will allow you to only work with the data that you need for a specific tutorial and you can drop the rest when you are done with it.Īll of the DataLad commands can be run within Python using the datalad python api. Specific files can be easily downloaded using datalad get, and files can be removed from your computer at any time using datalad drop. This allows you to explore all of the files in the dataset, without having to download the entire dataset at once. While DataLad offers a number of useful features for working with datasets, there are three in particular that we think make it worth the effort to install for this course.Ĭloning a DataLad Repository can be completed with a single line of code datalad clone and provides the full directory structure in the form of symbolic links. It provides a handy command line interface for downloading data, tracking changes, and sharing it with others. The easist way to access the data is using DataLad, which is an open source version control system for data built on top of git-annex. If you are taking the Psych60 course at Dartmouth, we have already made the data available on the jupyterhub server. Note, that the entire dataset is fairly large (~42gb), but the tutorials will mostly only be working with a small portion of the data (5.8gb), so there is no need to download the entire thing. In this notebook, we will walk through how to access the datset using DataLad. Though the data is being shared on the OSF website, we recommend downloading it from our g-node repository as we have fixed a few issues with BIDS formatting and have also performed preprocessing using fmriprep. There are a total of 94 subjects available, but we will primarily only be working with a smaller subset of about 15. This dataset is well suited for these tutorials as it is (a) publicly available to anyone in the world, (b) relatively small (only about 5min), and (c) provides many options to create different types of contrasts. Read the original paper for more specific details about the task and the dataset paper. There are 100 trials in total over a 5-minute scanning session. The trials are randomized across conditions and have been optimized to maximize efficiency for a rapid event related design. Several of the tasks are cued by reading text on the screen (i.e., visual modality) and also by hearing auditory instructions (i.e., auditory modality). The Pinel Localizer task was designed to probe several different types of basic cognitive processes, such as visual perception, finger tapping, language, and math. Many of the imaging tutorials throughout this course will use open data from the Pinel Localizer task.
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