Provided by: datalad_0.12.4-2_all
NAME
datalad install - install a dataset from a (remote) source.
SYNOPSIS
datalad install [-h] [-s SOURCE] [-d DATASET] [-g] [-D DESCRIPTION] [-r] [-R LEVELS] [--nosave] [--reckless [{auto}]] [-J NJOBS] [PATH [PATH ...]]
DESCRIPTION
This command creates a local sibling of an existing dataset from a (remote) location identified via a URL or path. Optional recursion into potential subdatasets, and download of all referenced data is supported. The new dataset can be optionally registered in an existing superdataset by identifying it via the DATASET argument (the new dataset's path needs to be located within the superdataset for that). It is recommended to provide a brief description to label the dataset's nature *and* location, e.g. "Michael's music on black laptop". This helps humans to identify data locations in distributed scenarios. By default an identifier comprised of user and machine name, plus path will be generated. When only partial dataset content shall be obtained, it is recommended to use this command without the GET-DATA flag, followed by a `get` operation to obtain the desired data. NOTE Power-user info: This command uses git clone, and git annex init to prepare the dataset. Registering to a superdataset is performed via a git submodule add operation in the discovered superdataset. Examples Install a dataset from Github into the current directory:: % datalad install https://github.com/datalad-datasets/longnow-podcasts.git Install a dataset as a subdataset into the current dataset:: % datalad install -d . --source='https://github.com/datalad-datasets/longnow-podcasts.git' Install a dataset, and get all content right away:: % datalad install --get-data --source https://github.com/datalad-datasets/longnow-podcasts.git Install a dataset with all its subdatasets:: % datalad install https://github.com/datalad-datasets/longnow-podcasts.git --recursive
OPTIONS
PATH path/name of the installation target. If no PATH is provided a destination path will be derived from a source URL similar to git clone. -h, -\-help, -\-help-np show this help message. --help-np forcefully disables the use of a pager for displaying the help message -s SOURCE, -\-source SOURCE URL or local path of the installation source. Constraints: value must be a string -d DATASET, -\-dataset DATASET specify the dataset to perform the install operation on. If no dataset is given, an attempt is made to identify the dataset in a parent directory of the current working directory and/or the PATH given. Constraints: Value must be a Dataset or a valid identifier of a Dataset (e.g. a path) -g, -\-get-data if given, obtain all data content too. -D DESCRIPTION, -\-description DESCRIPTION short description to use for a dataset location. Its primary purpose is to help humans to identify a dataset copy (e.g., "mike's dataset on lab server"). Note that when a dataset is published, this information becomes available on the remote side. Constraints: value must be a string -r, -\-recursive if set, recurse into potential subdataset. -R LEVELS, -\-recursion-limit LEVELS limit recursion into subdataset to the given number of levels. Constraints: value must be convertible to type 'int' -\-nosave by default all modifications to a dataset are immediately saved. Giving this option will disable this behavior. -\-reckless [{auto}] Set up the dataset to be able to obtain content in the cheapest/fastest possible way, even if this poses a potential risk the data integrity (e.g. hardlink files from a local clone of the dataset). Use with care, and limit to "read-only" use cases. With this flag the installed dataset will be marked as untrusted. The reckless mode is stored in a dataset's local configuration under 'datalad.clone.reckless', and will be inherited to any of its subdatasets. Constraints: value must be one of (None, True, False, 'auto') -J NJOBS, -\-jobs NJOBS how many parallel jobs (where possible) to use. Constraints: value must be convertible to type 'int', or value must be one of ('auto',) [Default: 'auto']
AUTHORS
datalad is developed by The DataLad Team and Contributors <team@datalad.org>.