bionic (1) datalad-install.1.gz

SYNOPSIS

       datalad-install  [-h]  [-s  SOURCE]  [-d  DATASET]  [-g] [-D DESCRIPTION] [-r] [--recursion-limit LEVELS]
              [--nosave] [--reckless] [-J NJOBS] [PATH [PATH ...]]

DESCRIPTION

       Install a dataset from a (remote) source.

       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.

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. [Default: None]

       -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 [Default: None]

       -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)
              [Default: None]

       -g, --get-data
              if given, obtain all data content too. [Default: False]

       -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 [Default:
              None]

       -r, --recursive
              if set, recurse into potential subdataset. [Default: False]

       --recursion-limit LEVELS
              limit recursion into subdataset to  the  given  number  of  levels.  Constraints:  value  must  be
              convertible to type 'int' [Default: None]

       --nosave
              by  default  all  modifications to a dataset are immediately saved. Given this option will disable
              this behavior. [Default: True]

       --reckless
              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. [Default: False]

       -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: None]

AUTHORS

        datalad is developed by The DataLad Team and Contributors <team@datalad.org>.