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>.