datalad.api.create(path=None, initopts=None, *, force=False, description=None, dataset=None, annex=True, fake_dates=False, cfg_proc=None)

Create a new dataset from scratch.

This command initializes a new dataset at a given location, or the current directory. The new dataset can optionally be registered in an existing superdataset (the new dataset’s path needs to be located within the superdataset for that, and the superdataset needs to be given explicitly via dataset). 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.

This command only creates a new dataset, it does not add existing content to it, even if the target directory already contains additional files or directories.

Plain Git repositories can be created via annex=False. However, the result will not be a full dataset, and, consequently, not all features are supported (e.g. a description).

To create a local version of a remote dataset use the ~datalad.api.install command instead.


Power-user info: This command uses git init and git annex init to prepare the new dataset. Registering to a superdataset is performed via a git submodule add operation in the discovered superdataset.


Create a dataset ‘mydataset’ in the current directory:

> create(path='mydataset')

Apply the text2git procedure upon creation of a dataset:

> create(path='mydataset', cfg_proc='text2git')

Create a subdataset in the root of an existing dataset:

> create(dataset='.', path='mysubdataset')

Create a dataset in an existing, non-empty directory:

> create(force=True)

Create a plain Git repository:

> create(path='mydataset', annex=False)
  • path (str or Dataset or None, optional) – path where the dataset shall be created, directories will be created as necessary. If no location is provided, a dataset will be created in the location specified by dataset (if given) or the current working directory. Either way the command will error if the target directory is not empty. Use force to create a dataset in a non- empty directory. [Default: None]

  • initopts – options to pass to git init. Options can be given as a list of command line arguments or as a GitPython-style option dictionary. Note that not all options will lead to viable results. For example ‘ –bare’ will not yield a repository where DataLad can adjust files in its working tree. [Default: None]

  • force (bool, optional) – enforce creation of a dataset in a non-empty directory. [Default: False]

  • description (str or None, optional) – 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. [Default: None]

  • dataset (Dataset or None, optional) – specify the dataset to perform the create operation on. If a dataset is given along with path, a new subdataset will be created in it at the path provided to the create command. If a dataset is given but path is unspecified, a new dataset will be created at the location specified by this option. [Default: None]

  • annex (bool, optional) – if disabled, a plain Git repository will be created without any annex. [Default: True]

  • fake_dates (bool, optional) – Configure the repository to use fake dates. The date for a new commit will be set to one second later than the latest commit in the repository. This can be used to anonymize dates. [Default: False]

  • cfg_proc – Run cfg_PROC procedure(s) (can be specified multiple times) on the created dataset. Use run_procedure(discover=True) to get a list of available procedures, such as cfg_text2git. [Default: None]

  • on_failure ({'ignore', 'continue', 'stop'}, optional) – behavior to perform on failure: ‘ignore’ any failure is reported, but does not cause an exception; ‘continue’ if any failure occurs an exception will be raised at the end, but processing other actions will continue for as long as possible; ‘stop’: processing will stop on first failure and an exception is raised. A failure is any result with status ‘impossible’ or ‘error’. Raised exception is an IncompleteResultsError that carries the result dictionaries of the failures in its failed attribute. [Default: ‘continue’]

  • result_filter (callable or None, optional) – if given, each to-be-returned status dictionary is passed to this callable, and is only returned if the callable’s return value does not evaluate to False or a ValueError exception is raised. If the given callable supports **kwargs it will additionally be passed the keyword arguments of the original API call. [Default: constraint:(action:{create} or status:{ok, notneeded})]

  • result_renderer – select rendering mode command results. ‘tailored’ enables a command- specific rendering style that is typically tailored to human consumption, if there is one for a specific command, or otherwise falls back on the the ‘generic’ result renderer; ‘generic’ renders each result in one line with key info like action, status, path, and an optional message); ‘json’ a complete JSON line serialization of the full result record; ‘json_pp’ like ‘json’, but pretty-printed spanning multiple lines; ‘disabled’ turns off result rendering entirely; ‘<template>’ reports any value(s) of any result properties in any format indicated by the template (e.g. ‘{path}’, compare with JSON output for all key-value choices). The template syntax follows the Python “format() language”. It is possible to report individual dictionary values, e.g. ‘{metadata[name]}’. If a 2nd-level key contains a colon, e.g. ‘music:Genre’, ‘:’ must be substituted by ‘#’ in the template, like so: ‘{metadata[music#Genre]}’. [Default: ‘tailored’]

  • result_xfm ({'datasets', 'successdatasets-or-none', 'paths', 'relpaths', 'metadata'} or callable or None, optional) – if given, each to-be-returned result status dictionary is passed to this callable, and its return value becomes the result instead. This is different from result_filter, as it can perform arbitrary transformation of the result value. This is mostly useful for top- level command invocations that need to provide the results in a particular format. Instead of a callable, a label for a pre-crafted result transformation can be given. [Default: ‘datasets’]

  • return_type ({'generator', 'list', 'item-or-list'}, optional) – return value behavior switch. If ‘item-or-list’ a single value is returned instead of a one-item return value list, or a list in case of multiple return values. None is return in case of an empty list. [Default: ‘item-or-list’]