Customization and extension of functionality

DataLad provides numerous commands that cover many use cases. However, there will always be a demand for further customization or extensions of built-in functionality at a particular site, or for an individual user. DataLad addresses this need with two mechanisms:

Plugins are a quick’n’dirty way to implement a single additional command with very little overhead. They are, however, not the method of choice for extending particular Datalad functionality, such as metadata extractor, or providing entire command suites for a specialized purpose. For all these scenarios extension packages are the recommended method.


A number of plugins are shipped with DataLad. This includes plugins which operate on a particular dataset, but also general functionality that can be used outside the context of a specific dataset. The following table provides an overview of plugins included in this DataLad release.

add_readme(dataset[, filename, existing]) Add basic information about DataLad datasets to a README file
addurls(dataset, urlfile, urlformat, …[, …]) Create and update a dataset from a list of URLs.
check_dates(paths[, reference_date, revs, …]) Find repository dates that are more recent than a reference date.
export_archive(dataset[, filename, …]) Export the content of a dataset as a TAR/ZIP archive.
export_to_figshare(dataset[, filename, …]) Export the content of a dataset as a ZIP archive to figshare
no_annex(dataset, pattern[, ref_dir, makedirs]) Configure a dataset to never put some content into the dataset’s annex
wtf([dataset, sensitive, sections, flavor, …]) Generate a report about the DataLad installation and configuration

In previous versions of DataLad, plugins were invoked differently than regular DataLad commands, but they can now be called like any other command. The wtf plugin, for example, is exposed as

% datalad wtf

Plugin detection

DataLad will discover plugins at three locations:

  1. official plugins that are part of the local DataLad installation

  2. system-wide plugins, provided by the local admin

    The location where plugins need to be placed depends on the platform. On GNU/Linux systems this will be /etc/xdg/datalad/plugins, whereas on Windows it will be C:\ProgramData\\datalad\plugins.

    This default location can be overridden by setting the datalad.locations.system-plugins configuration variable in the local or global Git configuration.

  3. user-supplied plugins, customizable by each user

    Again, the location will depend on the platform. On GNU/Linux systems this will be $HOME/.config/datalad/plugins, whereas on Windows it will be C:\Users\<username>\AppData\Local\\datalad\plugins.

    This default location can be overridden by setting the datalad.locations.user-plugins configuration variable in the local or global Git configuration.

Identically named plugins in latter location replace those in locations searched before. This can be used to alter the behavior of plugins provided with DataLad, and enables users to adjust a site-wide configuration.

Writing own plugins

The best way to go about writing your own plugin, is to have a look at the source code of those include in DataLad. Writing a plugin a rather simple when following the following rules.

Language and location

Plugins are written in Python. In order for DataLad to be able to find them, plugins need to be placed in one of the supported locations described above. Plugin file names have to have a ‘.py’ extensions and must not start with an underscore (‘_’).

Skeleton of a plugin

The basic structure of a plugin looks like this:

from datalad.interface.base import build_doc, Interface

class MyPlugin(Interface):
    """Help message description (parameters will be added automatically)"""
    from datalad.distribution.dataset import datasetmethod, EnsureDataset
    from datalad.interface.utils import eval_results
    from import EnsureNone
    from import Parameter

    _params_ = dict(
            args=("-d", "--dataset"),
            doc=""""specify the dataset to report on.
            no dataset is given, an attempt is made to identify the dataset
            based on the current working directory.""",
            constraints=EnsureDataset() | EnsureNone()))

    def __call__(dataset):
        # Do things and yield status dicts.

__datalad_plugin__ = MyPlugin

In this example, the plugin is called my-plugin. Any number of parameters can be added by extending both the _params_ dictionary and the signature of __call__. The help message for the plugin command is generated using the docstring of the plugin class and the _params_ dictionary.

Expected behavior

The plugin’s __call__ method must yield its results as a Python generator. Results are DataLad status dictionaries. There are no constraints on the number of results, or the number and nature of result properties. However, conventions exists and must be followed for compatibility with the result evaluation and rendering performed by DataLad.

The following property keys must exist:

{‘ok’, ‘notneeded’, ‘impossible’, ‘error’}
label for the action performed by the plugin. In many cases this could be the plugin’s name.

The following keys should exists if possible:

absolute path to a result on the file system
label indicating the nature of a result (e.g. ‘file’, ‘dataset’, ‘directory’, etc.)
string message annotating the result, particularly important for non-ok results. This can be a tuple with ‘logging’-style string expansion.

Extension packages

As the name suggests, a DataLad extension package is a proper Python package. Consequently, there is a significant amount of boilerplate code involved in the creation of a new Datalad extension. However, this overhead enables a number of useful features for extension developers:

  • extensions can provide any number of additional commands that can be grouped into labeled command suites, and are automatically exposed via the standard DataLad commandline and Python API
  • extensions can define entry_points for any number of additional metadata extractors that become automatically available to DataLad
  • extensions can define entry_points for their test suites, such that the standard datalad test command will automatically run these tests in addition to the tests shipped with Datalad core
  • extensions can ship additional dataset procedures by installing them into a directory resources/procedures underneath the extension module directory

Using an extension

A DataLad extension is a standard Python package. Beyond installation of the package there is no additional setup required.

Writing your own extensions

A good starting point for implementing a new extension is the “helloworld” demo extension available at This repository can be cloned and adjusted to suit one’s needs. It includes:

  • a basic Python package setup
  • simple demo command implementation
  • Travis test setup

A more complex extension setup can be seen in the DataLad Neuroimaging extension:, including additional metadata extractors, test suite registration, and a sphinx-based documentation setup for a DataLad extension.

As a DataLad extension is a standard Python package, an extension should declare dependencies on an appropriate DataLad version, and possibly other extensions via the standard mechanisms.