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Read me
[](https://travis-ci.org/datalad/datalad) [](https://ci.appveyor.com/project/mih/datalad/branch/master) [](https://codecov.io/github/datalad/datalad?branch=master) [](http://datalad.rtfd.org) [](https://opensource.org/licenses/MIT) [](https://GitHub.com/datalad/datalad/releases/) [](https://pypi.python.org/pypi/datalad/) [](https://github.com/datalad/datalad/wiki/Testimonials) [](https://singularity-hub.org/collections/667) [](https://doi.org/10.5281/zenodo.3233911)
# 10000ft overview
DataLad makes data management and data distribution more accessible.
To do that, it stands on the shoulders of [Git] and [Git-annex] to deliver a
decentralized system for data exchange. This includes automated ingestion of
data from online portals and exposing it in readily usable form as Git(-annex)
repositories, so-called datasets. The actual data storage and permission
management, however, remains with the original data providers.
The full documentation is available at http://docs.datalad.org and
http://handbook.datalad.org provides a hands-on crash-course on DataLad.
# Extensions
A number of extensions are available that provide additional functionality for
DataLad. Extensions are separate packages that are to be installed in addition
to DataLad. In order to install DataLad customized for a particular domain, one
can simply install an extension directly, and DataLad itself will be
automatically installed with it. Here is a list of known extensions:
- [crawler](https://github.com/datalad/datalad-crawler) -- tracking web resources and automated data distributions [](https://GitHub.com/datalad/datalad-crawler/releases/)
- [neuroimaging](https://github.com/datalad/datalad-neuroimaging) -- neuroimaging research data and workflows [](https://GitHub.com/datalad/datalad-neuroimaging/releases/)
- [container](https://github.com/datalad/datalad-container) -- support for containerized computational environments [](https://GitHub.com/datalad/datalad-container/releases/)
- [webapp](https://github.com/datalad/datalad-webapp) -- support for exposing selected DataLad API as REST API webapp [tech demo]
# Support
The documentation of this project is found here:
http://docs.datalad.org
All bugs, concerns and enhancement requests for this software can be submitted here:
https://github.com/datalad/datalad/issues
If you have a problem or would like to ask a question about how to use DataLad,
please [submit a question to
NeuroStars.org](https://neurostars.org/new-topic?body=-%20Please%20describe%20the%20problem.%0A-%20What%20steps%20will%20reproduce%20the%20problem%3F%0A-%20What%20version%20of%20DataLad%20are%20you%20using%20%28run%20%60datalad%20--version%60%29%3F%20On%20what%20operating%20system%20%28consider%20running%20%60datalad%20plugin%20wtf%60%29%3F%0A-%20Please%20provide%20any%20additional%20information%20below.%0A-%20Have%20you%20had%20any%20luck%20using%20DataLad%20before%3F%20%28Sometimes%20we%20get%20tired%20of%20reading%20bug%20reports%20all%20day%20and%20a%20lil'%20positive%20end%20note%20does%20wonders%29&tags=datalad)
with a ``datalad`` tag. NeuroStars.org is a platform similar to StackOverflow
but dedicated to neuroinformatics.
All previous DataLad questions are available here:
http://neurostars.org/tags/datalad/
# Installation
## Debian-based systems
On Debian-based systems, we recommend to enable [NeuroDebian] from which we
provide recent releases of DataLad. Once enabled, just do:
apt-get install datalad
## Other Linux'es via conda
conda install -c conda-forge datalad
will install released released version, and release candidates are available
via
conda install -c conda-forge/label/rc datalad
## Other Linux'es, OSX via pip
Before you install this package, please make sure that you [install a recent
version of git-annex](https://git-annex.branchable.com/install). Afterwards,
install the latest version of `datalad` from
[PyPi](https://pypi.org/project/datalad). It is recommended to use
a dedicated [virtualenv](https://virtualenv.pypa.io):
# create and enter a new virtual environment (optional)
virtualenv --python=python3 ~/env/datalad
. ~/env/datalad/bin/activate
# install from PyPi
pip install datalad
By default, installation via pip installs core functionality of datalad
allowing for managing datasets etc. Additional installation schemes
are available, so you could provide enhanced installation via
`pip install datalad[SCHEME]` where `SCHEME` could be
- `tests`
to also install dependencies used by unit-tests battery of the datalad
- `full`
to install all dependencies.
There is also a [Singularity container](http://singularity.lbl.gov) available.
The latest release version can be obtained by running:
singularity pull shub://datalad/datalad
More details on installation and initial configuration could be found in the
[DataLad Handbook: Installation].
# License
MIT/Expat
# Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) if you are interested in internals or
contributing to the project.
## Acknowledgements
DataLad development is supported by a US-German collaboration in computational
neuroscience (CRCNS) project "DataGit: converging catalogues, warehouses, and
deployment logistics into a federated 'data distribution'" (Halchenko/Hanke),
co-funded by the US National Science Foundation (NSF 1429999) and the German
Federal Ministry of Education and Research (BMBF 01GQ1411). Additional support
is provided by the German federal state of Saxony-Anhalt and the European
Regional Development Fund (ERDF), Project: Center for Behavioral Brain
Sciences, Imaging Platform. This work is further facilitated by the ReproNim
project (NIH 1P41EB019936-01A1).
[Git]: https://git-scm.com
[Git-annex]: http://git-annex.branchable.com
[setup.py]: https://github.com/datalad/datalad/blob/master/setup.py
[NeuroDebian]: http://neuro.debian.net
[DataLad Handbook: Installation]: http://handbook.datalad.org/en/latest/intro/installation.html