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About the developer

datalad
238 Stars 73 Forks Other 13.7K Commits 678 Opened issues

Description

Keep code, data, containers under control with git and git-annex

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Contributors list

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Travis tests status Build status codecov.io Documentation License: MIT GitHub release PyPI version fury.io Supported Python versions Testimonials 4 https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg DOI <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section --> <!-- ALL-CONTRIBUTORS-BADGE:END -->

10000-ft. 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. An annotated list of extensions is available in the DataLad handbook.

Support

The documentation for 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 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 enabling NeuroDebian, via 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 the most recently released version, and release candidates are available via

conda install -c conda-forge/label/rc datalad

Other Linux'es, macOS via pip

Before you install this package, please make sure that you install a recent version of git-annex. Afterwards, install the latest version of

datalad
from PyPI. It is recommended to use a dedicated virtualenv:
# 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 the core functionality of DataLad, allowing for managing datasets etc. Additional installation schemes are available, so you can request enhanced installation via

pip install datalad[SCHEME]
, where
SCHEME
could be:
  • tests
    to also install dependencies used by DataLad's battery of unit tests
  • full
    to install all dependencies.

More details on installation and initial configuration can be found in the DataLad Handbook: Installation.

License

MIT/Expat

Contributing

See 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).

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