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https://github.com/datalad/datalad

Keep code, data, containers under control with git and git-annex
https://github.com/datalad/datalad

closember data-storage dataset git-annex python usable

Last synced: about 1 month ago
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Keep code, data, containers under control with git and git-annex

Host: GitHub
URL: https://github.com/datalad/datalad
Owner: datalad
License: other
Created: 2013-11-01T19:40:08.000Z (about 11 years ago)
Default Branch: maint
Last Pushed: 2024-10-14T20:17:57.000Z (about 2 months ago)
Last Synced: 2024-10-29T00:29:27.699Z (about 1 month ago)
Topics: closember, data-storage, dataset, git-annex, python, usable
Language: Python
Homepage: http://datalad.org
Size: 39.6 MB
Stars: 536
Watchers: 28
Forks: 111
Open Issues: 533
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: COPYING
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff

Awesome Lists containing this project

awesome-starred - datalad/datalad - Keep code, data, containers under control with git and git-annex (python)
awesome-opendata-software - datalad - 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. (Tools / Data packaging)
awesome-neuroimaging - datalad - Keep code, data, containers under control with git and git-annex. Esp `datalad run --input=... --output=...`. (Provenance and Automation / Structural)
StarryDivineSky - datalad/datalad - annex 的数据管理和分发工具，它可以帮助用户轻松地管理代码、数据和容器。DataLad 通过将数据存储在 Git 仓库中，并使用 git-annex 来管理大型文件，从而实现数据版本控制、协作和分发。DataLad 还支持多种数据格式，并提供了一系列工具来简化数据分析和处理。 (其他_机器学习与深度学习)

README

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                                                  Read me

[![DOI](https://joss.theoj.org/papers/10.21105/joss.03262/status.svg)](https://doi.org/10.21105/joss.03262)

[![Test Status](https://github.com/datalad/datalad/actions/workflows/test.yml/badge.svg)](https://github.com/datalad/datalad/actions/workflows/test.yml)

[![Build status](https://ci.appveyor.com/api/projects/status/github/datalad/datalad?branch=master&svg=true)](https://ci.appveyor.com/project/mih/datalad/branch/master)

[![Extensions](https://github.com/datalad/datalad/actions/workflows/test_extensions.yml/badge.svg)](https://github.com/datalad/datalad/actions/workflows/test_extensions.yml)

[![Linters](https://github.com/datalad/datalad/actions/workflows/lint.yml/badge.svg)](https://github.com/datalad/datalad/actions/workflows/lint.yml)

[![codecov.io](https://codecov.io/github/datalad/datalad/coverage.svg?branch=master)](https://codecov.io/github/datalad/datalad?branch=master)

[![Documentation](https://readthedocs.org/projects/datalad/badge/?version=latest)](http://datalad.rtfd.org)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![GitHub release](https://img.shields.io/github/release/datalad/datalad.svg)](https://GitHub.com/datalad/datalad/releases/)

[![Supported Python versions](https://img.shields.io/pypi/pyversions/datalad)](https://pypi.org/project/datalad/)

[![Testimonials 4](https://img.shields.io/badge/testimonials-4-brightgreen.svg)](https://github.com/datalad/datalad/wiki/Testimonials)

[![https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg](https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg)](https://singularity-hub.org/collections/667)

[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](https://github.com/datalad/datalad/blob/master/CODE_OF_CONDUCT.md)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.808846.svg)](https://doi.org/10.5281/zenodo.808846)

[![RRID](https://img.shields.io/badge/RRID-SCR__003931-blue)](https://identifiers.org/RRID:SCR_003931)

[![All Contributors](https://img.shields.io/badge/all_contributors-52-orange.svg?style=flat-square)](#contributors-)

## Distribution

[![Anaconda](https://anaconda.org/conda-forge/datalad/badges/version.svg)](https://anaconda.org/conda-forge/datalad)

[![Arch (AUR)](https://repology.org/badge/version-for-repo/aur/datalad.svg?header=Arch%20%28%41%55%52%29)](https://repology.org/project/datalad/versions)

[![Debian Stable](https://badges.debian.net/badges/debian/stable/datalad/version.svg)](https://packages.debian.org/stable/datalad)

[![Debian Unstable](https://badges.debian.net/badges/debian/unstable/datalad/version.svg)](https://packages.debian.org/unstable/datalad)

[![Fedora Rawhide package](https://repology.org/badge/version-for-repo/fedora_rawhide/datalad.svg?header=Fedora%20%28rawhide%29)](https://repology.org/project/datalad/versions)

[![Gentoo (::science)](https://repology.org/badge/version-for-repo/gentoo_ovl_science/datalad.svg?header=Gentoo%20%28%3A%3Ascience%29)](https://repology.org/project/datalad/versions)

[![PyPI package](https://repology.org/badge/version-for-repo/pypi/datalad.svg?header=PyPI)](https://repology.org/project/datalad/versions)

# 10000-ft. overview

DataLad's purpose is to make data management and data distribution more accessible. 

To do so, 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 - or datasets. However, the actual data storage and permission

management remains with the original data provider(s).

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](http://handbook.datalad.org/extension_pkgs.html) is available in

the [DataLad handbook](http://handbook.datalad.org).

# 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](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 enabling [NeuroDebian], via which we

provide recent releases of DataLad. Once enabled, just do:

    apt-get install datalad

## Gentoo-based systems

On Gentoo-based systems (i.e. all systems whose package manager can parse ebuilds as per the [Package Manager Specification]), we recommend [enabling the ::science overlay], via which we

provide recent releases of DataLad. Once enabled, just run:

    emerge 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](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 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](CONTRIBUTING.md) if you are interested in internals or

contributing to the project.

## Acknowledgements

The DataLad project received support through the following grants:

- 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).

- CRCNS US-German Data Sharing "DataLad - a decentralized system for integrated

  discovery, management, and publication of digital objects of science"

  (Halchenko/Pestilli/Hanke), co-funded by the US National Science Foundation

  (NSF 1912266) and the German Federal Ministry of Education and Research

  (BMBF 01GQ1905).

- Helmholtz Research Center Jülich, FDM challenge 2022

- German federal state of Saxony-Anhalt and the European Regional Development

  Fund (ERDF), Project: Center for Behavioral Brain Sciences, Imaging Platform

- ReproNim project (NIH 1P41EB019936-01A1).

- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant

  SFB 1451 ([431549029](https://gepris.dfg.de/gepris/projekt/431549029),

  INF project)

- European Union’s Horizon 2020 research and innovation programme under grant

  agreements:

  - [Human Brain Project SGA3 (H2020-EU.3.1.5.3, grant no. 945539)](https://cordis.europa.eu/project/id/945539)

  - [VirtualBrainCloud (H2020-EU.3.1.5.3, grant no. 826421)](https://cordis.europa.eu/project/id/826421)

Mac mini instance for development is provided by

[MacStadium](https://www.macstadium.com/).

### Contributors ✨

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

  

    

      
_glalteva
💻

      
_adswa
💻

      
_chrhaeusler
💻

      
_soichih
💻

      
_mvdoc
💻

      
_mih
💻

      
_yarikoptic
💻

    

    

      
_loj
💻

      
_feilong
💻

      
_jhpoelen
💻

      
_andycon
💻

      
_nicholsn
💻

      
_adelavega
💻

      
_kskyten
💻

    

    

      
_TheChymera
💻

      
_effigies
💻

      
_jgors
💻

      
_debanjum
💻

      
_nellh
💻

      
_emdupre
💻

      
_aqw
💻

    

    

      
_vsoch
💻

      
_kyleam
💻

      
_driusan
💻

      
_overlake333
💻

      
_akeshavan
💻

      
_jwodder
💻

      
_bpoldrack
💻

    

    

      
_{yetanothertestuser}
💻

      
_{Christian Mönch}
💻

      
_{Matt Cieslak}
💻

      
_{Mika Pflüger}
💻

      
_{Robin Schneider}
💻

      
_{Sin Kim}
💻

      
_{Michael Burgardt}
💻

    

    

      
_{Remi Gau}
💻

      
_{Michał Szczepanik}
💻

      
_Basile
💻

      
_{Taylor Olson}
💻

      
_{James Kent}
💻

      
_xgui3783
💻

      
_tstoeter
💻

    

    

      
_{Stephan Heunis}
💻

      
_{Matt McCormick}
💻

      
_{Vicky C Lau}
💻

      
_{Chris Lamb}
💻

      
_{Austin Macdonald}
💻

      
_{Yann Büchau}
💻

      
_{Matthias Riße}
💻

    

    

      
_Aksoo
💻

      
_{David Guibert}
💻

      
_{Alex Shields-Weber}
💻

    

  

[![macstadium](https://uploads-ssl.webflow.com/5ac3c046c82724970fc60918/5c019d917bba312af7553b49_MacStadium-developerlogo.png)](https://www.macstadium.com/)

[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

[Package Manager Specification]: https://projects.gentoo.org/pms/latest/pms.html

[enabling the ::science overlay]: https://github.com/gentoo/sci#manual-install-

[DataLad Handbook: Installation]: http://handbook.datalad.org/en/latest/intro/installation.html