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data distribution geared toward scientific datasets

Project description

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Distribution

Anaconda Arch (AUR) Debian Stable Debian Unstable Fedora Rawhide package Gentoo (::science) PyPI package

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

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. 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

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, INF project)

  • European Union’s Horizon 2020 research and innovation programme under grant agreements:

Mac mini instance for development is provided by MacStadium.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

glalteva
glalteva

💻
adswa
adswa

💻
chrhaeusler
chrhaeusler

💻
soichih
soichih

💻
mvdoc
mvdoc

💻
mih
mih

💻
yarikoptic
yarikoptic

💻
loj
loj

💻
feilong
feilong

💻
jhpoelen
jhpoelen

💻
andycon
andycon

💻
nicholsn
nicholsn

💻
adelavega
adelavega

💻
kskyten
kskyten

💻
TheChymera
TheChymera

💻
effigies
effigies

💻
jgors
jgors

💻
debanjum
debanjum

💻
nellh
nellh

💻
emdupre
emdupre

💻
aqw
aqw

💻
vsoch
vsoch

💻
kyleam
kyleam

💻
driusan
driusan

💻
overlake333
overlake333

💻
akeshavan
akeshavan

💻
jwodder
jwodder

💻
bpoldrack
bpoldrack

💻
yetanothertestuser
yetanothertestuser

💻
Christian Mönch
Christian Mönch

💻
Matt Cieslak
Matt Cieslak

💻
Mika Pflüger
Mika Pflüger

💻
Robin Schneider
Robin Schneider

💻
Sin Kim
Sin Kim

💻
Michael Burgardt
Michael Burgardt

💻
Remi Gau
Remi Gau

💻
Michał Szczepanik
Michał Szczepanik

💻
Basile
Basile

💻
Taylor Olson
Taylor Olson

💻
James Kent
James Kent

💻
xgui3783
xgui3783

💻
tstoeter
tstoeter

💻
Stephan Heunis
Stephan Heunis

💻
Matt McCormick
Matt McCormick

💻
Vicky C Lau
Vicky C Lau

💻
Chris Lamb
Chris Lamb

💻
Austin Macdonald
Austin Macdonald

💻
Yann Büchau
Yann Büchau

💻
Matthias Riße
Matthias Riße

💻
Aksoo
Aksoo

💻

macstadium

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