Skip to main content

data distribution geared toward scientific datasets

Project description


|  _ \    __ _  | |_    __ _  | |       __ _    __| |
| | | |  / _` | | __|  / _` | | |      / _` |  / _` |
| |_| | | (_| | | |_  | (_| | | |___  | (_| | | (_| |
|____/   \__,_|  \__|  \__,_| |_____|  \__,_|  \__,_|
                                              Read me

Travis tests status Build status codecov.io Documentation License: MIT GitHub release PyPI version fury.io Testimonials 4 https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg DOI

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

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 -- tracking web resources and automated data distributions crawler release

  • neuroimaging -- neuroimaging research data and workflows neuroimaging release

  • container -- support for containerized computational environments container release

  • 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 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

Other Linux'es, OSX 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 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 available. The latest release version can be obtained by running:

singularity pull shub://datalad/datalad

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).

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

datalad-0.12.0rc5.tar.gz (1.2 MB view details)

Uploaded Source

Built Distribution

datalad-0.12.0rc5-py2.py3-none-any.whl (1.4 MB view details)

Uploaded Python 2 Python 3

File details

Details for the file datalad-0.12.0rc5.tar.gz.

File metadata

  • Download URL: datalad-0.12.0rc5.tar.gz
  • Upload date:
  • Size: 1.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.35.0 CPython/3.7.3

File hashes

Hashes for datalad-0.12.0rc5.tar.gz
Algorithm Hash digest
SHA256 9d8cc345512d49f9482f34bd20d964352e93b4fe702a07e56a03fc98eea97187
MD5 587e8d181f2d15088e0fd8ff387338ed
BLAKE2b-256 0a0640d90c86d34504597a589a4766bc1c392ea65d791b81cc6715ccad16cf85

See more details on using hashes here.

File details

Details for the file datalad-0.12.0rc5-py2.py3-none-any.whl.

File metadata

  • Download URL: datalad-0.12.0rc5-py2.py3-none-any.whl
  • Upload date:
  • Size: 1.4 MB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.35.0 CPython/3.7.3

File hashes

Hashes for datalad-0.12.0rc5-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 8c358aca5c05b9a75f7b04dc57a0dab6e103597ad8123fcc9945fdaf94dbd17d
MD5 fd5bcbab3d78835477cf405073bbddb9
BLAKE2b-256 c62c7aecd258cbc0ede2f23f8def2df4ef4ff5ca1d0c23b9dea6515f6807b12d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page