datalad 0.11.1-1 source package in Ubuntu

Changelog

datalad (0.11.1-1) unstable; urgency=medium

  * Fresh upstream bugfix/minor release
    - compatibility with the recent annex
    - variety of fixes (long command lines, metadata aggregation, etc)

 -- Yaroslav Halchenko <email address hidden>  Mon, 26 Nov 2018 09:03:36 -0500

Upload details

Uploaded by:
NeuroDebian Team on 2018-11-27
Uploaded to:
Sid
Original maintainer:
NeuroDebian Team
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section

Builds

Disco: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
datalad_0.11.1-1.dsc 3.2 KiB 9c1c5429ee236c093245870ed9b1f85a50cff0aa56e4934b44d2f350ae71e7da
datalad_0.11.1.orig.tar.gz 1.2 MiB 4b6015dba256a52cd0b70bc7a6ad5a8217e1baa2899f2067757fa7fd4deb2604
datalad_0.11.1-1.debian.tar.xz 9.9 KiB 684dee71edaa72289a090f9f371580cd00e19bbf2c06a33a55c1077b85c0c9a4

Available diffs

No changes file available.

Binary packages built by this source

datalad: data files management and distribution platform

 DataLad is a data management and distribution platform providing
 access to a wide range of data resources already available online.
 Using git-annex as its backend for data logistics it provides following
 facilities built-in or available through additional extensions
 .
  - command line and Python interfaces for manipulation of collections of
    datasets (install, uninstall, update, publish, save, etc.) and
 separate files/directories (add, get)
  - extract, aggregate, and search through various sources of metadata
    (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI
    support)
  - crawl web sites to automatically prepare and update git-annex
    repositories with content from online websites, S3, etc (install
    datalad-crawler)
 .
 This package provides the command line tools. Install without
 Recommends if you need only core functionality.

python-datalad: data files management and distribution platform

 DataLad is a data management and distribution platform providing
 access to a wide range of data resources already available online.
 Using git-annex as its backend for data logistics it provides following
 facilities built-in or available through additional extensions
 .
  - command line and Python interfaces for manipulation of collections of
    datasets (install, uninstall, update, publish, save, etc.) and
 separate files/directories (add, get)
  - extract, aggregate, and search through various sources of metadata
    (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI
    support)
  - crawl web sites to automatically prepare and update git-annex
    repositories with content from online websites, S3, etc (install
    datalad-crawler)
 .
 This package installs the module for Python 2, and Recommends install
 all dependencies necessary for searching and managing datasets, publishing,
 and testing. If you need base functionality, install without Recommends.

python3-datalad: data files management and distribution platform

 DataLad is a data management and distribution platform providing
 access to a wide range of data resources already available online.
 Using git-annex as its backend for data logistics it provides following
 facilities built-in or available through additional extensions
 .
  - command line and Python interfaces for manipulation of collections of
    datasets (install, uninstall, update, publish, save, etc.) and
 separate files/directories (add, get)
  - extract, aggregate, and search through various sources of metadata
    (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI
    support)
  - crawl web sites to automatically prepare and update git-annex
    repositories with content from online websites, S3, etc (install
    datalad-crawler)
 .
 This package installs the module for Python 3, and Recommends install
 all dependencies necessary for searching and managing datasets, publishing,
 and testing. If you need base functionality, install without Recommends.