StateSwitch Ageing
On GitLab ('mpibgit' remote):
- This is a full copy of all StateSwitch Ageing sudatasets, but without support for annexxed data. All annexed files (including first and foremost the data) are stored on currently private gin repositories. Once sharing permissions are figured out (incl. setting repos to public in an automated fashion, annexed data should be able to be pulled from the gin repos.)
On Gin ('gin' remote):
- This is a complete copy including all of the annexxed content.
In both cases, the core structure (excluding the annexed files unless explicitly specified) can be retrieved by installing datalad and running
datalad install -r <url>
Because some subdatasets have grown a bit large despite annexing, this may take a few hours. Maybe this can be further optimized in the future.
DataLad datasets and how to use them
This repository is a DataLad dataset. It provides
fine-grained data access down to the level of individual files, and allows for
tracking future updates. In order to use this repository for data retrieval,
DataLad is required. It is a free and
open source command line tool, available for all major operating
systems, and builds up on Git and git-annex
to allow sharing, synchronizing, and version controlling collections of
large files. You can find information on how to install DataLad at
handbook.datalad.org/en/latest/intro/installation.html.
Get the dataset
A DataLad dataset can be cloned by running
datalad clone <url>
Once a dataset is cloned, it is a light-weight directory on your local machine.
At this point, it contains only small metadata and information on the
identity of the files in the dataset, but not actual content of the
(sometimes large) data files.
Retrieve dataset content
After cloning a dataset, you can retrieve file contents by running
datalad get <path/to/directory/or/file>`
This command will trigger a download of the files, directories, or
subdatasets you have specified.
DataLad datasets can contain other datasets, so called subdatasets.
If you clone the top-level dataset, subdatasets do not yet contain
metadata and information on the identity of files, but appear to be
empty directories. In order to retrieve file availability metadata in
subdatasets, run
datalad get -n <path/to/subdataset>
Afterwards, you can browse the retrieved metadata to find out about
subdataset contents, and retrieve individual files with datalad get.
If you use datalad get <path/to/subdataset>, all contents of the
subdataset will be downloaded at once.
Stay up-to-date
DataLad datasets can be updated. The command datalad update will
fetch updates and store them on a different branch (by default
remotes/origin/master). Running
datalad update --merge
will pull available updates and integrate them in one go.
Find out what has been done
DataLad datasets contain their history in the git log.
By running git log (or a tool that displays Git history) in the dataset or on
specific files, you can find out what has been done to the dataset or to individual files
by whom, and when.
More information
More information on DataLad and how to use it can be found in the DataLad Handbook at
handbook.datalad.org. The chapter
"DataLad datasets" can help you to familiarize yourself with the concept of a dataset.
kosciessa