Frequently Asked Questions¶
What is Git?¶
Git is a free and open source distributed version control system. In a directory that is initialized as a Git repository, it can track small-sized files and the modifications done to them. Git thinks of its data like a series of snapshots – it basically takes a picture of what all files look like whenever a modification in the repository is saved. It is a powerful and yet small and fast tool with many features such as branching and merging for independent development, checksumming of contents for integrity, and easy collaborative workflows thanks to its distributed nature.
DataLad uses Git underneath the hood. Every DataLad dataset is a Git
repository, and you can use any Git command within a DataLad dataset. Based
on the configurations in
.gitattributes, file content can be version
controlled by Git or managed by git-annex, based on path pattern, file types,
or file size. The section More on DIY configurations details how these configurations work.
gives a comprehensive overview on what Git is.
Where is Git’s “staging area” in DataLad datasets?¶
As mentioned in Populate a dataset, a local version control workflow with DataLad “skips” the staging area (that is typical for Git workflows) from the user’s point of view.
What is git-annex?¶
is a distributed file synchronization system written by Joey Hess. It can
share and synchronize large files independent from a commercial service or a
central server. It does so by managing all file content in a separate
directory (the annex, object tree, or key-value-store in
and placing only file names and
metadata into version control by Git. Among many other features, git-annex
can ensure sufficient amounts of file copies to prevent accidental data loss and
enables a variety of data transfer mechanisms.
DataLad uses git-annex underneath the hood for file content tracking and
transport logistics. git-annex offers an astonishing range of functionality
that DataLad tries to expose in full. That being said, any DataLad dataset
(with the exception of datasets configured to be pure Git repositories) is
fully compatible with git-annex – you can use any git-annex command inside a
The chapter Data integrity can give you more insights into how git-annex takes care of your data. git-annex’s website can give you a complete walk-through and detailed technical background information.
What does DataLad add to Git and git-annex?¶
DataLad sits on top of Git and git-annex and tries to integrate and expose their functionality fully. While DataLad thus is a “thin layer” on top of these tools and tries to minimize the use of unique/idiosyncratic functionality, it also tries to simplify working with repositories and adds a range of useful concepts and functions:
Both Git and git-annex are made to work with a single repository at a time. For example, while nesting pure Git repositories is possible via Git submodules (that DataLad also uses internally), cleaning up after placing a random file somewhere into this repository hierarchy can be very painful. A key advantage that DataLad brings to the table is that it makes the boundaries between repositories vanish from a user’s point of view. Most core commands have a
--recursiveoption that will discover and traverse any subdatasets and do-the-right-thing. Whereas git and git-annex would require the caller to first cd to the target repository, DataLad figures out which repository the given paths belong to and then works within that repository. datalad save . --recursive will solve the subdataset problem above for example, no matter what was changed/added, no matter where in a tree of subdatasets.
DataLad provides users with the ability to act on “virtual” file paths. If software needs data files that are carried in a subdataset (in Git terms: submodule) for a computation or test, a
datalad getwill discover if there are any subdatasets to install at a particular version to eventually provide the file content.
DataLad adds metadata facilities for metadata extraction in various flavors, and can store extracted and aggregated metadata under
Does DataLad host my data?¶
No, DataLad manages your data, but it does not host it. When publishing a dataset with annexed data, you will need to find a place that the large file content can be stored in – this could be a web server, a cloud service such as Dropbox, an S3 bucket, or many other storage solutions – and set up a publication dependency on this location. This gives you all the freedom to decide where your data lives, and who can have access to it. Once this set up is complete, publishing and accessing a published dataset and its data are as easy as if it would lie on your own machine.
sketch a typical workflow, link a yet to write section on sharing on third party infrastructure
How does GitHub relate to DataLad?¶
DataLad can make good use of GitHub, if you have figured out storage for your large files otherwise. You can make DataLad publish file content to one location and afterwards automatically push an update to GitHub, such that users can install directly from GitHub and seemingly also obtain large file content from GitHub. GitHub is also capable of resolving submodule/subdataset links to other GitHub repos, which makes for a nice UI.
What is the difference between a superdataset, a subdataset, and a dataset?¶
Conceptually and technically, there is no difference between a dataset, a
subdataset, or a superdataset. The only aspect that makes a dataset a sub- or
superdataset is whether it is registered in another dataset (by means of an entry in the
.gitmodules, automatically performed upon an appropriate
install -d or
datalad create -d command) or contains registered datasets.
How can I convert/import/transform an existing Git or git-annex repository into a DataLad dataset?¶
You can transform any existing Git or git-annex repository of yours into a DataLad dataset by running:
$ datalad create -f
How can I cite DataLad?¶
What is the difference between DataLad, Git LFS, and Flywheel?¶
Flywheel is an informatics platform for biomedical research and collaboration.
Git Large File Storage (Git LFS) is a command line tool that extends Git with the ability to manage large files. In that it appears similar to git-annex.
A more elaborate delineation from related solutions can be found in the DataLad developer documentation.
DataLad version-controls my large files – great. But how much is saved in total?¶
How can I copy data out of a DataLad dataset?¶
Moving or copying data out of a DataLad dataset is always possible and works in many cases just like in any regular directory. The only caveat exists in the case of annexed data: If file content is managed with git-annex and stored in the object-tree, what appears to be the file in the dataset is merely a symlink (please read section Data integrity for details). Moving or copying this symlink will not yield the intended result – instead you will have a broken symlink outside of your dataset.
When using the terminal command
cp1, it is sufficient to use the
--dereference option. This will follow symbolic links, and make
sure that content gets moved instead of symlinks.
With tools other than
cp (e.g., graphical file managers), to copy or move
annexed content, make sure it is unlocked first:
After a datalad unlock copying and moving contents will work fine.
A subsequent datalad save in the dataset will annex the content
Is there a graphical user interface for DataLad?¶
No, DataLad’s functionality is available in the command line or via it’s Python API.
maybe update this by mentioning the DataLad webapp extension
How does DataLad interface with OpenNeuro?¶
OpenNeuro is a free and open platform for sharing MRI, MEG, EEG, iEEG, and ECoG data. It publishes hosted data as DataLad datasets on GitHub. The entire collection can be found at github.com/OpenNeuroDatasets. You can obtain the datasets just as any other DataLad datasets with datalad clone or datalad install.