8.1. Beyond shared infrastructure¶
Data sharing potentially involves a number of different elements:
Users on a common, shared computational infrastructure such as an SSH server can share datasets via simple installations with paths, without any involvement of third party storage providers or repository hosting services:
But at some point in a dataset’s life, you may want to share it with people that can’t access the computer or server your dataset lives on, store it on other infrastructure to save diskspace, or create a backup. When this happens, you will want to publish your dataset to repository hosting services (for example GitHub, GitLab, or Gin) and/or third party storage providers (such as Dropbox, Google, Amazon S3 buckets, the Open Science Framework (OSF), and many others).
This chapter tackles different aspects of dataset publishing. The remainder of this section talks about general aspects of dataset publishing, and illustrates the idea of using third party services as special remotes from which annexed file contents can be retrieved via datalad get.
The upcoming section Walk-through: Dataset hosting on GIN shows you one of the most easy ways to publish your dataset publicly or for selected collaborators and friends. If you don’t want to dive in to all the details on dataset sharing, it is safe to directly skip ahead to this section, and have your dataset published in only a few minutes.
Other sections in this chapter will showcase a variety of ways to publish datasets and their contents to different services: The section Publishing datasets to Git repository hosting demonstrates how to publish datasets to any kind of Git repository hosting service. The sections Walk-through: Amazon S3 as a special remote and Walk-through: Dropbox as a special remote are concrete examples of sharing datasets publicly or with selected others via different cloud services. The section Walk-through: Git LFS as a special remote on GitHub talks about using the centralized, for-pay service Git LFS for sharing dataset content on GitHub, and the section Built-in data export shows built-in dataset export to services such as figshare.com. If you want a walk-through for a different service, or if you maybe even want to share your own walk-through, please get in touch.
There can never be “too much” documentation
If you plan to share your own datasets with people that are unfamiliar with DataLad, it may be helpful to give a short explanation of what a DataLad dataset is and what it can do. For this, you can use a ready-made text block that the handbook provides. To find this textblock, go to How can I help others get started with a shared dataset?. Alternatively, run datalad add-readme.
8.1.1. Leveraging third party infrastructure¶
There are several ways to make datasets available for others:
You can publish your dataset to a repository with annex support such as gin or the Open Science Framework (OSF)1. This is the easiest way to share datasets and all their contents. Read on in the section Walk-through: Dataset hosting on GIN or consult the tutorials of the datalad-osf extension to learn how to do this.
You can publish your dataset to a repository hosting service, and configure an external resource that stores your annexed data. Such a resource can be a private web server, but also a third party services cloud storage such as Dropbox, Google, Amazon S3 buckets, Box.com, owncloud, sciebo, or many more.
You can export your dataset statically as a snapshot to a service such as Figshare or the Open Science Framework (OSF)1.
You can publish your dataset to a repository hosting service and ensure that all dataset contents are either available from pre-existing public sources or can be recomputed from a run record.
8.1.2. Dataset contents and third party services influence sharing¶
Because DataLad datasets are Git repositories, it is possible to
push datasets to any Git repository hosting service, such as
GitHub, GitLab, Gin, Bitbucket, Gogs,
You have already done this in section YODA-compliant data analysis projects when you shared your
midterm_project dataset via GitHub.
However, most Git repository hosting services do not support hosting the file content
of the files managed by git-annex.
For example, the the results of the analysis in section YODA-compliant data analysis projects,
prediction_report.csv, were not published to
GitHub: There was meta data about their file availability, but if a friend cloned
this dataset and ran a datalad get command, content retrieval would fail
because their only known location is your private computer to which only you have access.
Instead, they would need to be recomputed from the run record in the dataset.
When you are sharing DataLad datasets with other people or third party services, an important distinction thus lies in annexed versus not-annexed content, i.e., files that stored in your dataset’s annex versus files that are committed into Git. The third-party service of your choice may have support for both annexed and non-annexed files, or only one them.
126.96.36.199. The common case: Repository hosting without annex support and special remotes¶
Because DataLad datasets are Git repositories, it is possible to push datasets to any Git repository hosting service, such as GitHub, GitLab, Gin, Bitbucket, Gogs, or Gitea. But while anything that is managed by Git is accessible in repository hosting services, they usually don’t support storing annexed data2.
When you want to publish a dataset to a Git repository hosting service to allow others to easily find and clone it, but you also want others to be able to retrieve annexed files in this dataset via datalad get, annexed contents need to be pushed to additional storage hosting services. The hosting services can be all kinds of private, institutional, or commercial services, and their location will be registered in the dataset under the concept of a special remote.
What is a special remote
A special-remote is an extension to Git’s concept of remotes, and can enable git-annex to transfer data from and possibly to places that are not Git repositories (e.g., cloud services or external machines such as an HPC system). For example, an s3 special remote uploads and downloads content to AWS S3, a web special remote downloads files from the web, and datalad-archive extracts files from the annexed archives, etc. Don’t envision a special-remote as merely a physical place or location – a special-remote is a protocol that defines the underlying transport of your files to and/or from a specific location.
To register a special remote in your dataset and use it for file storage, you need to configure the service of your choice and publish the annexed contents to it. Afterwards, the published dataset (e.g., via GitHub or GitLab) stores the information about where to obtain annexed file contents from such that datalad get works. Once you have configured the service of your choice, you can push your datasets Git history to the repository hosting service and the annexed contents to the special remote. But DataLad also makes it easy to push these different dataset contents exactly where they need to be automatically via a publication dependency.
Exemplary walk-throughs for Dropbox, Amazon S3 buckets, and Git LFS can be found in the upcoming sections in this chapter. But the general workflow looks as follows:
From your perspective (as someone who wants to share data), you will need to
(potentially) install/setup the relevant special-remote,
create a dataset sibling on GitHub/GitLab/… for others to install from
set up a publication dependency between repository hosting and special remote , so that annexed contents are automatically pushed to the special remote when ever you update the sibling on the Git repository hosting site
publish your dataset
This gives you the freedom to decide where your data lives and who can have access to it. Once this set up is complete, updating and accessing a published dataset and its data is almost as easy as if it would lie on your own machine.
From the perspective of a consumer (as someone who wants to obtain your dataset), they will need to
(potentially) install the relevant special-remote (dependent on the third-party service you chose) and
perform the standard datalad clone and datalad get commands as necessary.
Thus, from a collaborator’s perspective, with the exception of potentially installing/setting up the relevant special-remote, obtaining your dataset and its data is as easy as with any public DataLad dataset. While you have to invest some setup effort in the beginning, once this is done, the workflows of yours and others are the same that you are already very familiar with.
If you are interested in learning how to set up different services as special remotes, you can take a look at the sections Walk-through: Amazon S3 as a special remote, Walk-through: Dropbox as a special remote or Walk-through: Git LFS as a special remote on GitHub for concrete examples with DataLad datasets, and the general section Publishing datasets to Git repository hosting on setting up dataset siblings. In addition, there are step-by-step walk-throughs in the documentation of git-annex for services such as S3, Google Cloud Storage, Box.com, Amazon Glacier, OwnCloud, and many more. Here is the complete list: git-annex.branchable.com/special_remotes.
188.8.131.52. The easy case: Repository hosting with annex support¶
There are a few Git repository hosting services with support for annexed contents. One of them is Gin. What makes them extremely convenient is that there is no need to configure a special remote – creating a sibling and running datalad push is enough.
Read the section Walk-through: Dataset hosting on GIN for a walk-through.
184.108.40.206. The uncommon case: Special remotes with repository hosting support¶
Typically, storage hosting services such as cloud storage providers do not provide the ability to host Git repositories. Therefore, it is typically not possible to clone from a cloud storage. However, a number of datalad extensions have been created that equip cloud storage providers with the ability to also host Git repositories. While they do not get the ability to display repositories the same way that pure Git repository hosting services like GitHub do, they do get the super power of becoming clonable.
One example for this is the Open Science Framework, which can become the home of datasets by using the datalad-osf extension. As long as you and your collaborators have the extension installed, annexed dataset contents and the Git repository part of your dataset can be pushed or cloned in one go.
Please take a look at the documentation and tutorials of datalad-osf extension for examples and more information.
220.127.116.11. The creative case: Ensuring availability using only repository hosting¶
When you only want to use pure Git repository hosting services without annex support, you can still allow others to obtain (some) file contents with some creativity:
For one, you can use commands such as datalad download-url (datalad-download manual) or datalad addurls (datalad-addurls manual) to retrieve files from web sources and register their location automatically.
The first Chapter DataLad datasets demonstrates download-url, and the usecase Scaling up: Managing 80TB and 15 million files from the HCP release demonstrates
addurls on a large scale.
Other than this, you can rely on digital provenance in the form of run records that allow consumers of your dataset to recompute a result instead of datalad geting it. The midterm-project example in section YODA-compliant data analysis projects has been an example for this.
18.104.22.168. The static case: Exporting dataset snapshots¶
While DataLad datasets have the great advantage that they carry a history with all kinds of useful digital provenance and previous versions of files, it may not in all cases be necessary to make use of this advantage. Sometimes, you may just want to share or archive the most recent state of the dataset as a snapshot.
DataLad provides the ability to do this out of the box to arbitrary locations, and support for specific services such as Figshare. Find out more information on this in the section Built-in data export. Other than that, some datalad extensions allow an export to additional services such as the Open Science Framework1.
8.1.3. General information on publishing datasets¶
Beyond concrete examples of publishing datasets, some general information may be useful in addition: The section Overview: The datalad push command illustrates the DataLad command datalad push, a command that handles every publication operation, regardless of the type of published content or its destination. In addition to this, the section Keeping (some) dataset contents private contains tips and strategies on publishing datasets without leaking potentially private contents or information. Finally, you may be interested in publishing datasets into centrally managed locations for backup, archival, or central data management. In this case, take a look at the advanced section Remote Indexed Archives for dataset storage and backup.
Requires the datalad-osf extension.
In addition to not storing annexed data, most Git repository hosting services also have a size limit for files kept in Git. So while you could theoretically commit a sizable file into Git, this would not only negatively impact the performance of your dataset as Git doesn’t handle large files well, but it would also prevent your dataset to be published to a Git repository hosting service like GitHub.
Old versions of GitLab, on the other hand, provide a git-annex configuration. It is disabled by default, and to enable it you would need to have administrative access to the server and client side of your GitLab instance. Alternatively, GitHub can integrate with GitLFS, a non-free, centralized service that allows to store large file contents. Walk-through: Git LFS as a special remote on GitHub shows an example on how to use their free trial version.