Without access to the same computational infrastructure, you can share your DataLad datasets with friends and collaborators by leveraging third party services. DataLad integrates well with a variety of free or commercial services, and with many available service options this gives you freedom in deciding where you store your data and thus who can get access.
An easy, free, and fast option is GIN, a web-based repository store for scientific data management. If you are registered and have SSH authentication set up, you can create a new, empty repository, add it as a sibling to your dataset, and publish all dataset contents – including annexed data, as GIN supports repositories with an annex.
Other repository hosting services such as GitHub and GitLab do not support an annex. If a dataset is shared via one of those platforms, annexed data needs to be published to an external data store. The published dataset stores information about where to obtain annexed file contents from such that a
datalad get(manual) works.
The external data store can be any of a variety of third party hosting providers. To enable data transfer to and from this service, you (may) need to configure an appropriate special remote, and configure a publication dependency. The section Beyond shared infrastructure walked you through how this can be done with Dropbox.
datalad push(manual) allows to override automatic decision making on to-be-published contents. If it isn’t specified, DataLad will attempt to figure out itself which and how dataset contents shall be published. With a path to files, directories, or subdatasets you can also publish only selected contents’ data.
8.10.1. Now what can I do with it?¶
Finally you can share datasets and their annexed contents with others without the need for a shared computational infrastructure. It remains your choice where to publish your dataset to – considerations of data access, safety, or potential costs will likely influence your choice of service.