Publishing or sharing data, code, and results is a key element of the scientific process. Sharing comes in many flavors, for example:
- sharing with yourself, and having certainty that the copy on your laptop matches what you have been working with on your workstation;
- sharing with colleagues, and making sure they all know when data changed and which results have to be recomputed; and
- sharing with the world, to make sure that your work has maximum impact.
DataLad aids many types of sharing efforts. It supports synchronization of multiple instances of a dataset belonging to a single person. It provides on-demand updates of datasets shared with local or remote collaborators. It offers a wide variety of publications methods, ranging from a (cloud) server, to services such as GitHub, DropBox, or box.com.
Here is a demo of using DataLad with a cloud storage service. This combination allows for convenient data exchange between colleagues, or simple data synchronization between the machines of a single person.
Most cloud storage services are not ideal for data sharing with the general public, because they require a file or directory to be shared with particular accounts of that service provider. An SSH-accessible web server does not have these limitation, and is equally well supported by DataLad. Here is a demo that shows how one can publish large data "on GitHub" for maximum visibility, and configure DataLad to transparently obtain all data files from a different web server.