EGI Document 3005-v1
- This face-to-face training is organised by WP15, T15.1, e-Infrastructure technology training part. This training introduces a state-of-the-art e-Infrastructure platform service for distributed data management that supports the vision of European Open Science Cloud.
In the recent published ESFRI Roadmap 2016, it highlights the notion of a European e-infrastructure Commons referring to the framework for an easy and cost-effective shared use of distributed electronic resources for research and innovation across Europe and beyond. This e-infrastructure Commons is a solid basis for building the European Open Science Cloud as introduced in the description of the Digital Single Market, already containing most of the ingredients needed for an integrated European platform for Open Science. In April 2106, the EC announces the European Cloud Initiative - 6.7billion of public and private investment in European Open Science Cloud (2016), opening up by default all scientific data (2017), flagship initiative on quantum technology (2018), development and deployment of European high performance computing, data storage and network infrastructure (2020).
To respond to this vision, EGI Foundation (as part of its flagship project, EGI-Engage) is designing and developing a new Data as a Service (DaaS) offering called the DataHub. The DataHub will be based on the Open Data Platform (ODP), which provides the backend for efficient data access and sharing on a global scale, with support for open data publishing and access.
The ODP allows the integration of various data repositories available in a distributed infrastructure, offering the capability to make data open and shared with members of the same community. Thanks to this solution researchers have the opportunity to set-up a community-specific space, fill it with some relevant data files from existing repositories and share this collection of data with all the members of the community in a distributed manner. Researchers will be able to access remote/distribute data as if they are stored in their local file-system in an efficient way. For technical details, please refer to publication: http://dx.doi.org/10.1016/j.procs.2016.08.294
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