Current resource reservation architectures f o r multimedia networks don't scale well f o r a large number of flows. W e propose a new architecture that aggregates flows o n each link in the network. Therefore, the network has n o knowledge of individual flows, and resource management functions traditionally implemented an the network (such as flow acceptance control) are delegated t o hosts.

Nowadays, research practice in all scientific disciplines is increasingly, and in many cases exclusively, data driven. Knowledge of how to use tools to manipulate research data, and the availability of e-Infrastructures to support them for data storage, processing, analysis and preservation, is fundamental. In parallel, new types of communities are forming around interests in digital tools, computing facilities and data repositories. By making infrastructure services, community engagement and training inseparable, existing communities can be empowered by new ways of doing research, and new communities can be created around tools and data.

Current resource r eservation architectures for multimedia networks do not scale well for a l a r ge number of ows. We propose a new architecture that automatically aggregates ows on each link in the network. Therefore, the network has no knowledge of individual ows.

The digital revolution made available vast amounts of data both in industry and in the research landscape. The ability to manipulate and extract knowledge and value from this data represents a new profession called the Data Scientist: expected to be the most visible job in future years.The EDISON project has been established in order to support Universities, Research Centers, Industry and Research Infrastructure organisations to cope with the potential shortfall of Data Scientists, to define the framework of competences as well as the body of knowledge for this profession.In this paper the EDISON team describes how it intends to nurture the profession of Data Scientist to cope with the expected increase in demand. The strategy proposed is based on both the analysis of the demand side (Industries, Research Centers and Research Infrastructure organisations) and the supply side (Universities and training centers) bridging between the providers and employers by cooperating on the establishment of a Competence Framework and a Body of Knowledge for the Data Scientist Professional. The project will exploit piloting initiatives in cooperation with pioneer universities and also involve external experts as evangelists.