A shared definition of the concept of "e-infrastructure" is key for the project e-ROSA. According to the European Commission, an e-infrastructure is usually described as:

  • a combination of digital technologies (hardware and software), resources (data, services, digital libraries), communications (protocols, access rights and networks), and the people and organisational structures needed to manage them.

  • They are keys in future development of research infrastructures, as activites go increasingly "online" and produce vast amounts of data and are at the heart of the Digital Agenda of the European Commision to support Open Science and the link between researchers, citizens or private companies.

  • They are becoming a part of the research infrastructure.

  • Whereas these definitions are shared by the members of the e-ROSA project, it is strongly felt that the definition has to be both broadened and specified.

As general considerations the infrastructure should:


    Infrastructures are generally community based. A city has an infrastructure, the same goes for a region or a country. Infrastructures are strongly or loosely coupled. Even within an infrastructure there will be networks of institutions with more or less binding collaboration agreements. These collaboration agreements might contain:

  • agreement about the components of the community, science and research, education, private sector, public institutions
  • shared values, i.e. FAIR principles for data
  • Governance for data, not just for hardware or computer or interface, stewardship is often key to use and reuse of data
  • Knowledge exchange mechanisms, online and offline
  • Structures for maintaining skills, support and capacity development
  • Agreements about scaling operations for tasks that could not be executed singly by members of the community
  • Agreement about shared services for efficiency gains
  • Different categories of stakeholders: Operators of the components of the e-infra - users: scientific users, education, private sector - institutions
  • Efficient communication structures and gathering of communities

Governance and Business Model

    Without an agreed governance and a clear business model no infrastructure is sustainable. Governance and business model need to clarify the roles of the different components of the community and the revenue streams that can sustain the infrastructure

  • centralize as much as necessary but decentralize as much as possible with federated structures
  • Maximum transparency about rules and operations with a set of agreed charter documents
  • Preferring self-sustained operations to operations that are dependent from continuous external input
  • Self-sustained operations are operations that are offered from partners in the network or which create the revenue that they need for functioning
  • Efficient mechanisms to access external funding for investments

technical Backbone

    High speed Cloud and Grid connectivity

  • Computing centers and distributed computing power with sufficient capacity for analysis, storage and back up
  • Effective connections to Laboratory and other research equipment
  • Effective connections to Earth Observation facilities, like remote sensing and satellites
  • Facilities that gather data from field operations


    Services have to facilitate a Web environment for the creation, sharing and publication of FAIR agricultural data as well as the tools and methods to process them and transform them into actionable knowledge that will enable to address the big agricultural challenges. Services within an Infrastructure include the entire range of from production to elaboration, storage, analysis and archive of data. They constitute a sustainable virtual working environment consisting of e-/webservices (data, computing, storage, processing, analytics, etc.) that supports full, data-intensive work processes for end-users in a specific domain or specific type of work.Services within an Infrastructure can be offered by partners as part of their business operations or by facilities maintained by the community with a business and sustainability model for any service to function:

  • Data channels to production services like gene and protein sequencing, chemical analysis, observation data production...
  • Big scientific data workflows (e.g., Galaxy, Taverna)
  • Data analysis Services
  • Semantic Support Services for Annotation and Analysis of data (vocabulary and ontology servers)
  • Data Discovery services, like registries for data sets
  • Data Storage and Archival Services
  • Services to research communities (extension to non-academic players for an open science development)
  • Capacity Development Services with Guidelines, courses….

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