This presentation was given as part of the EOSC Stakeholders Forum Scientific Community Workshop which gives the opportunity to prospective consumers and providers - from both the public and commercial sectors - to discuss needs and opportunities that should drive the definition of the EOSC service portfolio roadmap. This workshop aims to answer the following: - What prospective needs and priorities do scientific communities have as consumers of the EOSC? - How should EOSC facilitate the sharing of data, scientific outputs and services across national and organisational borders? The workshop starts with a presentation of today’s state of play in federating resources and services to enable multi-disciplinary science and transnational access; it featured invited talks from representatives of digital infrastructures, research projects and communities and the long-tail of science. The workshop also involved representatives from both public and commercial organisations in their role as a service provider and service consumer. The workshop participants also took the opportunity to define a list of recommendations giving direction to the development and provisioning of the EOSC portfolio.

1. ELIXIR’s mission is to operate a sustainable
European infrastructure for biological
information, supporting life-science research
and its translation to society, the bio-
industries, environment and medicine
ELIXIR’s strategy is to connect national
bioinformatics centres and EMBL-EBI into a
distributed infrastructure build from coordinated
national and international data resources, tools and
services

2. What are the ELIXIR services?
ELIXIR Platforms bring
coordinate services provided
by our nodes
Beacons/Local EGA – Federated
Genomics Discovery & Query
services
ELIXIR / Life Science AAI
ELIXIRCommunities bring
together Node experts and
users around standards and
ELIXIR services
Plant Community:
Standards and services for
federated plant
phenotyping data

3. ELIXIR in 2023:
‘Well-connected and collaborating
Nodes allow users to access and
extract knowledge from large and
distributed life science data sets’