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Supporting EOSC
Co-funded by the European Union
Co-funded by UKRI

Key Exploitable Results

The project will directly support EOSC Partnership Specific Objective 1.2. Professional data stewards are available in research-performing organisations in Europe to support Open Science, measured through two specific KPIs

  • KPI (2025) European curricula for data stewards are defined
  • KPI (2027) All research done by EOSC Association members is supported by professional data stewards.

Skills4EOSC actions address the three gaps identified in the EOSC SRIA concerning skills and training: a lack of Open Science and data expertise, a lack of clearly defined data professional profiles and career paths for these roles, and fragmentation in training resources.

Throughout its lifetime, Skills4EOSC will deliver the following Key Exploitable Results (KER):

  1. Definition of data stewards and Open Science related profiles and curricula

    Minimum Viable Skillsets (MVS) - Skills4EOSC will chart a comprehensive map of different career profiles and define for each one a MVS, i.e. a set of minimum requirements for competencies and proficiency levels tailored to a specific Open Science professional profile.

    Harmonised curricula and learning paths - Skills4EOSC will harmonise OS curricula and learning paths targeting researchers at different career stages, data professionals and policymakers and offering discipline-, thematic- and research infrastructure-oriented training. This action will allow for the creation of curricula for specific professional profiles that are recognised across Europe while addressing their different training needs. Based on the MVS defined for each target group, common underlying content will be adapted to cover topics and competencies for specific audiences.

  2. Bridging the fragmentation of training resources

    FAIR-by-design methodology for learning materials - Skills4EOSC will define a methodology to ensure the full compliance of training courses and materials to the FAIR principles, making them reusable for humans and machines. This will be achieved by providing rich contextual information about the organisation, delivery and assessment of training.

  3. Recognition of professional Open Science competencies

    A standardised soft certification mechanism will be defined: a quality assurance and certification framework for learning materials, and quality assurance mechanisms for professional training and qualifications.

  4. Availability of a large number of skilled professionals in Open Science

    Training-of-Trainers (ToT) allows for cost-effectively scaling up trainer numbers. Working via a network embracing 18 European countries, Skills4EOSC will bring an extra dimension of rigour to this approach.

    Lifelong learning through professional networks: OS is a quickly evolving domain and professionals need to refresh and update their competencies to work effectively. Skills4EOSC will harness professional and thematic networks of peers as vehicles for lifelong learning and building and sustaining the EOSC-ready digitally skilled workforce.

  5. Fostering the efficient uptake of relevant scientific data by public administration

    Designing and delivering “Science for Policy” courses targeting researchers, “honest brokers”, civil servants and policymakers. Workshops “The Practice of Informing Policy Through Evidence” will be organised: ToT sessions for the Competence Centres and pilots will be delivered.

  6. Building a Open Science Skills Commons by creating a Competence Center Coordination network and a European network for user support

    Skills4EOSC Competence Centre and support network. Competence Centres, or Data Competence Centres at the national and institutional level are widely recognised as key players to achieve the EOSC partnership’s objectives related to training. The creation of a broad network across Competence Centres is instrumental in aligning and sustaining the key outputs of the project (i.e. curricula, quality assurance and certification frameworks for skills and materials, professional networks, user support networks and ToT programmes) and setting up a user support network for the entire science workflow, ranging from FAIR data management tools, through data-intensive science and high-level techniques, including AI, up to the delivery of scientific results.