Strategies for skills development

Identify gaps and formulate strategies to develop and maintain the diverse skills necessary for data-driven research and innovation and thereby support: 

a.development of basic competence among all researchers and students to appropriately manage and reuse research data and other research-relevant digital objects and extract knowledge from them; 

b.training a cadre of dedicated data managers and stewards with expertise in curation and stewardship of research data, as well as research software engineers;

c.development of cohorts of researchers with advanced (e.g., PhD-level) data-intensive research and data science skills suitable to discipline-specific needs; 

d.the improved understanding, by relevant policymakers and research management staff, of approaches for effective management of research data and other research-relevant digital objects, and, if necessary, the training to accomplish their role;

e.the continued acquisition of data literacy skills by citizens to enable them to make effective use of research data.

To address the skills gap for data-driven research and innovation, policymakers could consider the following measures:

• Define new roles and job profiles, such as data managers, data scientists, data stewards, and data librarians, to support Research Data Management (RDM) and stewardship.

• Integrate RDM into academic curricula, ensuring all researchers and students acquire skills in data sharing, curation, ethical/legal aspects, and Open Science. Examples like Bielefeld University’s RDM modules highlight successful integration into training programs.

• Expand discipline-specific RDM training to address unique needs and foster collaboration between research institutions and industry through internships and public-private partnerships.

• Invest in resources and prioritization of data repositories, supporting their development as critical infrastructure for research.

• Promote Open Science practices through forums, training activities, working groups, and knowledge-sharing initiatives to embed best practices across research communities.

• Develop international training programs, such as FosterOS, Open Science Massive Open Online Courses (MOOCs), or Operating Systems (OS) Handbooks, to build early career competencies and align skills development with institutional curricula.

• Encourage policymakers and research staff to develop expertise in effective RDM through targeted training programs to enhance governance and oversight capabilities.

Implementing strategies for skills development in data-driven research and Open Science is challenging: 

• Fragmented institutional approaches hinder cohesive training programs, requiring standardized curricula to ensure consistent skill development across institutions and disciplines. 

• The lack of long-term funding and evaluation frameworks discourage the development of institution-led training initiatives.

• It is difficult to develop efficient training programs that meet training needs for varied disciplinary requirements in data stewardship and Open Science practices.

• Institutional inertia and resource constraints impede embedding Research Data Management (RDM) training into academic curricula. 

• Research support staff lacks clear incentives for upskilling. 

• The lack of robust metrics to measure the impact of skills development on research quality and societal impact, demotivates researchers upskilling.