Human capital

Article VIII  of the Recommendation recommends that

“Adherents support the development of the human capital necessary to realise the full potential benefits of enhancing access to research data and other research-relevant digital objects from public funding.”

To strengthen human capital for data-driven research and innovation, policymakers should invest in skills development, training programs, and career incentives.

Defining new roles—such as data stewards, data scientists, and research software engineers—will help professionalise research data management (RDM). Integrating RDM and Open Science principles into academic curricula, expanding discipline-specific training, and fostering public-private partnerships can enhance workforce readiness.

Establishing competence centres, Open Science units, and international training initiatives will support cross-institutional skill development.

Certification programs and institutional frameworks should incentivise participation, while sustained funding is necessary for long-term capacity building.

To attract and retain talent, offering competitive career paths, improving working conditions, and recognising contributions to research infrastructures will be critical.

Encouraging collaboration across disciplines, developing diverse expertise, and engaging industry and government stakeholders can further enhance the sustainability of human capital in research data ecosystems.

Developing a skilled workforce for data-driven research and Open Science faces significant challenges due to fragmented institutional approaches, insufficient long-term funding, and a lack of standardised training curricula.

• The absence of robust evaluation frameworks and incentives for upskilling discourages researchers and support staff from engaging in training. 
• Open Science skills are often undervalued in academic career progression, further limiting participation.
• Discipline-specific training needs and rapid technological advancements make it difficult to develop flexible yet standardised programs that ensure interoperability across institutions.
• Additionally, academia struggles to attract and retain data scientists and research software engineers due to uncompetitive salaries, unclear career pathways, and limited recognition of data management contributions.

Overcoming these barriers requires systemic changes in funding, career structures, and institutional priorities to support sustainable skills development and workforce retention.

Human Capital pillar can be broken down into the following provisions

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

• 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;
• training a cadre of dedicated data managers and stewards with expertise in curation and stewardship of research data, as well as research software engineers;
• development of cohorts of researchers with advanced (e.g., PhD-level) data-intensive research and data science skills suitable to discipline-specific needs;
• 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 the training to accomplish their role;
• the continued acquisition of data literacy skills by citizens to enable them to make effective use of research data.

Develop appropriate learning and training programs and resources to: support lifelong learning and re-skilling where necessary, including:

• through the development and provision of  open educational resources and online learning, and making a sufficient number of quality digital training resources openly available so that they can be reused by other trainers;
• ensure inclusivity and diversity in data and software management and training in digital skills for research;
• raise awareness of  potential bias in datasets and resulting analyses and promulgate effective approaches to mitigate such bias;
• and involve the private sector in the creation of sustainable training resources, in particular in the development and co-financing of relevant programmes.

Attract and retain data scientists and research software engineers across the breadth of scientific disciplines, notably through developing:

• attractive career paths for data scientists and research software engineers within publicly funded research and innovation, including facilitation of transfers between scientific disciplines, as well as careers combined with private sector experience;
• and recognition and reward of data management skills and software development skills as high value added to publicly funded research and innovation.