Appropriate training programs

Develop appropriate learning and training programs and resources to: 

a. 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; 

b. ensure inclusivity and diversity in data and software management and training in digital skills for research;

c. raise awareness of  potential bias in datasets and resulting analyses and promulgate effective approaches to mitigate such bias; and

d. involve, where appropriate, the private sector in the creation of sustainable training resources, in particular in the development and co-financing of relevant programmes.

To implement this provision, the following actions can be considered:

• Incentivise participation by offering certifications for training, particularly for roles like Data Stewards, to ensure attendance and recognition of acquired skills. Address funding gaps for research support staff to access training programs.

• Develop new skills and programs to align with open science practices, including creating institutional training frameworks and services tailored to researchers’ needs.

• Establish competence centers (local, thematic, or digital) to coordinate and support skills development and training efforts across disciplines and institutions.

• Incorporate data management training into institutional programs, ensuring it is recognised as good practice and valued in research evaluations.

• Seed and sustained funding is essential for training initiatives, leveraging programs like Horizon Europe and European Open Science Cloud (EOSC) to expand offerings at the national level.

• Universities and research centres should create Open Science units to foster uptake, develop training materials, and provide ongoing advice.

• Promote inclusivity and diversity in training programs and integrate Open Science skills into master’s courses and institutional curricula, moving beyond ad-hoc workshops or seminars.

Developing suitable training programs is challenging:

• Many institutions lack long-term funding mechanisms to sustain continues investment in training programs for Open Science skills development. Seed funding may support initial implementation, but sustaining training programs over time is difficult.

• There is lack of robust evaluation frameworks to assess the impact of training programs on research quality and societal outcomes, motivating long-term investment and ensuring relevance.

• There is lack of incentives for researchers to participate in training. Open Science training is often seen as optional or secondary to researchers' core activities. 
  • Data management and Open Science skills are not integrated into institutional career progression frameworks. The absence of a standardised recognition or reward system for researchers who complete Open Science training is further discouraging.
• Different research disciplines require different Open Science skills (e.g., bioinformatics vs. social sciences). Creating flexible yet standardised Open Science training that suits different disciplines is a challenging task.

• It is difficult to ensure interoperability and compatibility of training frameworks across national and international institutions.

• Rapid technological advancements, such as Artificial Intelligence (AI) and emerging data tools, demand continually evolving training content.