The proposals should focus on all following aspects:

• Develop and prototype tools to drive machine-actionability in repositories, data, and services, establishing a network of trusted repositories linked to the EOSC EU Node;
  • formulate open protocols and policies to facilitate effortless data access, transfer, processing, and provenance updates within EOSC’s repository and service network;
  • deliver AI-based capabilities to make interoperable AI/ML solutions and facilitate the setup of custom workflows for research data processing;
  • offer tools/services for automatic quality measures of inputs and outputs of the AI/ML models.
• Provide federated infrastructure services for serving AI models integrating horizontal and thematic EOSC nodes:
  • ensure capacities for AI model retraining and inference;
  • take into account the whole research data life cycle, including raw data retention before AI modelling.
• Provide access to an easy-to-use technology platform offering reference implementations and recipes to quickly get started working with AI/ML with limited engineering overhead:
  • promote and apply state-of-the-art AI/ML operational best practices;
  • validate reference implementations and share commonly used recipes within EOSC.
• Establish and/or provide access to existing AI/ML model repositories and services to serve models for retraining of generic models for specific needs for future predictions and reproducibility:
  • create AI/ML model repository and enhance FAIRness of existing AI/ML models;
  • offer services for utilisation of these models, including fine-tuning and inference, thus providing the foundational building blocks for the development of AI applications in EOSC.
• Establish an EOSC AI/ML competency centre for the pooling of expertise and coordinated support on AI/ML use of data, compute infrastructure and AI/ML models for the upskilling and technical support of EOSC users and research operators, as a strategic asset that will enable a new paradigm for science production.

The proposers should take into account and leverage on the results of relevant projects in the field, including EOSC Data Commons[1], and the other initiatives and projects contributing to the development of EOSC, especially in the area of machine-actionability and data FAIRification.