This course will teach you how to use the CI/CD procedures in DATAMITE to commit changes to the repository, from the developer's perspective. After finishing this course, you should understand the rules and pipelines of the project, while being able to configure the gitlab files and actively contribute to the DATAMITE project.
The main three objectives are to:
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Provide a basic understanding of the benefits of CI/CD.
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Familiarize with key concepts of CI/CD.
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Provide explanation of an actual CI/CD process of DATAMITE.
- Teacher: Giorgos N
This course provides a structured introduction to fairness in AI and data-driven decision-making, using probability, statistics, and supervised machine learning as foundations. Participants will learn how bias can emerge in datasets and models. The course combines conceptual understanding with practical demonstrations of a fairness analysis tool, enabling both technical and non-technical learners to confidently interpret fairness results and make responsible, data-informed decisions.
By the end of this course, learners will be able to identify fairness risks, interpret statistical and fairness metrics, and use a fairness tool to assess whether AI systems treat different groups equitably.
- Teacher: Prathyusha Sagi
The online course ‘Bringing synergy to better data management and research in Europe’ covers four themes: Open Science and EOSC, research data management, FAIR principles, and data management plans.
The main three objectives are to:
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Provide a basic understanding of EOSC and Open Science in relation to the EOSC Synergy project.
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Familiarise with key concepts and practical tools for FAIR data and data management.
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Raise awareness on best practices in research data management and equip learners with practical tools to embrace these practices.