Introduction to Data Spaces for Data Providers

“Introduction to Data Spaces for Data Providers” is primarily aimed at data providers and other energy stakeholders with potential interest in trustworthy and efficient data sharing. The learning goals of this course are a) to spread knowledge about the concept of data spaces to a wider audience and b) to assist interested parties in data sharing. The course offers details about data spaces, their benefits and the associated roles of participants in the ecosystem. The DATAMITE platform is used to showcase the preparation of data products and to demonstrate the sharing of metadata in an Energy Data Space catalogue. To support the learning process, quizzes are added after each section while screenshots illustrate the steps of each process. Links to a wide variety of resources are also provided. 

Introduction to Data Spaces for Data Consumers

“Introduction to Data Spaces for Data Consumers” targets individuals or organizations that require data for various purposes (e.g., research, ΑΙ training datasets). Apart from providing general information about data spaces, this course explores the topic of obtaining data using an Energy Data Space. The DATAMITE platform is employed to showcase the steps required from onboarding to a data space to downloading data products. Learning is assisted with visual media and quizzes.

Pontus-X marketplace course

This course is providing introduction to Pontus-X Ecosystem and DATAMITE integration with it. It will provide basic information about the Pontus-X, introduce common terms like various roles and concept of wallet. Showcases of Pontus-X marketplace will be shown as well as process of publishing new asset through the Pontus-X portal. Finally a DATAMITE framework and it's integration with Pontus-X will be shown. Broker plugins concept of DATAMITE and Pontus-X solution implementation will be briefly discussed. The DATAMITE showcase will present creation of datasets, data products and publishing them on the Pontus-X marketplace.

Introduction to Fairness in AI

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.

How to use CI/CD procedures in DATAMITE

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:

  • Provide a basic understanding of the benefits of CI/CD.

  • Familiarize with key concepts of CI/CD.

  • Provide explanation of an actual CI/CD process of DATAMITE.

Bringing synergy to better data management and research in Europe

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:

  • Provide a basic understanding of EOSC and Open Science in relation to the EOSC Synergy project.

  • Familiarise with key concepts and practical tools for FAIR data and data management.

  • Raise awareness on best practices in research data management and equip learners with practical tools to embrace these practices.