Introduction to Data Spaces

By iliad DTO



At 15:00
At 16:30


– Organiser: iliad DTO
– Language: English
– Virtual or in-person: Virtual

The concept of data spaces will be introduced along with examples of different thematic data spaces in Europe and how they differ and can be combined with data lakes


  • Welcome and introduction to ILIAD (Bente Lilja Bye, BLB)
  • Introduction to the data space concept (Arne-Jørgen Berre, SINTEF Digital)
  • Overview of data spaces in Europe – and the AD4GD project, (Joan Maso, CREAF)
  • TBD: GREAT – overview of data space and how it can be combined/used to support digital twins of the ocean

About Iliad Digital Twin of the Ocean

Iliad aspires to be an interoperable, data-intensive, and cost-effective Digital Twin of the Ocean
The Iliad Digital Twin of the Ocean, an EU funded project, builds on the assets resulting from two decades of investments in policies and infrastructures for the blue economy and aims at establishing an interoperable, data-intensive, and cost-effective Digital Twin of the Ocean.

About the AD4GD Project

AD4GD’s mission is to co-create and shape the European Green Deal Data Space as an open hub for FAIR data and standards-based services that support the key priorities of pollution, biodiversity and climate change. The focus will be on interoperability concepts that bridge the semantic and technology gaps which currently prevent stakeholders and application domains from multi-disciplinary and multi-scale access to data, and which impede the exploitation of processing services, and processing platforms at different levels including Cloud, HPC and edge computing.

This project will enable the combination and integration of data from remote sensing, established Virtual Research Environments and Research Infrastructures, Internet of Things (IoT), socio-economic data, INSPIRE and Citizen Science (CitSci) in an interoperable, scalable and reliable manner. This will facilitate integration by including semantic mappings to different standards and dominant models bridging domain- and data source-specific semantic concepts such as the Essential Variables framework, as well as applying machine learning and geospatial user feedback to ensure quality, reliability and trustworthiness of data and transforming spatial scales.