Earth and environmental sciences require a large panel and volume of data from satellite, in-situ observations, models, omics experiments...

Earth system domains are interconnected and even if interfaces between domains appear of primary importance for several studies with large societal impacts, such as climate change, agriculture and food, human safety and health, the present digital architecture is based essentially on distributed and domain-dependent data repositories inducing real difficulties for integrated uses of all the environmental data.

To go beyond this state-of-the-art, the overall objective of FAIR-EASE is to customize and operate distributed and integrated services for observation and modelling of the Earth system, environment and biodiversity by improving the TRL of their different components implemented in close cooperation with user-communities, the European Open Science Cloud (EOSC) and research infrastructures in their design and sustainable availability.

The project will:

• Improve a FAIR-EASE data discovery and data access service, relying on pre-operational existing services, in order to provide users with an easy and FAIR tool for discovery and access to environmental multidisciplinary and aggregated data-sets as managed and provided by a range of European data infrastructures
• Set up a FAIR-EASE Earth Analytical Lab, with EOSC connectivity supporting, through web-based interfaces, predefined processing tools and on-demand data visualization services for remote analysis and processing of heterogeneous data facilitating the cross-disciplinary collaboration, reducing the time to results and increasing productivity
• Develop a number of multidisciplinary Use Cases to contribute requirements for the FAIR-EASE system components and to validate and demonstrate the capabilities of the FAIR-EASE service for supporting open science.

The FAIR-EASE's ambition is to actively take part in the progressive implementation of a “Web of FAIR Data” through the integration and the customization of a set of services scaled up at a higher TRL that will allow scientists and machines to collaborate in storing, processing, finding, accessing and reusing scientific data in an open way.