The research group Data and Knowledge Sharing and Integration, implements new digital environments enabling interoperability, data integration and knowledge extraction within heterogeneous, multi-modal and silo-managed data masses. It is part of knowledge engineering and AI. The objectives of this group are to ensure

1) the storage and preservation of observation data in a sustainable manner and in secure environments

2) Interoperable spatio-temporal and thematic representations for interdisciplinary analysis

3) The reproducibility of analysis (modeling, simulation, visualization) involving the cross-referencing of data and multidisciplinary algorithms

It pushes the boundaries of research in computer science and AI, in order to develop methods, and their technological declinations if necessary, at the interface of multiple disciplinary fields. In addition, we undertake a study on the data life cycle in order to position the work of this group between the phases of observation, analysis and representation of results.