GEO Data Terra Occitania
Development of a spatial data infrastructure and distributed services allowing large volume storage, openness (FAIR) and services (Data Lake) for processing old, current and future spatial data, for the benefit of scientific communities, public and private actors in a regional (depending on the priorities of the Occitan Region), national (Ir DATA TERRA, INFRANIUM, GENCI/CINES), European (EOSP, Copernicus) and international (Mediterranean, Southern countries) context.
Coord. CNES; partners of IR data Terra
strengthening in Occitania the equipment of the DINAMIS/THEIA (THRS data) system and services of the Theia cluster within IR Data Terra to reinforce the use of satellite imagery in the digital and agro-ecological transformations of territories.
Instrumentation and observation services (SDU and EO).
New instruments deployed in the regions to understand and respond to global change. A project that takes off in three stages:
- astronomical time
- the weather of climate change
- times of crisis
Data Science and AI infrastructure of Bretagne Océane
The strong growth in the volume of observation, genomics, imaging and simulation data, both for major technological (e.g. defence, digital) and societal (e.g. environment, climate, health) challenges, is accompanied by increased needs in processing capacities to fully exploit the potential of these masses of data.
As an example, the marine component of IR DATA TERRA to which this project contributes foresees a growth of more than 50% in the volumes of data to be considered (at least) in the coming decade.
In this context, this project aims to respond to three types of issues:
– preservation issues – how to store, archive, validate and make accessible the acquired data?
– networking issues – how to make the masses of data and processing means accessible and interoperable at regional, national and international levels?
– Knowledge and services issue – how to support the creation of knowledge and services based on the manipulation and exploitation of these masses of data?
The distributed infrastructure proposed by the AIDA project will make it possible to absorb the growing flow of data, to make available high-performance, agile and interactive processing means and to support the emergence of a data-driven ecosystem of knowledge and innovation, in relation to several research infrastructures such as DATA TERRA? IFB, EMBRC FRAnce, Data Health Hub.
Ile de France
The project aims to strengthen the means of aggregating, processing and making available data from a large number of structured and unstructured sources in the fields of climate and energy to support research and innovation in the field of climate mitigation and energy transition.
Energy4Climate data centre (Energy4Climate datahub), coordination Institut Polytechnique/LMD, AERIS/ESPRI.
CINAuRA project – Convergence of higher education digital infrastructures in the Auvergne – Rhone-Alpes region and deployment of an eco-efficient digital research platform.
It proposes the deployment of a very high-level and eco-efficient regional digital infrastructure, meeting the current and future needs of the ESR AuRA, and falling within the national and European context of structuring digital instruments for research.
Haut de France Region
Project CLIMENSE :
Climate Change: Environmental Impacts on the Health of Organisms and Ecosystems – CPER (2021-2026)
(extension of a previous CPER)
2 Transversal Actions out of 4 are piloted by the ICARE/AERIS CDS: Data acquisition and analysis, ambition: to develop a complete integrated platform for analyses at the regional level allowing the coupling of observations and modelling at different scales.
20 million (including 4 from own funds); need for ICARE/AERIS/Data Terra in terms of material/infrastructure investment: minimum 400 K euros to be supplemented by requests for ERDF credits (in discussion with the region).
It is led by computer science teams and laboratories (specialized in AI and communication technologies): demonstrators of the use of AI for Big Data processing based on 2 examples:
On-board information reduction system on space platform to reduce the need for data transfer between ground and satellite (on-board intelligent decision making for the identification of intense events by satellite);
In-board information reduction system on a space platform to limit the need for data transfer between ground and satellite (on-board intelligent decision making for the identification of intense events by satellite) ;
Semi-supervised learning system for the exploration and classification of extreme events based on crowdsourcing strategies: classifying different atmospheric phenomena resembling typical examples (cyclone, volcanic plumes, etc.) in order to continuously teach an AI to recognize different elements in satellite images and to automatically index the data archived by the ICARE/AERIS CDS.