BigData, AI and Open Science

Extract from the report on page 10: "the development of AI is taking place in a technological context marked by the "datafication" of the world, which affects all fields and sectors".

Since the publication of Cédric Villani's report "Giving meaning to Artificial Intelligence" in March 2018 as part of the National Strategy for Research in Artificial Intelligence of the Ministry of Higher Education, Research and Innovation, incentives for sharing and opening up data and digital resources have multiplied both in the context of Research and Industry.

We can take up many of the challenges that arise from this:

SCIENTIFIC CHALLENGES

Regardless of the discipline, data are a source of new knowledge production and progress, but the process to achieve these results requires the proposal/appropriation of standards, preparation and sharing of data for analysis by AI approaches but also for research in AI and more generally in Data Science.

Economic Issues

    The expected innovations will be a promising lever for economic growth for those who will develop them; the national research strategy in France with the PIA3 call for Interdisciplinary Institutes of Artificial Intelligence (the 3IAs) in March 2018 has created a synergy between Research-Training and Companies in order to accelerate the valorisation of this research and training within our French economy.

Regulatory issues

The "Plan national pour la science ouverte" announced by the Minister of Higher Education, Research and Innovation in July 2018 and the European and national requirements in terms of defining data management plans for research projects are imposed on the academic world in compliance with the General Regulations for Data Protection (RGPD) and the Protection of Scientific and Technical Heritage (PPST).

Structural and technical issues

For several years, the academic community, gathered around initiatives such as the Research Data Alliance (RDA), the European Open Science Cloud (EOSC), but also within our national organizations and institutes, has been organizing infrastructures for sharing and archiving, labeling, certification and adoption of norms and standards for research data (HAL, W3C, FAIR principle "Easy to find, Accessible, Interoperable, Reusable"...).


This package is a powerful means of fulfilling major public missions, meeting the future programmes of Horizon Europe and the 17 UN Sustainable Development Goals (SDOs).

The Occitania region, like many others, has major assets in terms of data production and exploitation, particularly research data, and can assert itself at the national, European and international levels as an "authority" of quality data meeting the FAIR principles. The Occitanie Data consortium contributes to the development of an ethical and responsible AI in the Occitanie Region.

The research of the University of Toulouse organized around its six research poles is already engaged in this dynamic with the 3IA ANITI, its poles of Very Large Research Infrastructures such as HumaNum, PROGEDO, Data Terra, the GIS Genetoul, its University Research Schools (such as NEXT, ...), its LabEx (such as SMS, CIMI, ...) and its shared scientific strategy.