Data science and engineering

Data, computing, and methods constitute the three major cornerstones for launching a digital transformation strategy across the entire landscape, spanning from 5P medicine to One Health. However, this transformation faces significant challenges due to the sheer volume and heterogeneity of data, the fragmentation of digital resources, and the potential disconnect between scientific disciplines, training programs, and professional networks.

A comprehensive data hub. Cutting-edge science will rely on a fully integrated data collection and management program—linking quantitative, qualitative, historical, prospective, and multi-scale data—to strengthen ties between academic research, healthcare institutions, and policymakers. Relevant data types include electronic patient records, imaging, biological data, sequencing results, and environmental and social science data. The initiative will capitalize on Lyon’s unique landscape of expertise in digital health and engineering (telemedicine, connected devices, population and environmental sensors, etc.) within a “One Health” framework. The data center will enable a network of environmental, human health, and animal health agencies to share and exchange medical data based on FAIR principles and ensure data interoperability.

To address these challenges, SHAPE aims to establish a comprehensive scientific value chain—spanning from data collection to the generation and implementation of knowledge—by creating a data repository based on an integrated digital infrastructure and data center at the LyonTech-la Doua campus. These facilities will serve as a strategic asset around which to attract, structure, and integrate a diverse community comprising researchers, engineers, hospital and socio-economic stakeholders, and training programs. At every level, digital transformation will make transdisciplinarity a key strategy, fostering feedback loops and cross-fertilization between fundamental research and translational output.

The acquisition, availability, quality, and diversity of data analyses all entail significant methodological challenges. Regarding acquisition, we will rely on local engineering experts who possess health and environmental data, and we will develop sensors, smartphone applications, and innovative tools that also enable citizen participation in data generation and collection. Processing large, heterogeneous, spatially structured, and multi-scale datasets will require mathematical, statistical, and computational developments, involving computer scientists specializing in artificial intelligence and machine learning, data analysis, modeling and simulation, imaging, and more.