Abstract
The recent environmental and climate changes stressed the need to redefine the way of producing food towards a more stable, healthier and more sustainable model for our society. In this context, the strategic French Program France 2030 [1] approved a 48,3 M€ funding in 2022 for the project Ferments du Futur (FdF) [2]. FdF is a unique public-private partnership entity, located over several sites: a technology facility spread across seven INRAE research units (MaIAGE, MGP, Micalis, SayFood, SPO, STLO, UMRF) specializing in microbiology, processes and bioinformatics, and an innovation facility located at Orsay, with equipment for screening, fermentation and prototyping of fermented foods. Each year FdF will request for new project partners to carry out multidisciplinary and novel research related to strategic topics of fermented foods. The selected partners will produce high quality data by taking advantage of the last cutting-edge technology established across the FdF structure. In this regard, the Migale bioinformatics facility, member of the FdF technology facility, plays a key role in leading the conception and development of the data warehouse, Siduri (named after the goddess of fermentation in Sumerian mythology). Siduri will gather public data and data produced within the FdF project in an efficient manner. The implementation of Siduri is conducted on the ontology-driven information system, OpenSILEX [3], an open source software suite developed at the research unit MISTEA (INRAE, Montpellier). OpenSILEX allows to create information systems designed to manage large amounts of experimental data by using modern web technologies. In addition, OpenSILEX is, by design, in agreement with the FAIR principles (Findable, Accessible, Interoperable and Reusable) required for the data management in Siduri. OpenSILEX has been successfully implemented through different use cases such as plant phenotyping (PHIS, Sixtine) and environmental depollution processes (EnviBIS). Nevertheless, for the development of Siduri, the expected data is prone to change over the time for each cycle of new funded projects. To address this challenge, a flexible and robust model of scientific objects is proposed to overcome the dynamic state of FdF. The types of data produced include: multi-omics data sets, microbial phenotype profiling, metabolic capacity, microscopy images, sensory analysis, among others. Theses generated data will be used to gain a better understanding on the development of novel process of fermented foods. Furthermore, the substantial volume and heterogeneous nature of produced data will be used to enrich predictive models that will foster the design of new fermented foods.