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TIMe-ID consortium project (2025-2026)

Interdisciplinary work on integrated methodologies for temporal data in biology

The integration of temporal data in biology is a cutting-edge field of research that currently lacks operational methods. The purpose of the TIMe-ID consortium is to build an interdisciplinary community that can progress the development of such methods.

Context and key challenges

The integration of heterogenous data is a current focus for multiple scientific communities from a variety of scientific disciplines. However, the integration of temporal, or longitudinal, data in biology is still an emerging area of research for which effective operational methods have yet to be developed.

Logo consortium TIMe-ID

The TIMe-ID consortium project aims to bring together an interdisciplinary scientific community, at the interface between the experimental and formal sciences, that can identify and test integrative methods for temporal data based on a biological ‘tree’ model for black poplar.

 

Goals and methodology

TIMe-ID proposes to create a partnership of scientists from different disciplines (plant biology, genetics, mathematics, statistics, systems engineering) who have not as yet had the opportunity to work together, in order to address cross-cutting and cutting-edge methodological questions on the integration of temporal data.

To encourage dialogue and mutual knowledge exchange between these scientists from different communities, the two-year program plans to hold regular meetings:

  • An in-person kickoff meeting in Orléans for all participants, held in 2025 Q1, will include a presention of the scientific question and methodologies to be explored. It will also, importantly, allow partners to meet and share their projects and expectations. This event is co-funded by the Math-Vives PEPR.
  • Quarterly video meetings will allow regular discussion of results and methods relevant to the project.
  • A hybrid plenary meeting, to be held in 2026 Q1 in Toulouse, will consider the data and the scientific obstacles that must be overcome. It will also address the development of an experimental system of data production.
  • Last, prediction methods and their limits will be tested out on real data through a second-year Master’s studentship at the beginning of 2026.

The project will close with a reflective session to take stock of progress and agree a strategy to respond to project calls. A first draft will be produced for a prospective major project, based on the group’s discussions and on the experimental system developed by the consortium.

Contact - Coordination :

Participating INRAE units and external partners

INRAE units

DépartementsUnitésExpertises
ECODIVBioForAIntegrated biology, modelling, quantitative genetics, epigenetics, ecology and genetics of forest trees
BAPGQE - Le MoulonQuantitative genetics, statistics, omics data analysis 
BAPIJPBImage processing/analysis, integrated biology, 2D/3D(+t) modelling, biostatistics
BAPAGAPQuantitative genetics, biostatistics
GAGenPhyseBioinformatics, omics data analysis
SPEInstitut Sophia AgrobiotechSystems biology, AI, modelling
PHASEPRCDynamic systems, mathematical modelling, parameter inference, AI
AGROECOSYSTEMLBEBiostatistics
TRANSFORMStatSCMultiblock and tensor statistical methods, Bayesian statistics
MATHNUMMIATSpatial statistics, temporal statistics, statistical inference

External partners

InstitutExpertises
Université de Toulouse (IMT)Statistics, data analysis
AgroCampus Ouest (IRMAR)Statistics, data analysis, missing values, statistical machine learning 
Université Aix-Marseille / INSERM (MMG)Integrated biology
Université d'Orléans (PRISM)Analysis and processing of heterogenous multimodal data, imaging, modelling, AI
Université de Tours (LIFAT)Temporal data search, skyline problems, machine learning