Information Analysis and Management (IAM) Node

The “Information Analysis and Management” (IAM) node is a transversal node of the national infrastructure “France Life Imaging (FLI)”.

FLI is a proposed large-scale research infrastructure project aimed at establishing a coordinated and harmonized network of biomedical imaging in France.

This project was recently selected by the call “ investissements d’Avenir « Infrastructure en Biologie et Santé”

The “Information Analysis and Management” (IAM) node is a consortium of teams that will contribute to the construction of a network for data storage and information processing. Instead of building yet other dedicated facilities, the IAM node will use already existing data storage and information processing facilities (LaTIM Brest; CREATIS Lyon, CIC-IT Nancy; Visages INSERM/INRIA Rennes; CATI CEA Saclay; LSIIT/Icube Strasbourg) that will increase their capacities for the FLI infrastructure. Inter-connections and access to services will be achieved through a dedicated software platform that will be developed based on the expertise gained through successful existing developments.

The IAM node has several goals. It aims first at building a versatile facility for data management that will inter-connect the data production sites and data processing for which state-of-the-art solutions, hardware and software, will be available to infrastructure users. Modular solutions are preferred to accommodate the large variety of modalities acquisitions, scientific problems, data size, and adapted for future challenges. Second, it aims at offering the latest development that will be made available to image processing research teams.

Through web services, the software platform will offer the users with (a) transfer, storage, access control, indexing and retrieval of data produced at distributed sites, (b) integration and management of data processing plug-ins, (c) building, description and sharing of workflows, and (d) workflow deployment on high performance computing facilities to process large databases. Hardware facilities will enable workflows to scale up from pilot studies to clinical studies on cohorts.

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