Major innovation axes

    Data Management Plan

    The IFB is committed to promoting open science by developing resources to make life and health science data findable, accessible, interoperable and reusable (FAIR). This includes writing Data Management Plans (DMPs) during the initial design of research projects, and evolving them from a static document to a programmatic, machine-driven tool to streamline data flows between different hosting locations (data production, rapid storage during the analysis phase, medium-term security, and long-term repositories). The DMP will also be articulated with the electronic laboratory book, which offers life scientists a user-friendly and powerful interface to record their research, from bench experiments to computer analyses. 

    The IFB envisages the deployment of data brokering services to help life scientists deposit their data in national and international repositories in standardized formats. The IFB also encourages the development of a FAIR culture, by training life scientists to access, share and use scientific data. 

    The IFB aims to create software environments better adapted to open science, notably by advancing new methodologies (continuous integration, code versioning, functional testing) but also by deploying modular and versatile technologies (Ansible, Conda, Containers). In particular, the aim is to transform data management plans from a static document into a living tool, powered by access to maDMP programs, systematically invoked at the main stages of a research project: allocation of digital resources, definition of access rights, data transfers between production, analysis, preservation and deposit sites. This development will be carried out in synergy with the international efforts undertaken by ELIXIR in the framework of the ELIXIR-CONVERGE project.

    • Modular DMPs: laying the foundations for a general framework enabling life scientists to manage their data transparently at every stage of their projects. 
    • MaDMP: generalization and orchestration of data flows via MaDMP throughout their life. Using MaDMPs to smooth data flows between the different stages of the data life cycle will commit the life sciences community to adopt FAIR (Findable, Accessible, Interoperable and Reusable) principles at every stage of their projects. MaDMP will concretely transform data management into a dynamic process, streamlining data flows between the different sites hosting the data at their different stages (production, analysis, public release). 

     

    Since March 2020, the IFB has initiated a project to develop machine-readable data management plans for life sciences (maDMP4LS). This project helps to manage the allocation of resources (calculation and storage) through programmatic access to the data of the management plans deposited in the DMP OPIDoR at INIST This will lead researchers to design data throughout the research life cycle. It will also transform the DMP into a dynamic and adaptable document system that will be updated during the course of research projects to request additional resources as soon as the evolution of the project requires it. 

    maDMP4LS

    Funded by the ANR, and in collaboration with INIST, the IFB has launched the maDMP4LS project which aims to “put the automated data management plan in the hands of biologists”. This project envisages the deployment and integration of the Data Management Plan (DMP) in the scientific process through a software integration of DMP OPIDoR, a DMP design tool, with the IFB's storage and calculation infrastructure and with the CeSGO platform which federates tools for collaboration, project management, data storage and retrieval. The result is a platform of computer tools interconnected with PGD, thus making it a "machine-actionable" (maDMP): the PGD feeds the data processing and management tools, and is in turn updated by these same tools. It is thus synchronized with the project throughout its life cycle. Through this project, the IFB wishes to simplify the monitoring of data management and storage, but also to increase the degree of conformity of the data with FAIR principles. This approach will allow the DMP to be promoted as a tool to assist in the production and exploitation of scientific results rather than as an administrative burden. In addition, the transmission of good community practices through the DMP will make it possible to define an environment conducive to the "FAIRification" of data. In the longer term, it is envisaged to interface DMP PIDoR with other software elements and services involved in the data life cycle and to transpose this concept of integration to other areas of research. In this way, all elements of the DMP will be implemented and FAIR will then become a reality for open and accessible science.