Data FAIRification in a cross-institutional governance framework : recommendations from the ANR-BRIDGE project [poster]

2022

Colloque

Aventurier Pascal, Alaux M., Barthelemy C., Bonnet P., Hugo Catherine, Dzalé E., Deboin M.C., Decker Luc, Desconnets Jean-Christophe, Doux G., Fortuno S., Mougin C., Perez Jérôme, Sabot François, Szabo D.

Paris (FRA) ; Montpellier : INRAE ; CIRAD ; IRD, 2022, 1 p. multigr.

Plenary Meeting, Part of International Data Week, 19., Séoul (KOR), 2022/06/20-23

Context : Open science is a priority for the European Commission, fostering research data management policies and infrastructures. In France, the Second National Plan for Open Science aims to ensure that open science becomes a common and shared practice, encouraged by the entire international ecosystem of higher education, research, and innovation. In addition, the French Ministry of Research is building a national federated data repository : Recherche Data Gouv, as part of its Open Science Plan. Objectives In this context, three French research organizations, each maintaining an institutional data repository based on Dataverse software, initiated the BRIDGE project funded by the French National Research Agency following a special Open Science call. One challenge is that French research is mainly organized into joint research units : within a same laboratory, project or publication datasets can be managed by different research organizations, necessitating choosing between several institutional and disciplinary data repositories that can cause data scattering. Three priorities. The goal of the Bridge project is to provide guidelines and harmonize research data policies and repository management in a reusable approach for other institutes or contexts, focusing on : 1. Analyzing and improving institutional data governance policies 2. Providing and endorsing common guidelines for data producers and managers 3. Choosing FAIR vocabularies and developing IT tools to improve FAIRness of repositories with some shared metadata schemas. Main outputs : Define and adopt common rules for all the project partners. The DMP (Data Management Plan) must be built in the light of these rules. Overcome some difficulties related to the practical implementation of data FAIRication. For example : bringing data into standard; define sharing conditions, common rules for harvesting metadata repositories . Align with common ontologies and controlled vocabularies (data type, disciplinary fields, etc.) available via the semantic web.

Sciences fondamentales / Techniques d'analyse et de recherche [020] ; Développement scientifique et technique [116] ; Documentation [124]

Fonds IRD [F B010090478]

fdi:010090478