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Updated: May 31, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
Published on: November 22, 2019
Data management and sharing
Claude Pellen1, Nchangwi Syntia Munung2, Anna Catharina Armond3
1University Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Centre d'investigation clinique de Rennes (CIC1414), Rennes, France.
Abstract:
Guided by the FAIR principles (Findable, Accessible, Interoperable, Reusable), responsible data sharing requires well-organized, high-quality datasets. However, researchers often struggle with implementing Data Management and Sharing Plans due to lack of knowledge on how to do this, time constraints, and legal, technical, and financial challenges, particularly concerning data ownership and privacy. While patients support data sharing, researchers and funders may hesitate, fearing the loss of intellectual property or competitive advantage. Although some journals and institutions encourage or mandate data sharing, further progress is needed. Additionally, global solutions are vital to ensure equitable participation from low- and middle-income countries. Ultimately, responsible data sharing requires strategic planning, cultural shifts in research, and coordinated efforts from all stakeholders to become standard practice in biomedical research.
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