Researcher Cell Biology and Immunology / Wageningen University & Research Ede, Gelderland, Netherlands
Introduction: Polymeric-based nanoparticles have been extensively designed and developed over recent decades, including by our group. Despite the rapid growth of high-impact publications and technological advances, the field still lacks robust parameterization, standardized data reporting, structured organization, accessibility, and functional feedback mechanisms. We propose a data-driven platform enabling systematic storage and rapid retrieval of nanoparticle protocols and outcomes, including both successful and failed formulations, to support predictive design, peer-reviewed data sharing, and collaborative nanoparticle development, particularly for biomedical applications.
Learning Objectives:
Dataset compilation to keep systematic records in loop-feedback learning for nanoparticle design.
Fast learn, predictive, and resourceful improvement based on data analysis of sucessful and unsuccessful protocols
Construction of a peer-reviewed open database enabling learning, cross-cooperation and analysis of community data
