Digital Twin-Based Concept for Reliable Research Data Management Integrating Proprietary Data Sources for Hyperspectral Imaging

Abstract

In data-intensive research, reliable management of research data is a major challenge. In the field of Mass Spectrometry Imaging, vast amounts of data are being acquired from mostly proprietary data sources. Consequently, hindering seamless data integration into Research Data Management systems. Without a data repository, the continuous generation of scientific knowledge and innovative research based on existing information is limited. Moreover, to maintain the value of data to researchers throughout and beyond its lifecycle, FAIR principles for reliable data management approaches must be applied. To enable the required data transmission, the Digital Twin paradigm can be considered a reliable solution. The conceptual implementation of a heterogeneous mass spectrometer generating hyperspectral images leverages the Digital Twin to overcome common data management problems in data-intensive research.