We present an overview of research data management workflows including multidimensional characterization techniques using the NOMAD platform. Our approach focuses on efficiently handling rich metadata as well as large-volume datasets—particularly in HDF5 format—and on the development of specialized NeXus application definitions for emerging characterization methods. We demonstrate how cloud-based analysis tools can be seamlessly integrated into the entire workflow, illustrated by examples from microscopy and spectroscopy techniques (electron microscopy, multidimensional photoemission spectroscopy, Raman spectroscopy, etc.). By leveraging customized JupyterLab environments and desktop-based tools in the cloud, this strategy supports efficient, advanced analyses that remain tightly integrated with the NOMAD data management infrastructure, eliminating the need to relocate large datasets and enhancing data share-ability. Furthermore, we show how instrument inventories and sample metadata can be linked to specific measurements, enabling robust traceability and a comprehensive history throughout the entire materials development lifecycle.