Description
Research labs generate vast amounts of data capable of unlocking new opportunities in materials science when analyzed with specialized algorithms, yet most remains trapped on local data silos without contextual metadata, inaccessible for advanced analysis. At Helmholtz Zentrum Berlin, we developed a suite of Python applications that motivate scientists to actively use the NOMAD workflow. Our three-pillar approach transforms data collection from burden to asset, successfully transitioning researchers from isolated workflows to active NOMAD contributors.
Primary authors
Edgar Nandayapa
(Helmholtz-Zentrum Berlin)
Yousef Razegi
(Helmholtz-Zentrum Berlin)