Volume 3, Issue 1 (2023) – 7 articles
Cover Picture: Materials-to-device integration for hydrogen technologies employs scale-to-scale and lab-to-lab methods to generate large amounts of highly heterogeneous data, calling for a flexible and interoperable data management. Using the EMMO ontology as a blueprint for a versatile and adaptable data model, we propose a native graph database for storing data from fabrication, measurements, and simulations. Mapping between the database and Python objects is established by the Django framework and its neomodel library. The data model can represent synthesis, characterization, fabrication and simulation data; its deployment in the virtualmind platform (https://vimilabs.com/) will facilitate data sharing and accelerate research by providing curated data.
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