NOMAD CAMELS—An Open-Source Solution for Creating FAIR Data from Experiments

In materials science and solid-state physics, a significant fraction of our science relies on highly specific self-written software for driving experiments, resulting in extremely heterogeneous data output. For facilitating homogeneous and metadata-rich research data, an easy-to use lab control software with a standardized output would be a crucial factor for data interoperability.<br/>NOMAD CAMELS (short: CAMELS)[1] is a configurable open-source measurement software created within Germany’s national research-data consortium FAIRmat. It is suited to control smaller or complex experiments and records fully self-describing experimental data. It has its origins in the field of experimental physics where a wide variety of measurement instruments are used in frequently changing experimental setups and measurement protocols. CAMELS provides a graphical user interface (GUI) which allows the user to configure experiments without the need of programming skills or deep understanding of instrument communication. CAMELS translates user-defined measurement protocols into stand-alone executable Python code for full transparency of the actual measurement sequences. Existing large-scale, distributed control systems, such as EPICS can be natively implemented. Metadata inflow from Electronic Lab Notebooks (ELNs) and data output into such is well supported for a seamless workflow. CAMELS is designed with a focus on full recording of data and metadata aligned with the NeXus ontology. Because CAMELS is open source, the community is welcome to contribute instrument drivers and alternative output data formats.<br/>When shared with others, data produced with CAMELS allow full understanding of the measurement and the resulting data in accordance with the FAIR (Findable, Accessible, Interoperable and Re-usable) principles.<br/><br/>[1] A.D. Fuchs, J.A.F. Lehmeyer, H. Junkes, H.B. Weber, M. Krieger, NOMAD CAMELS: Configurable Application for Measurements, Experiments and Laboratory Systems, Journal of Open Source Software, 9 (2024).