Reproducibility of open research data (ORD) is both crucial and challenging. This is made even more difficult in mixed workflows involving both experiments and simulations, requiring seamless integration of tools and data. To this end, metadata schemas that are semantically annotated with well-defined ontologies are key to establishing interoperability. Under the Swiss project PREMISE (https://ord-premise.github.io), we address these challenges through the development of standards and protocols for metadata schemas. We also design a transactional capsule format for communicating these schemas and their associated raw data between electronic lab notebooks (ELNs) and workflow management systems (WFMSs). In this talk, we demonstrate our developments via two interoperability use cases. In the first use-case, the AiiDA¹ WFMS is used to run simulations of microscopies and spectroscopies, with the goal of elucidating experimental results and driving future experiment design. Here, we demonstrate how an AiiDA workflow is triggered directly from the openBIS² ELN, opening a custom AiiDAlab³ instance (a dedicated Jupyter-based AiiDA GUI), where simulations are prepared and executed. Results are then seamlessly attached back to the original data in openBIS once the simulation is completed. The second use-case, developed as part of the Battery2030+ BIG-MAP (https://www.big-map.eu) Stakeholder Initiative Aurora⁴, uses AiiDA to drive and monitor fully automated cycling experiments of robot-assembled coin-cell batteries, with the aim of delivering a FAIR-compliant platform for autonomous battery laboratories. Here, data and metadata of battery samples are fetched from openBIS into AiiDAlab, where cycling experiments are constructed and submitted, and results can be explored, visualized, and transferred back to openBIS. Using these use-cases, we discuss the challenges of handling samples and data provenance on equal footing in experiments and simulations, and of designing metadata schemas for battery digital twins and for cycling protocols.

¹ S. P. Huber et al., Sci. Data 7, 300 (2020); https://aiida.net

² C. Barillari et al., bioinformatics, 32, 638 (2016); https://openbis.ch

³ A. V. Yakutovich et al., Comp. Mat. Sci. 188, 110165 (2021); https://aiidalab.net

⁴ P. Kraus et al., ChemRxiv (2023); doi:10.26434/chemrxiv-2023-4vs5w