Ameliorating experimental reproducibility requires a proper data management plan involving the collection of experimental procedures, outcomes, and environment metadata from an SDL. Notably, we draw inspiration from the remarkable advancements in computational chemistry, where sophisticated data solutions have successfully ensured reproducibility in chemical simulations. To streamline data management, our orchestration device is equipped with two independent relational databases: one for experimental, and another for simulation data. The first is designed to store information about the laboratory state, including raw and processed data and metadata of the instruments, as well as the input and output of experimental processes. We implement this desired modularity in the database layout by defining key universal sub-tables, device, job, and devicelog, and more specialized ones tailored to the needs of each instrument. This concise layout ensures max-imum flexibility and a robust architecture for experimental data collection, that is readily transferable to any SDL. A detailed layout of the experimental database can be found in Note S1 and Figure . On the other hand, the simulation database is managed by AiiDA, which provides a welltested and robust design specifically tailored for simulation orchestration. These databases are not only used to keep track of all the data collected in the laboratory, but they also serve as a broker to share data between different devices and software running in the laboratory. ChemOS 2.0's databases can be easily exported and published into specialized databases to report complete experimental information. As such, we gain a more comprehensive snapshot of the laboratory, encompassing all instructions executed by experimental devices and their corresponding metadata.