Data-driven methods become more and more important in modern materials science and, in particular, machine learning and artificial intelligence need access to a large amount of high-quality data. Managing data according to FAIR principles, therefore, becomes a important aspect in laboratory management.

The transformation towards a modern data-centric experimental materials science laboratory faces many challenges from managing experimental samples, consumables, and scientific instruments, to capturing relevant data, automatic extraction of embedded metadata, and linking relevant information together. Additionally, users need to get into the habit of entering descriptions about their experiments and results, as well as data that cannot be captured electronically into an electronic laboratory notebook (ELN).

In this talk, we will discuss the current status quo and future roadmap of introducing openBIS as a combined Laboratory Information Management System (LIMS) and ELN at the Institute for Physical Metallurgy and Materials Science at RWTH Aachen University. We will demonstrate how data recorded by scientific instruments can be ingested automatically, extracting the relevant metadata, how this enables scientists to build data-driven workflows, and how data can be shared according to FAIR data principles both amongst research groups at the institutional level, as well as the wider community.