The increasing demand for FAIR (Findable, Accessible, Interoperable, Reusable) research data challenges experimental scientists to integrate structured data management directly into daily laboratory work. However, practical adoption is often hindered by high documentation effort, limited usability, and insufficient integration with experimental workflows. We present FAIR-Save (FS), a Python-based user-centered suite that extends the electronic lab notebook Kadi4Mat with controlled vocabularies (VocPopuli), guided record creation (FS-DigitalBook), quality checks (FS-Validator), and seamless instrument integration (FS-Instrument). The suite captures metadata at the bench, supports offline work, and generates QR code-based labels to ensure error-free linking of samples, processes, and files. By using simple vocabularies rather than complex ontologies, FAIR-Save accelerates the creation of consistent metadata while fully complying with FAIR requirements. The activity of our research group demonstrates the impact of the FAIR-Save suite’s graphical user interface through the increased number of yearly recorded processes from 45 to over 2,000. The introduction of both FS-DigitalBook and key features for visualization significantly improved user adoption. FAIR-Save reduces documentation burden and improves data consistency. These results demonstrate that a user-friendly, automated system can substantially raise the adoption of FAIR principles, thereby supporting open-science objectives and accelerating data-driven discovery.
Flachmann et al. (Thu,) studied this question.