Electronic Lab Notebooks (ELNs) play a pivotal role in digitalizing laboratory procedures, supporting the improvement of experiment reproducibility and fostering transparent, traceable scientific practices with ease. ELNs can support the generation of FAIR (Finable, Accessible, Interoperable, Re-usable) data which offers key benefits in comparison to the manual tracking and documentation of research processes. Also, more and more communities become aware of the limited number of well structure data being available for the development of AI methodswhich hinders efficient data analysis in order to accelerate the scientific development in experimental sciences. Therefore, ELNs have evolved into essential tools for contemporary research facilities, streamlining data management, fostering collaboration, and documenting scientific endeavors. Notably, ELNs are endorsed by numerous funding bodies and communities for their sustainability and reproducibility benefits, leading to a growing uptake within academic circles. There are different types of ELNs in use. Generic or multidisciplinary ELNs typically support a discipline-agnostic approach, providing researchers with significant freedom in data storage methods and process documentation. While generic ELNs can be used across various scientific domains, they are often limited in providing well-annotated, structured and standardized data. Depending on the requirements of a specific discipline, limitations may arise, impacting the efficiency of scientific processes, the searchability and the reusability of results due to lessstructured data. Furthermore, there is a multitude of Electronic Lab Notebooks (ELNs) with specialized tools and a data structure well adapted to each respective discipline. The standards and processes prescribed by these discipline-specific ELNs are defined to effectively support and accelerate scientific work, and discipline specific ELNs prepare data storage in a suitable manner to ensure FAIR data. This translates to good direct reusability of the data and potentially prepares the data for long-term reusability and comparability with other data sources.