Electronic Laboratory Notebook: An Adaptable Solution

Good research data management is essential in modern-day laboratory work. Various solutions exist that are either highly specific or need a significant effort to be customized appropriately. This paper presents an integrated solution for individuals and small groups of researchers in data-driven deductive research. Our Electronic Laboratory Notebook software generates electronic laboratory notebooks based on notes and files, which originate from one or several research experiments. The generated notebooks are then presented via a Django-based website. Automated gathering of metadata aims to reduce the documentation effort for the lab worker and prevent human error in the repetitive tasks of manually entering basic metadata. The software is provided as an adaptable framework. To use it, researchers must have basic Python skills to define data models for their specific experiments, using the included models as templates.