Motivation

Lab notebooks provide the record of scientific experiments and their rationale. The lab notebook is a scientist’s legacy, a key artifact to enable replicability and a legal document for claims of ownership of scientific results. The notebook records not just the chemical or enzyme you use, but complete information to obtain the exact same reagent; and, when using instruments, the lab notebook should contain the type, name, location and serial number, and everything needed to configure it in the exact same way. Importantly, research ethics require us to capture experiments in full, including mistakes and outliers. A well kept notebook provides an accurate record of the thought process behind every step taken in the laboratory.

Electronic Lab Notebooks (ELN) integrate the usage of computers (and other digital technology like mobile devices) into the scientific workflow, offering many advantages with respect to data sharing, access and analysis. In the natural sciences, the combination of the ELN together with the underlying experimental sources in the research lab provide potentially highly valuable Big Science data, that we however do not yet fully grasp how to manage and take advantage of. Usage of a wide variety of instruments and their often complex configuration settings should mix fluently with the more traditional art of scientific notekeeping. In computing science and mathematics, especially data science, we observe increased usage of computational notebooks like the Jupyter Notebook; and, experimental scientists have expressed the need to combine key elements of ELNs and computational notebooks to record and store experimental results.

Increasing volumes of data involved in scientific experiments, concerns over reproducibility and regulations for improved research data management have all contributed to an increase in the take-up of electronic solutions for documenting lab work. Another trend to motivate scientists to consider a digitized laboratory environment is the rise of Artificial Intelligence for science. Given the important role of AI for science that we expect to see in the near future, the ELN would not only capture the scientific process, but also help automate it. Simultaneously, integration with Large Language Models or Foundation models can improve the efficiency of keeping track of the rationale behind the steps we take in the lab.