Understanding key challenges in digitizing and contextualizing experimental results

Recent advances in automated, high-throughput experimentation have enabled scientists to leverage machine learning methods and structured data in screening large parameter spaces. These approaches could be even more effective if supplemented by data collected in traditional experimental labs, where samples are handed off between collaborators at multiple processing and characterization steps. In these settings, unstructured experimental observations recorded in physical lab notebooks can provide context for data and metadata collected from a variety of instruments. Despite a number of electronic lab notebook products available in market, digitization of experimental notes remains a challenge due to low levels of adoption by researchers. In this talk, we present our findings from user research conducted in three different academic labs, on researchers’ behaviors and needs throughout the experimental process. We discuss methods based on human-centered design to guide the development of an easily adoptable solution that seeks to 1) integrate experimental notes into data-driven software platforms, 2) contextualize experimental data obtained from multiple sources, and 3) enhance knowledge transfer between collaborators, thereby accelerating scientific discovery in experimental labs.