Abstract:
Collections of analog lab notebooks are an invaluable source of data about
research conditions, steps, and outcomes, and in aggregate have the
potential to provide new insights into the successes, failures and pedagogy
of research laboratories. Unfortunately, these artifacts are increasingly at
risk of being lost from the historical scientific record, given limited
archiving and an absence of computational and AI readiness. This paper
reports on research addressing this challenge by testing mechanisms for
transforming digital scans of analog lab notebooks into AI-ready data
resources. The research being pursued is framed by the field of
computational archival science (CAS) and the aim to utilize analog, research
lab notebook data for scientific study. The paper presents background
context on archival lab notebooks and CAS, discusses MOF (metal organic
frameworks) and COF (covalent organic frameworks) synthesis – the scientific
domain of the lab notebooks under study, and details our research methods.
We demonstrate a promising approach that automatically segments pages into
discrete entry types, extracts the contents of those entries, refines the
output and assesses the automated results. These efforts represent a first
step towards developing a framework for both improving the usability of
archival lab notebooks, and enabling their contents to be used in subsequent
scientific inquiry.
