Abstract:
Scientific laboratory notebooks, particularly those in analog, handwritten
form, represent a significant yet underutilized data source for
computational studies. This paper reports on our research to further
develop a pipeline for transforming analog lab notebooks to AI-Ready digital
archives. The research is conducted within the framework for Computational
Archival Science (CAS), extending CAS principles, drawing from archival
practice and computational thinking. We provide background context on
laboratory notebook history and current day use, explore CAS as a framework
for study, followed by our research goals and methods. Automated extraction
results for table records found in the notebooks have an error rate under
5% on a per cell basis. The framework, methods, and our findings seek to
advance pipelines for making analog records, both historical and current,
accessible and curated for computational research. The findings presented
underscore both the accelerating pace of extraction technologies and the
importance of more structured, consistent analog documentation practices
to support computational transformation and AI-readiness. The conclusion
summarizes results and identifies next steps.
