Metadata vs Arsenic: a Case Study of Digital Library for the Conservation of Physical Collections

Abstract

Libraries worldwide have begun to uncover the presence of arsenic in their collections, posing risks to collections, staff, and the broader public. KB National Library of the Netherlands is at the forefront of addressing this issue, having tested a large sample of items for arsenic. Building on these testing practices and results, this study adopts a digital library and information science approach to explore how Exploratory Data Analysis (EDA) and Machine Learning (ML) can be integrated into library workflows to optimise the detection of toxic materials. Using a mixed method approach, the current workflow is traced via interviews with 12 staff members, revealing key issues including selection bias and fragmentation of data across non-interoperable systems. The final dataset of arsenic test results is then enriched with metadata, including 1,418 items with 9 features per item. EDA reveals patterns including a higher positive arsenic rate among 17th- and 18th-century items and notable regional differences. Random Forest (RF) and Gradient Boosting (GB) classifiers were trained on this dataset, both showing promising recall (70% and 69%, respectively) for arsenic-positive items. Based on these findings, we propose integrating data visualisations and a human-in-the-loop (HITL) ML system into existing workflows to support decision-making. This study demonstrates that digital methods such as EDA and ML applied to metadata can meaningfully support the detection of toxic materials in physical library collections.