Automated Metadata Enhancement for Physical Sample Record Aggregation in the iSamples Project

Abstract

ABSTRACT Large amounts of samples have been collected and stored by different institutions and collections across the world. However, even the most carefully curated collections can appear incomplete when aggregated. To solve this problem and support the increasing multidisciplinary science conducted on these samples, we propose a method to support the FAIRness of the aggregation by augmenting the metadata of source records. Using a pipeline that is a combination of rule‐based and machine learning‐based procedures, we predict the missing values of the metadata fields of 4,388,514 samples. We use these inferred fields in our user interface to improve the reusability.