Year: 2025
Type: article
Abstract: ObjectiveThe QUality Analyser for Interpreting Linkage (QUAIL) is a project that aims to provide a package of recommended researched methodologies and tools to streamline the quality assurance process of linked data. We aim for QUAIL to be as generalisable and as automated as possible. Standardised, yet suitable for a variety of linkage contexts. MethodsWe will highlight key processes and considerations made in the development of QUAIL and its sub-modules. These include candidate link creation for the purpose of false positive and false negative estimation, stratification, sampling, clerical review, and the computation of key quality metrics, including precision and recall. Results and ConclusionResults will focus on our recommendations based on our evaluation of each of these modules separately, and in conjunction. We will highlight the advantages of using QUAIL to facilitate quality assurance of linked data. This includes reducing reviewer burden with easy-to-use tools, while simultaneously enhancing the accuracy, reliability, efficiency and reproducibility of quality reporting.
Authors: Leah Maizey, Matt Wray, Laszlo Antal, Gavin Thomson, Tim Gammon, Jack Linaker
Institution: Office for National Statistics
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,747
OpenAlex ID: W4413775520