Dataset of the study "An empirical study on architectural smells through a pipeline for continuous technical debt assessment" Abstract In recent years, researchers spent an increasing amount of effort investigating technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic. However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.In response to this concern, researchers strive to expand the scope of their studies by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study. To address this issue, this paper presents an approach for tackling this problem and enabling researchers to study architectural smells, a well-known indicator of architectural technical debt, at a large scale. Specifically, we introduce a novel approach to a data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets with any static analysis tool. Finally, we use the data collected through the pipeline to study the correlation between architectural smells and logical coupling in order to understand how smells influence maintenance efforts.