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This chapter describes a case study in using a combination of virtualization technology, Git as well as a continuous integration (CI) server to support sustainability of analytical pipelines. The case study was designed to reproduce one processing step in the analytical pipeline described in the paper “Taking a goal-centered dynamic snapshot as a possibility for local homing in initially naive bumblebees” [1]. In this paper, the researchers report their findings regarding the exploratory flights of bumblebees in unknown territories. Trajectories were recorded using two cameras and triangulated, yielding 3D trajectories of the flights. The original analytical workflow was implemented in MATLAB. As a result of Conquaire, the analytical workflow could be reproduced using Python, yielding trajectories that faithfully match the original trajectories. In Conquaire, we implemented an analytical workflow that relies on virtualization as well as on a continuous integration server. The main function of the virtualization is to preserve the computational environment so that it can be easily executed by third parties without the need to reproduce the exact computational environment nor to install any libraries. A continuous integration server was used to implement basic mechanisms for quality control over the data, leading to the discovery of some minor mistakes that could be directly corrected. The case study has demonstrated the usefulness of using a combination of virtualization and continuous integration to support analytical reproducibility in the natural sciences, neuroethology in particular.