Abstract Although electronic nose technology has been studied for years, drift effects remain one of the major challenges. While ongoing research focuses on effective correction methods, the evaluation of these methods requires reliable and well-documented datasets. However, only a few drift datasets are available, some of which lack sufficient experimental detail or are outdated. This motivated us to introduce a new long-term drift dataset. It has been collected over 12 months using a commercial electronic nose, which is based on 62-metal oxide sensors. The measurements were conducted under controlled experimental conditions with three analytes (diacetyl, 2-phenylethanol, and ethanol) in different concentrations. The dataset consists of 700 time-series recordings, for which we provide both the raw data and a set of pre-extracted features. The data can support the development, evaluation, and comparison of methods for feature extraction and selection, as well as drift detection and compensation. By providing a comprehensive, well-documented dataset, we aim to advance research on sensor drift in electronic nose systems.
Wörner et al. (2025) studied this question.