ArticleScientific data2025
Long-term drift behavior in metal oxide gas sensor arrays: a one-year dataset from an electronic nose.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Data-driven exploration of electronic nose technology to differentiate bacteria in blood cultures under biofilm-promoting conditions.Scientific reports · 2026Article
- A Non-Contact Electronic Nose System Based on Off-Gas Response for Real-Time NHSensors (Basel, Switzerland) · 2026Article
- Enhanced Humidity Sensing Performance of FeACS omega · 2026Article
- Drift-Robust Lightweight Deep Learning on Open Gas Sensor Benchmarks: A Reproducible Architecture Study with CBRN Applicability Mapping.Molecules (Basel, Switzerland) · 2026Article
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- Evaluation of an Electronic Nose Coupled with In Vitro Fecal Fermentation as a Screening Tool for Fecal Odor in Cats.Animals : an open access journal from MDPI · 2026Article
- Multimodal AI for Real-Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation.Food science & nutrition · 2026Review
- Long-term drift behavior in metal oxide gas sensor arrays: a one-year dataset from an electronic nose.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.