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ShareScore release 0.9.0
Dataset results
23 results for “electronic nose”
Staircase thermal modulation electronic nose dataset
<p>The dataset has 99 rows, corresponding to 99 odor samples, their labels are shown in label.xlsx</p> <p>The dataset has 450 columns, corresponding to the responses of 30 odor sensors under 15 heating voltages (2.6V-5.4V).</p> <p>Taking the first row as an example, the first to 15th elements correspond to the response values of sensor No.1 at heating voltage range from 2.6 V to 5.4 V in 0.2 V increments; The 16th to 30th elements correspond to the response values of sensor No.2 at heating voltage range from 2.6 V to 5.4 V in 0.2 V increments; And so on.</p> <p>This dataset is supplied by School of Microelectronics and Communication Engineering, Chongqing University and Chongqing Key Laboratory of Bio-perception Intelligent Information Processing.</p>
Predicting the crossmodal correspondences of odors using an electronic nose
<p>Odour Recordings</p> <p>There are 100 recordings in total of 10 different essential oils; five were from Mystic Moments™ (caramel, cherry, coffee, freshly cut grass, and pine) and five from Miaroma™ (black pepper, lavender, lemon, orange, and peppermint).<br> Each recording is 10 minutes in duration (600 seconds). Columns in each of the .csv files are in the following order: time, air quality, pollution level, temperature, pressure, humidity, gas, MQ3, MQ5, MQ9, and HCHO. The file's name denotes the odour being recorded and the record number (1 - 10).<br> For more information, please view the publication - R.J. Ward, S. Rahman, S.M. Wuerger, A. Marshall, Predicting the crossmodal correspondences of odors using an electronic nose, Heliyon.</p> <p>Perceptual Data </p> <p>The underlying perceptual data used from R.J. Ward, S.M. Wuerger, A. Marshall, Smelling Sensations: Olfactory Crossmodal Correspondences, J. Percept. Imaging. 4 (2021) 1–12. https://doi.org/10.2352/j.percept.imaging.2021.4.2.020402.<br> The data used from the later paper is the (angularity of shapes, smoothness of texture, perceived pleasantness, pitch, and the colour ratings in L*a*b* space).</p> <p>Each file contains the raw perceptual ratings for the ten different odours (columns) from sixty-eight different participants (rows) in the following order: black pepper, caramel, cherry, coffee, freshly cut grass, lavender, lemon, orange, peppermint, and pine.<br> NOTE: the pitch ratings only contain data from sixy participants due to it being added to the experiment at a later date.</p> <p>The folder regression models contains the required MATLAB code to train and test the regression models for predicting the crossmodal correspondences of odors using physicochemical data.</p> <p>The folder raw perceptual data contains the raw unprocessed perceptual data in .csv format.</p> <p>The folder e-nose code contains the code to drive the Arduino circuit and additional libraries required by the sensors.</p> <p>The folder e-nose recorder contains a Unity project and code to receive the UDP packets from the e-nose and log them.</p> <p><br> If you use this data please cite the following papers;</p> <p>Perceptual Data<br> R.J. Ward, S.M. Wuerger, A. Marshall, Smelling Sensations: Olfactory Crossmodal Correspondences, J. Percept. Imaging. 4 (2021) 1–12. https://doi.org/10.2352/j.percept.imaging.2021.4.2.020402</p> <p>Chemical Data<br> R. Ward, S. Rahman, S. Wuerger, A. Marshall, Predicting the colour associated with odours using an electronic nose, in: 1st Work. Multisensory Exp. - SensoryX’21, 2021: pp. 1–6. https://doi.org/10.5753/sensoryx.2021.15683.<br> R.J. Ward, S. Rahman, S.M. Wuerger, A. Marshall, Predicting the crossmodal correspondences of odors using an electronic nose, Heliyon, (under review as of file upload).</p>
Identifying volatile metabolite signatures for the diagnosis of bacterial respiratory tract infection using electronic nose technology: a pilot study
<p>Datasets for the <strong>Identifying volatile metabolite signatures for the diagnosis of bacterial respiratory tract infection using electronic nose technology: a pilot study. </strong></p>
eNose-TB: Electronic Nose for Tuberculosis Screening
ClinicalTrials.gov study NCT04567498. IPD Sharing: NO. Countries: 1. Publications: 1.
Detection and Use of Nasal Nitrous Oxide and the Electronic Nose
ClinicalTrials.gov study NCT02476929. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Can the Electronic Nose Smell COVID-19 Antibodies?
ClinicalTrials.gov study NCT04475575. IPD Sharing: YES. Countries: 1. Publications: 7.
Detection of Colorectal Cancer in Patients With a Positive Fecal Immunochemical Test Using an Electronic Nose Device (AeoNoseTM)
ClinicalTrials.gov study NCT03346005. IPD Sharing: NO. Countries: 1. Publications: 2.
Breath Analysis Using an Electronic Nose in Non Alcoholic Fatty Liver Disease
ClinicalTrials.gov study NCT02950610. IPD Sharing: NO. Countries: 1. Publications: 6.
Electronic Nose for Diagnosis of Neurodegenerative Diseases Via Breath Samples
ClinicalTrials.gov study NCT01291550. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Can the Electronic Nose Smell COVID-19?
ClinicalTrials.gov study NCT04475562. IPD Sharing: YES. Countries: 1. Publications: 6.
High-speed odour sensing using miniaturised electronic nose
Open the record for dataset details and reuse information.
A LA-BTC MOF AS A SENSOR ELEMENT OF AN ELECTRONIC NOSE FOR SELECTIVE ADSORPTION OF BIOMARKERS OF DISEASES: MOLECULAR DYNAMICS SIMULATIONS OF ADSORPTION
<p>The MD trajectories calculated for all the La-BTC MOF-biomarker simulation systems </p>
Electronic Nose Identification of Fasting and Non-fasting Breath Profiles
ClinicalTrials.gov study NCT02419976. IPD Sharing: NO. Countries: 1. Publications: 0.
Validation of the Diagnostic Accuracy of the Electronic Nose in the Detection of Thyroid Cancer
ClinicalTrials.gov study NCT04883294. IPD Sharing: YES. Countries: 0. Publications: 10.
Data from: Rapid and accurate detection of urinary pathogens by mobile IMS-based electronic nose: a proof-of-principle study
Urinary tract infection (UTI) is a common disease with significant morbidity and economic burden, accounting for a significant part of the workload in clinical microbiology laboratories. Current clinical chemisty point-of-care diagnostics rely on imperfect dipstick analysis which only provides indirect and insensitive evidence of urinary bacterial pathogens. An electronic nose (eNose) is a handheld device mimicking mammalian olfaction that potentially offers affordable and rapid analysis of samples without preparation at athmospheric pressure. In this study we demonstrate the applicability of ion mobility spectrometry (IMS) –based eNose to discriminate the most common UTI pathogens from gaseous headspace of culture plates rapidly and without sample preparation. We gathered a total of 101 culture samples containing four most common UTI bacteries: E. coli, S. saprophyticus, E. faecalis, Klebsiella spp and sterile culture plates. The samples were analyzed using ChemPro 100i device, consisting of IMS cell and six semiconductor sensors. Data analysis was conducted by linear discriminant analysis (LDA) and logistic regression (LR). The results were validated by leave-one-out and 5-fold cross validation analysis. In discrimination of sterile and bacterial samples sensitivity of 95% and specificity of 97% were achieved. The bacterial species were identified with sensitivity of 95% and specificity of 96% using eNose as compared to urine bacterial cultures. In conclusion: These findings strongly demonstrate the ability of our eNose to discriminate bacterial cultures and provides a proof of principle to use this method in urinanalysis of UTI.
Using an Electronic Nose to Predict Gastrointestinal Consequences of Pelvic Radiotherapy
ClinicalTrials.gov study NCT02649491. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluating a Treatment Effect in Patients With FBSS Using an Electronic Nose: a Pilot Study
ClinicalTrials.gov study NCT04469738. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Analysis of Volatile Organic Compounds by Electronic Noses in Hospitalised Patients for an Infection by SARS-COV-2 (COVID-19)
ClinicalTrials.gov study NCT04379154. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Demonstrating the Diagnostic Power of an Electronic Nose: Study on Exhaled Air Samples
ClinicalTrials.gov study NCT03721042. IPD Sharing: NO. Countries: 1. Publications: 0.
Application of an Electronic Nose in the Early Detection of ASpergillosis
ClinicalTrials.gov study NCT01395446. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.