Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
24
datasets available to search
ShareScore release 0.9.0
Dataset results
24 results for “gas sensor”
Summer water chemistry, high frequency sensors, zooplankton and benthic macroinvertebrate community composition, periphyton, fish, and macrophyte biomass, along with lake metabolism and greenhouse gas dynamics in six experimental ponds in central Iowa, USA (2020)
This data product contains physical, chemical, and biological data ranging from the minute to daily to weekly scale in six artificial ponds (400 square meter surface area, 2m depth) in central Iowa (USA) 2020. Ponds were paired into three sets of treatment and reference with treatment ponds receiving two nutrient pulses designed to increase ambient phosphorus concentrations ~ 3 - 5%. Nitrogen and phosphorus were added as NH4NO3 and H3PO4, respectively, at a 24:1 molar ratio. The first nutrient pulse occurred on Julian day of year (DOY) 176 corresponding to a 3% increase and the second nutrient pulse occurred on DOY 211 to a 5% increase. Each treatment-reference set had a different food web structure established ranging between low, intermediate, and high complexity based on trophic connectivity and food chain length. Added to this data package is a document titled "2020 Iowa State University Horticultural Farm Experimental Ponds Nutrient Addition Experiment". For experimental set up, context, and a summary table of the data tables archived herein with available variables please review this document. It is added to aid in successful interpretation and to increase ease-of-use. Please email Tyler Butts (tyler.james.butts@gmail.com) for any and all questions regarding context or use of this dataset!
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
Dataset supplementing B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)
<p>Dataset presented in B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)</p>
Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors
<p>The "Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors" can be used to test machine learning approaches on their capability of interpreting sensor patterns of commercially available MOS gas sensors, i.e., SGP40 (Sensirion AG, Stäfa, Switzerland), to predict multiple different gas concentrations and the relative humidity. Furthermore, the dataset can be used to test the transferability between sensors. <br> The dataset was recorded with the help of a custom-built gas mixing apparatus (GMA). The GMA allows applying well-known gas mixtures to multiple gas sensors. For this experiment, three SGP40 with four sub-sensors each were exposed to 900 different unique gas mixtures (UGMs) consisting of ten different gases. In detail, the dataset consists of eight volatile organic compounds (VOCs) (acetic acid, acetone, ethanol, ethyl acetate, formaldehyde, isopropanol, toluene, and xylene), two background gases (carbon monoxide and hydrogen), and the relative humidity at 20 °C. During exposure, the sensors are operated in a temperature-cycled operation. The temperature cycle consists of alternating high and low-temperature phases. The high-temperature phases are set at 400 °C and have a duration of 5 seconds, while the low-temperature steps increase in 25 °C steps from 100 °C-375 °C, where each step has a duration of 7 seconds. The only exception is sub-sensor 4, where the temperature is only alternated between 250 °C and 300 °C. The total duration of the temperature cycle is 144 seconds, and during this time, the logarithmic sensor resistance is read out at 10 Hz. Each gas mixture was recorded for ten temperature cycles to ensure that stable gas mixtures were applied to the sensor. Only stable samples 6 (not always),7,8, and 9 were used for further evaluation. The 900 UGMs can be separated into three parts, and for each part, the mixtures were generated based on Latin hypercube sampling and the ranges specified in Table 1.</p> <table> <caption>Tabel 1: Uniform distributed ranges for all gasses within the gas mixtures</caption> <tbody> <tr> <td> </td> <td>UGM 1-200</td> <td>UGM 201-500</td> <td>UGM501-900</td> </tr> <tr> <td>Carbon monoxide</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> </tr> <tr> <td>Hydrogen</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> </tr> <tr> <td>Relative humidity</td> <td>25 - 80 %</td> <td>25 - 80 %</td> <td>25 - 80 %</td> </tr> <tr> <td>Acetic acid</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Acetone</td> <td>3 - 50 ppb</td> <td>3 - 150 ppb</td> <td>3 - 500 ppb</td> </tr> <tr> <td>Ethanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Ethyl acetate</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Formaldehyde</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 300 ppb</td> </tr> <tr> <td>Isopropanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Toluene</td> <td>1 - 75 ppb</td> <td>1 - 75 ppb</td> <td>1 - 250 ppb</td> </tr> <tr> <td>Xylene</td> <td>2 - 150 ppb</td> <td>2 - 150 ppb</td> <td>2 - 500 ppb</td> </tr> </tbody> </table> <p>To be able to use this dataset for transfer learning, the dataset consists of three different SPG40; two are from the same batch (sensor A and sensor B), and sensor C is from a different batch. <br> The dataset consists of the sensors' data and a target for evaluation. The data is already split into training and Validation and is stored in cells for each sensor: <br> sensorA_train<br> sensorA_test<br> sensorB_train<br> sensorB_test<br> sensorC_train<br> sensorC_test</p> <p> Each sensor cell contains four arrays, one for each sub-sensor within one SGP40. The number of rows in the arrays represents the number of observations (693 for test and 2401 for training), and the number of columns represents the number of samples per observation (1440).<br> The targets, i.e., the concentrations of each gas, are given in the target_train and targe_test structs. Since the data were recorded simultaneously, those structs can be used as targets for all sensors. The ten different gases, relative humidity, and TVOCsens are actual targets, while the range parameter represents the specific unique gas mixture ID.</p> <p>Although this is a mat file, it can be opened as an hdf5 file.</p>
Rawdata for: Preparation of low-concentration H2 test gas mixtures in ambient air for calibration of H2 sensors, Karbach et al., 2024
<p>Rawdata for publication: Karbach et al.: Preparation of low-concentration H2 test gas mixtures in ambient<br>air for calibration of H2 sensors, AMT, 2024</p> <p>DOI: 10.5194/amt-17-4081-2024</p>
Temperature-modulated gas sensor signal
<p><strong>Temperature-modulated</strong><strong> gas sensor signal</strong></p> <p><strong>Abstract</strong>: Data is the conductance of a temperature-modulated gas sensor exposed to several concentrations of several gases. Both classification (gas type) and (selective) quantification are of interest.</p> <p><strong>Source</strong>:<br> Creator: Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany<br> Contact: m.bastuck@lmt.uni-saarland.de, info@lmt.uni-saarland.de</p> <p><strong>Dataset</strong>:<br> The data set was experimentally obtained from a semiconductor gas sensor (UST GGS 1330) with temperature cycled operation (TCO). The sensor temperature was linearly increased from 200 °C to 400 °C within 20 s, and back to 200 °C within another 20 s. This cycle is repeated during the whole measurement (~18 h). During the measurement, the sensor was exposed to four different gases (carbon monoxide, CO, ammonia, NH3, nitrogen dioxide, NO2, and methane, CH4) in three different concentrations each.<br> The aim is either to classify the type of gas that is currently seen by the sensor independent of its concentration, or the concentration of one specific gas type.</p> <p><strong>Attribute Information</strong>:</p> <ul> <li><strong>sensordata.csv</strong><br> The data set consists of the measured conductance in nS (nanosiemens) of the sensor. Each row represents one cycle (200-400-200 °C in 40 s) with 4001 data points. </li> <li><strong>targetvectors.csv</strong><br> Different target vectors have been prepared manually. All of them include an ‘ignore’ label that indicates cycles where the exact gas concentrations are unknown, e.g. due to a change in concentration which can take several cycles. Cycles during an “init peak” in the beginning to test the setup are labeled as ‘ignore’ as well. These cycles should always be discarded. The objectives of the ten target vectors are as follows: <ol> <li>categorical. A short segment at the beginning is labeled as ‘background’, i.e. no relevant test gas is present. In the following, the presence of a test gas is indicated with its name, i.e. CO, NH3, NO2, and CH4</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, CO exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, NH3 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, NO2 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, CH4 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>categorical. Like (1), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘background’.</li> <li>continuous. Like (2), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (3), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (4), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (5), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> </ol> </li> </ul> <p>Target vectors (6)-(10) contain some sensor drift in the background. Additionally, the background class is much larger compared to the gas exposure classes. Target vectors (7)-(10) also label all but one gas as ‘0’, so that a selective quantification of the target gas must be performed.</p> <ul> <li><strong>gasexposures.png</strong><br> A graphical summary of the dataset.</li> <li><strong>temperaturecycle.png</strong><br> A graphical representation of the temperature cycle used when operating the gas sensor.</li> </ul>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
Dataset for "AACVD Synthesized Tungsten Oxide-NWs loaded with Osmium oxide as Gas Sensor Array: Enhancing Detection with PCA and ANNs"
<p>Dataset with the gas senisng mesurements for Osmium oxide decorated tunsten oxide gas sensors</p>
Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018
<p>Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018</p>
Dataset supplementing " X. Zhang, B. Ojha, H. Bichlmaier, I. Hartmann and H. Kohler; Extensive Gaseous Emissions Reduction of Firewood-Fueled Low Power Fireplaces by a Gas Sensor-Based Advanced Combustion Airflow Control System and Catalytic Post-Oxidation, Sensors, 2023"
<p>Dataset supplementing " X. Zhang, B. Ojha, H. Bichlmaier, I. Hartmann and H. Kohler; Extensive Gaseous Emissions Reduction of Firewood-Fueled Low Power Fireplaces by a Gas Sensor-Based Advanced Combustion Airflow Control System and Catalytic Post-Oxidation, Sensors, 2023"</p>
Differential spectrometric gas sensor with dual out-of-phase microplasma sources, dataset
<p>Data recorded for the publication <em>Differential spectrometric gas sensor with dual out-of-phase microplasma sources.</em></p> <p>The CSVs contain five columns, split at half the number of rows. Columns in the top half:<br> 1) Timestamp [s]<br> 2) Photodetector voltage [V]<br> 3) Reference voltage for power supplies [V]</p> <p>Bottom half columns:<br> 1) Sensor signal [Hz]<br> 2) FFT of the sensor signal [dBm]</p>
Figaro Taguchi Gas Sensor SUEZ Amailloux Data 2022-2023
<p>Minute-average data from the SUEZ Amailloux landfill site:</p> <ul> <li>Gill MetPak 2D sonic anemometer wind data between 1<sup>st</sup> April 2022 and 31<sup>st</sup> May 2023</li> <li>LICOR LI-7810 mole fraction data between 1<sup>st</sup> April 2022 and 31<sup>st</sup> May 2023</li> <li>Logger 1 (Figaro Ultra Logger B) data between 24<sup>th</sup> November 2023 and 31<sup>st</sup> May 2023</li> <li>Logger 2 (LSCE009) data between 1<sup>st</sup> April 2022 and 31<sup>st</sup> May 2023</li> </ul>
Evaluating the feasibility of using downwind methods to quantify point source oil and gas emissions using continuous monitoring fence-line sensors
Open the record for dataset details and reuse information.
Random gas mixtures for efficient gas sensor calibration: Dataset
<p>The dataset was created at the Lab for Measurement Technology (Saarland University). It consists the raw signals (proportional to the logarithmic conductance) of two temperature-modulated semiconductor metaloxide gas sensors (ScioSense AS-MLV and AS-MLV-P2, former AppliedSensor, ams).</p> <p>An exact description of the measurement setup and its results can be found in the open access article:</p> <p>Baur, T., Bastuck, M., Schultealbert, C., Sauerwald, T., and Schütze, A.: Random gas mixtures for efficient gas sensor calibration, J. Sens. Sens. Syst., 9, 411–424, 2020, https://doi.org/10.5194/jsss-9-411-2020</p> <p><strong>Dataset:</strong><br> The mat-file comprises diffrent datasets:</p> <ul> <li><strong>as_mlv: </strong>raw signal (~ logarithmic conductance) of the AS-MLV containing 12202 sensor cycles with 12000 data points (@ 100 Hz)</li> <li><strong>as_mlv_p2: </strong>raw signal (~ logarithmic conductance) of the AS-MLV-P2 containing 12202 sensor cycles with 12000 data points (@ 100 Hz)</li> <li><strong>as_mlv_targets and as_mlv_p2_targets:</strong> <ul> <li><strong>acetone: </strong>acetone concentration in ppb</li> <li><strong>benzene</strong>: benzene concentration in ppb</li> <li><strong>formaldehyde</strong>: formaldehyde concentration in ppb, NaN values are undefined formaldehyde concentrations</li> <li><strong>toluene</strong>: toluene concentration in ppb</li> <li><strong>carbon_monoxide</strong>: carbon monoxide concentration in ppb</li> <li><strong>hydrogen</strong>: hydrogen concentration in ppb</li> <li><strong>humidity</strong>: relative humidity in %RH</li> <li><strong>voc_sum_ppb: </strong>sum of all VOC concentations (acetone, toluene, formaldehyde, benzene) in ppb </li> <li><strong>voc_sum_ugm3: </strong>sum of all VOC concentations (acetone, toluene, formaldehyde, benzene) in µg/m<sup>3</sup></li> <li><strong>count</strong>: number of the gas mixture exposure, each gas mixture exposure contains approx.10 sensor cycles</li> <li><strong>measurement</strong>: number of the measurement, each measurement contains approx.100 gas exposures</li> <li><strong>ranges</strong>: marked cycles for the data evaluation with group number</li> </ul> </li> </ul> <p> </p>
Dataset gas sensor T050401
<p>The gas monitoring system datasets acquitted by gas sensor T050401 between 16 April and 16 May 2022. </p>
Dataset for 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations'
<p> </p> <p>This dataset is associated with the research entitled: 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. It contains raw data from six low-cost sensor loggers and one anemometer, collected during an experiment consisting of a series of controlled releases conducted in October 2019 at the TADI (TotalEnergies Anomaly Detection Initiative) platform.</p> <p><strong>Dataset Structure:</strong></p> <p>- `time`: Timestamp, marking the exact time the data was collected.<br> - `CH4`: Methane concentration measured by the reference instrument in parts per million (ppm).<br> - `2611C`: Voltage variation measured by the Figaro TGS 2611C-00 MOS sensor in volts (V).<br> - `2600`: Voltage variation measured by the Figaro TGS 2600 MOS sensor in volts (V).<br> - `2611E`: Voltage variation measured by the Figaro TGS 2611E-00 MOS sensor in volts (V).<br> - `RH_DHT22`: Relative humidity measured by the DHT22 sensor in percentage (%).<br> - `RH_SHT75`: Relative humidity measured by the SHT75 sensor in percentage (%).<br> - `T_DHT22`: Air temperature measured by the DHT22 sensor in degrees Celsius (°C).<br> - `T_SHT75`: Air temperature measured by the SHT75 sensor in degrees Celsius (°C).<br> - `T_BMP180`: Air temperature measured by the BMP180 sensor in degrees Celsius (°C).<br> - `T_BMP280`: Air temperature measured by the BMP280 sensor in degrees Celsius (°C).<br> - `P_BMP180`: Atmospheric pressure measured by the BMP180 sensor in pascals (Pa).<br> - `P_BMP280`: Atmospheric pressure measured by the BMP280 sensor in pascals (Pa).<br> - `Release`: Number of the controlled release.</p> <p><strong>Acknowledgment:</strong></p> <p>When using this dataset, please reference the original research paper titled ‘Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’.</p> <p><strong>Contact Information:</strong></p> <p>Olivier Laurent (olivier.laurent@lsce.ipsl.fr)</p> <p> </p>
Black Gold Layers Preparation via Thermal Evaporation, Material and Optical Properties, Application Potential for Gas Sensors
Open the record for dataset details and reuse information.
Density functional theory study of Mobius boroncarbon-nitride as potential CH4, H2S, NH3, COCl2 and CH3OH gas sensor
<p>The interesting properties of Mobius structure and Boron-Carbon-Nitride inspired this research to study different characteristics of Mobius Boron-Carbon-Nitride (MBCN) nanoribbon. The structural stability, vibrational, electrical, and optical properties are analyzed using the density functional theory. The gas sensing ability of the modeled MBCN structure was also studied for CH<sub>4</sub>, H<sub>2</sub>S, NH<sub>3</sub>, COCl<sub>2</sub>, and CH<sub>3</sub>OH gases. The negative adsorption energy and alteration of electronic bandgap verified that MBCN is very sensitive toward the selected gases. The complex structures showed a high absorption coefficient with strong chemical potential and 7 ps- 0.3 ms recovery time. The negative change in entropy signifies that all the complex structures were thermodynamically stable. Among the selected gases, the MBCN showed the strongest interaction with CH<sub>3</sub>OH gas.</p>
A chemiresistive-potentiometric multivariate sensor for discriminative gas detection
<p>The data set contains source data for all figures in this article and supplementary materials.</p>
Density functional theory study of Mobius boroncarbon-nitride as potential CH4, H2S, NH3, COCl2 and CH3OH gas sensor
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.