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1,772 results for “sensors”
Evaluating a Kinematic Data Glove with Pressure Sensors to Automatically Differentiate Free Motion from Product Manipulation (Experimental Data)
<p>Experimental data from <em>"Evaluating a kinematic data glove with pressure sensors to automatically differentiate free motion from product manipulation", </em>available at Applied Sciences.</p> <p>"DATA.zip" contains raw data collected using VMG30 and CyberGlove data gloves, in txt format. </p> <p>For further information please see the details in the manuscript or contact the corresponding author Alba Roda-Sales (rodaa@uji.es).</p>
Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks
<p>This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. </p> <p>The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.</p> <p>The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.</p>
TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation
<p><strong>TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation</strong></p> <p> </p> <p><strong>Description:</strong></p> <p>The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.</p> <p> </p> <p><strong>cloud_fraction</strong><strong>[float]</strong>:</p> <p>Description: Effective cloud fraction derived from GOME-2 Level1B product.</p> <p>Units: none</p> <p><strong>latitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center latitude.</p> <p>Units: degrees N</p> <p><strong>longitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center longitude.</p> <p>Units: degrees E</p> <p><strong>latitude_bounds</strong><strong>[float]</strong>:</p> <p>Description: Latitude of the boundary corners for each pixel.</p> <p>Units: degrees N</p> <p><strong>longitude _bounds</strong><strong>[float]</strong>:</p> <p>Description: Longitude of the boundary corners.</p> <p>Units: degrees E</p> <p><strong>SIF_740</strong><strong>[float]</strong>:</p> <p>Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>SIF_daily</strong><strong> [float]</strong>:</p> <p>Description: SIF signal at 740nm with correction of day-length.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>Sigma_i</strong><strong>[float]</strong>:</p> <p>Description: The squre of single retrieval error of SIF_740.</p> <p>Units: (mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>)<sup>2</sup></p> <p><strong>Solar_zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar zenith angle.</p> <p>Units: degrees</p> <p><strong>Solar_azimuth_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar azimuth angle.</p> <p>Units: degrees</p> <p><strong>Viewing _zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Viewing zenith angle.</p> <p>Units: degrees</p> <p><strong>Viewing_azimuth_angle</strong><strong>[float]</strong>:</p> <p>Description: Viewing azimuth angle.</p> <p>Units: degrees</p> <p><strong>chi2</strong><strong>[float]</strong>:</p> <p>Description: The reduced chi-square value calculated based on the the fitting residuals.</p> <p>Units: None</p> <p><strong>Rad_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average radiance within the 735~758 nm window</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>ps_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within at around 680 nm ().</p> <p>Units: None</p> <p><strong>ps_red</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within the 665~680 nm window</p> <p>Units: None</p> <p><strong>NDVI</strong><strong>[float]</strong>:</p> <p>Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm).</p> <p>Units: None</p> <p><strong>QA</strong><strong>[int]</strong>:</p> <p>Description: Quality_flag.</p> <p>0= Bad (ineffective original data)</p> <p>1= Good (passed all quality-filtering criteria)</p> <p>2= Good and the cloud fraction is lower than 0.3</p> <p>Units:None</p>
Dataset for the "Comparison of fiber interferometric sensor with a commercial interferometer for a Kibble balance velocity calibration"
<p>This dataset supports findings of the study presented in paper "Comparison of fiber interferometric sensor with a commercial interferometer for a Kibble balance velocity calibration".</p>
TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation
<p><strong>TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation</strong></p> <p> </p> <p><strong>Description:</strong></p> <p>The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.</p> <p> </p> <p><strong>cloud_fraction</strong><strong>[float]</strong>:</p> <p>Description: Effective cloud fraction derived from GOME-2 Level1B product.</p> <p>Units: none</p> <p><strong>latitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center latitude.</p> <p>Units: degrees N</p> <p><strong>longitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center longitude.</p> <p>Units: degrees E</p> <p><strong>latitude_bounds</strong><strong>[float]</strong>:</p> <p>Description: Latitude of the boundary corners for each pixel.</p> <p>Units: degrees N</p> <p><strong>longitude _bounds</strong><strong>[float]</strong>:</p> <p>Description: Longitude of the boundary corners.</p> <p>Units: degrees E</p> <p><strong>SIF_740</strong><strong>[float]</strong>:</p> <p>Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>SIF_daily</strong><strong> [float]</strong>:</p> <p>Description: SIF signal at 740nm with correction of day-length.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>Sigma_i</strong><strong>[float]</strong>:</p> <p>Description: The squre of single retrieval error of SIF_740.</p> <p>Units: (mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>)<sup>2</sup></p> <p><strong>Solar_zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar zenith angle.</p> <p>Units: degrees</p> <p><strong>Solar_azimuth_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar azimuth angle.</p> <p>Units: degrees</p> <p><strong>Viewing _zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Viewing zenith angle.</p> <p>Units: degrees</p> <p><strong>Viewing_azimuth_angle</strong><strong>[float]</strong>:</p> <p>Description: Viewing azimuth angle.</p> <p>Units: degrees</p> <p><strong>chi2</strong><strong>[float]</strong>:</p> <p>Description: The reduced chi-square value calculated based on the the fitting residuals.</p> <p>Units: None</p> <p><strong>Rad_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average radiance within the 735~758 nm window</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>ps_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within at around 780 nm.</p> <p>Units: None</p> <p><strong>ps_red</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within the 665~680 nm window</p> <p>Units: None</p> <p><strong>NDVI</strong><strong>[float]</strong>:</p> <p>Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm).</p> <p>Units: None</p>
VIO-GNSS Dataset: Benchmarking Dataset for Sensor Fusion of Visual Inertial Odometry and GNSS Positioning
<p>This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on <a href="https://github.com/AaltoVision/vio-gnss-dataset">GitHub</a>.</p> <p>The upload contains two datasets (version 1.0.0):</p> <ul> <li>urban_with_gnss_dead_zones (7.0 GB, ~16 minutes) <ul> <li>City streets</li> <li>A building is passed through on two occasions which makes the GNSS location signal unavailable at times.</li> <li>RTK Fix is acquired at times</li> </ul> </li> <li>suburban_nature (10.6 GB, ~19 minutes) <ul> <li>The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation</li> <li>The RTK solution is only Float or None most of the route.</li> </ul> </li> </ul> <p>Details on collecting the data:</p> <ul> <li>Software <ul> <li>The data was collected using <a href="https://github.com/AaltoVision/vio-gnss-recorder">this</a> open-source recorder. <ul> <li>Can be easily replayed using <a href="https://github.com/SpectacularAI/sdk-examples">SpectacularAI's SDK</a> (sdk-examples/python/oak/vio_replay.py)</li> </ul> </li> <li>Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland.</li> <li><strong>Necessary files to implement SLAM are included</strong> in the dataset.</li> <li>Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters.</li> </ul> </li> <li>Hardware <ul> <li>OAK-D stereo depth + color camera (Luxonis)</li> <li>C099-F9P GNSS module (u-blox)</li> <li>ANN-MB-00 high precision GNSS antenna (u-blox)</li> </ul> </li> </ul>
VNMPF-LIS: Validation Network Multiplatform Precipitation Feature (VNMPF) Dataset with International Space Station Lightning Imaging Sensor (ISS LIS) Data
<p>The Multiplatform Precipitation Feature (MPF) database combines ground- and space-based precipitation observations and retrievals from the Global Precipitation Measurement (GPM) mission Validation Network (VN) with space-based lightning measurements from the Lightning Imaging Sensor on board the International Space Station (ISS LIS). The data are synthesized in a thunderstorm-like, feature-based framework that encapsulates the microphysical, kinematic, and electrical properties of the observed storm.<br> <br> A VNMPF includes:</p> <p>- Radar information, GPM orbit, and ISS orbit <br> - Time/date information<br> - Geographical information<br> - Radar reflectivity characteristics<br> - Lightning energetic and identification information (where there is lightning)<br> - 3-dimensional wind information (where radars in dual-Doppler configuration are available)<br> <br> Version 1: 2017-2020</p> <p>Version 2: 2017-2022, updated VN winds </p>
Datasets for article "Robot Self-Calibration Using Actuated 3D Sensors"
<p>Real and sythetic datasets used in artilcle "Robot Self-Calibration Using Actuated 3D Sensors". For each recodring of a calibration scene is there is a ROS bag file holding a single message of type vision_3d_msgs/Actuated3dRecording.</p>
ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments
<p>We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms.<br> <br> To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>.<br> <strong>Please read the license file (available in the GitHub repository) before using the database.</strong></p>
PoqueiraOccupancy: Dataset and metrics from occupancy sensors of urban areas and establishments in the region of Barranco del Poqueira in the Alpujarra Granadina
<p>This dataset is linked to the analysis of different aspects related to the conservation of the Sierra Nevada National Park through advanced digital systems. The devices have been deployed in the municipalities of Pampaneira and Capileira in the Alpujarra region of the province of Granada. The data is collected by 11 BOSCH fixed cameras 11.00 387.4900 Interior IR 5.3 MP and 4 TURRET type cameras Interior IR Lens 2.8 mm 5.3 MP 100º H.265 multi-streaming (H.265; H.264; M-JPEG).</p> <p>The devices have been installed as follows: 3 devices have been placed in establishments in Capileira, 8 in establishments in Pampaneira, and 4 in urban passage areas in the municipality of Pampaneira. All devices are capable of measuring the entry and exit to the establishment or area they are designated for. In some cases, there is also a metric which measures the number of people present within that area. The information related to the establishments has been anonymized to ensure the privacy of the collaborating companies in the project and the flow of customers during the studied period.</p> <p>The data attached in the CSV files DATA_OCCUPANCY_2022 and DATA_OCCUPANCY_2023 contain information about individuals detected by the cameras in the years 2022 (from February to December) and 2023 (from January to August). The calculation of the number of people is done cumulatively in hourly intervals. The collected variables include:</p> <ul> <li> <p>device_ID: The name of the device recording the value.</p> </li> <li> <p>type: The metric measuring the recording, which can be ENTRADA (entry), SALIDA (exit), or AFORO (occupancy).</p> </li> <li> <p>date: The date and time at which the cumulative people count is recorded for the specific metric.</p> </li> <li> <p>counter: The number of people counted for a specific metric in that time period.</p> </li> </ul>
PoqueiraVehicleLPR: A Dataset of Vehicle Detection Sensors in the region of Barranco de Poqueira in the Alpujarra Granadina.
<p>This dataset is linked to the analysis of different aspects related to the conservation of the Sierra Nevada National Park through advanced digital systems. The devices have been deployed along the road that passes through the municipalities of Pampaneira, Bubión and Capileira, in the Alpujarra region of Granada. Data is collected by 4 devices equipped with vehicle detection sensors. These devices are Hikvision LPR IP cameras with Automatic number-plate recognition (ANPR) based on Deep Learning. The devices have a 2MP resolution, 2.8-12 mm varifocal optics, and IR LEDs with a range of 50 m.</p> <p>To cover the entrances and exits of each village in the target area, we strategically positioned the cameras. The locations are entrance to Pampaneira from the western part of the Alpujarra (PAM1), entrance to Pampaneira from the eastern part of the Alpujarra (PAM2), entrance to Bubión via a single road (BUB), and entrance to Capileira via a single road (CAP). </p> <p>The attached data in the CSV files DATA_VEHICLES_2022 and DATA_VEHICLES_2023 contain information about vehicle passages in the years 2022 (from February to December) and 2023 (from January to August) for each installed camera. It includes anonymized license plate information based on an identifier to facilitate cross-referencing and in-depth analysis. The collected variables include:</p> <ul> <li> <p>camera_ID: License Plate Recognition (LPR) camera identifier.</p> </li> <li> <p>date: Timestamp indicating the date and time of the vehicle passage through the camera.</p> </li> <li> <p>num_plate_id: Anonymized license plate identifier. A value of -1 indicates that the vehicle was not correctly identified by the camera.</p> </li> <li> <p>direction: Binary value (IN/OUT) indicating whether the vehicle is entering or exiting the municipality referenced by the camera.</p> </li> </ul>
Dataset For "Nyiragongo crater collapses measured by multi-sensor SAR amplitude time series"
<p>This archive contains the input ant results files used with PickCraterSAR for publication "Nyiragongo crater collapses measured by multi-sensor SAR amplitude time series" submitted to JGR-SE.</p> <p>It also contains crops of each amplitude images used in this study in ENVI format with corresponding headers.</p> <p>At least, it contains the ash index values derives from SEVIRI data analysis.</p>
Experimental Database of deterministic wave prediction built from synchronous measurements from an X-band pulse radar and met-ocean sensors deployed on the Floatgen floating wind turbine and its vicinity on SEM-REV test site.
<p>This dataset is a deliverable of the FLOATECH project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007142.<br> The aim of this dataset is a result of the field experiments carried out at the Floatgen FOWT located at the SEM-REV test site.</p>
Virtual sensors benchmark study test timeseries tier 0 - part 1
<p>A dataset with time series for testing virtual sensor models for wind turbine aeroelastic loads. Data are in zipped format, with 100 time series available.</p> <p>The test sets in the study are organized with several levels of "difficulty" according to added uncertainties and noise with respect to the training data. This particular dataset (tier 0) is with exactly the same distribution as the training data.</p>
Code and data for: Decoupling channel count from field-of-view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery
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Multi-stage sleep classification using photoplethysmographic sensor
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Data from: Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors
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Properties of leak detection sensor, Arduino and application code
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Data supplement to: Quality control of image sensors using gaseous tritium light sources
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Using optical flow temporal interpolation of satellite imagery to assist multi-sensor global cloud product composites
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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
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.