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412 results for “sensor data”
Using a low-cost 2D LiDAR Sensor to capture 3D Data - Raw Data
<p>Raw Data for an upcoming publication in the MDPI Journal of Sensors, titled: "Using a low-cost 2D LiDAR Sensor to capture 3D Data"</p>
Data for the publication: "Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor"
<p>This data set contains all raw data for the publication “Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor”:</p> <p>- Raw sensor data</p> <p>- Python scripts</p> <p>- particle photon scripts</p> <p>- CAD Drawings</p> <p>- PCB Designs</p>
3D kinematics and kinetics of change of direction motions reconstructed from virtual inertial sensor data through optimal control simulation
<p>This is the data belonging to the publication "Estimating 3D kinematics and kinetics from inertial sensor data through musculoskeletal movement simulations".</p> <p>This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions, with a 3D full-body musculoskeletal model by tracking virtual inertial sensor data in optimal control simulations. We used the recordings of 90 trials with optical motion capture to generate marker tracking simulations from which we computed virtual inertial sensor data. Using this data, we compared inertial tracking simulations and marker tracking simulations.</p> <p>Please see the README and the publication for further details.</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>
Sense2Stop:Mobile Sensor Data to Knowledge
ClinicalTrials.gov study NCT03184389. IPD Sharing: YES. Countries: 1. Publications: 4.
Data for: Melt electrowriting enabled 3D liquid crystal elastomer structures for cross-scale actuators and temperature field sensors
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Data from: Salty Sensors, Fresh Ideas: The use of molecular and imaging sensors in understanding plankton dynamics across marine and freshwater ecosystems
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Data from: Monitoring microvascular changes over time with a repositionable 3D ultrasonic capacitive micromachined row-column sensor
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Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors
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Data accompanying: Performance characterization of low-cost air sensors for off-grid deployment in rural Malawi
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Data from: Glutamate sensor and calcium signals in dopamine neurons and dopamine release
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Data from: The evolution of sexual signal modes and associated sensor morphology in fireflies (Lampyridae, Coleoptera)
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Data from: Polyelectrolyte-based wireless and drift-free iontronic sensors for orthodontic sensing
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Predicting soil interpedal macroporosity and hydraulic conductivity dynamics: A model for integrating laser-scanned profile imagery with soil moisture sensor data
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iSCAPE Low Cost Sensor Development Data
<p><strong>Dataset Description</strong></p> <p>This dataset contains all the tests used for the low-cost sensor development during the iSCAPE project. The dataset is divided in a series of tests, each of them described on a yaml file with the test name. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each test. The tests are described in the comment of the yaml file, and are meant to be self explanatory. The conditions of the test and the purpose vary, and their reports are also included.</p> <p><strong>Sensors</strong></p> <p>The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and the Living Lab Station or Smart Citizen Station. These are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The Smart Citizen Station is meant to expand the capabilities of the Smart Citizen Kit, aiming to measure pollutants with more advanced sensors. The hardware is licensed under <a href="https://www.ohwr.org/licenses/cern-ohl/license_versions/v1.2">CERN Open Hardware License V1.2</a> and is fully described in the HardwareX Open Access publication: <a href="https://doi.org/10.1016/j.ohx.2019.e00070">https://doi.org/10.1016/j.ohx.2019.e00070</a>. The sensor documentation can be found at <a href="https://docs.smartcitizen.me">https://docs.smartcitizen.me</a> and with this DOI at Zenodo: <a href="https://doi.org/10.5281/zenodo.2555029">https://doi.org/10.5281/zenodo.2555029</a>.</p> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <ul> <li>Air temperature (ºC): Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (%rh): Sensirion SHT-31 [HUM]</li> <li>Noise level (dBA): Invensense ICS-434342 [NOISE_A]</li> <li>Ambient light (lux): Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure (kPa): NXP MPL3115A26 [PRESS]</li> <li>Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]</li> </ul> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <ul> <li>Air Temperature (ºC) Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (% REL) Sensirion SHT-31 [HUM]</li> <li>Noise Level (dBA) Invensense ICS-434342 [NOISE_A]</li> <li>Ambient Light (Lux) Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure and AMSL (Pa and Meters) NXP MPL3115A26 [PRESS]</li> <li>Carbon Monoxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]</li> <li>Nitrogen Dioxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]</li> <li>Carbon Monoxide (ppm) Alphasense CO-B4 [GB_1W, GB_1A]</li> <li>Nitrogen Dioxide (ppb) Alphasense NO2-B43F [GB_2W, GB_2A] </li> <li>Ozone (ppb) Alphasense OX-B431 [GB_3W, GB_3A]</li> <li>Gases Board Temperature (ºC) Sensirion SHT-31 [GB_TEMP] or [EXT_TEMP]</li> <li>Gases Board Rel. Humidity (% REL) Sensirion SHT-31 [GB_HUM] or [EXT_HUM]</li> <li>PM 1 (µg/m3) Plantower PMS5003 [EXT_PM_1] or [EXT_PM_A_1], [EXT_PM_B_1] for each PM sensor in the case of the Living Lab Station</li> <li>PM 2.5 (µg/m3) Plantower PMS5003 [EXT_PM_25] or [EXT_PM_A_25], [EXT_PM_B_25] for each PM sensor in the case of the Living Lab Station</li> <li>PM 10 (µg/m3) Plantower PMS5003 [EXT_PM_10] or [EXT_PM_A_10], [EXT_PM_B_10] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 0.3um<0.5um particle size (#/l) Plantower PMS5003 [EXT_PN_03] or [EXT_PN_A_03], [EXT_PN_B_03] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 0.5um<1um particle size (#/l) Plantower PMS5003 [EXT_PN_05] or [EXT_PN_A_05], [EXT_PN_B_05] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 1m<2.5um particle size (#/l) Plantower PMS5003 [EXT_PN_1] or [EXT_PN_A_1], [EXT_PN_B_1] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 2.5m<5um particle size (#/l) Plantower PMS5003 [EXT_PN_25] or [EXT_PN_A_25], [EXT_PN_B_25] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 5m<10um particle size (#/l) Plantower PMS5003 [EXT_PN_5] or [EXT_PN_A_5], [EXT_PN_B_5] for each PM sensor in the case of the Living Lab Station</li> <li>PN between >10um particle size (#/l) Plantower PMS5003 [EXT_PN_10] or [EXT_PN_A_10], [EXT_PN_B_10] for each PM sensor in the case of the Living Lab Station</li> </ul> <p><strong>How to find the data</strong></p> <p>Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:</p> <ul> <li>author: who has been in charge of performing the test (internal reference - not relevant)</li> <li>comment: describing in general terms what was done in the test, and with what purpose</li> <li>commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only</li> <li>devices: a descriptor containing different fields for traceability (below)</li> <li>id: the test name</li> <li>project: within the test was performed, in this case it is always iscape</li> <li>report: if there is any report analysing the test</li> <li>type_test: indoor, oudoor test or other.</li> </ul> <p><strong>Description of devices entry</strong></p> <p>For each device that was used in the test, two generic types are used:</p> <ul> <li>low cost sensors (type: STATION or KIT)</li> <li>high end sensors (type: REFERENCE)</li> </ul> <p>For <strong>low cost Smart Citizen sensors</strong>, the fields are:</p> <ul> <li>alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed</li> <li>device_id: device id in Smartcitizen API</li> <li>fileNameInfo: not used</li> <li>fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv</li> <li>fileNameRaw: (only if source = csv is used) raw file name</li> <li>frequency: original recording frequency</li> <li>location: for timezone correction only, not accurate</li> <li>max_date: last recording date</li> <li>min_date: first recording date</li> <li>name: self-explanatory</li> <li>pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)</li> <li>source: api or csv</li> <li>type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT</li> <li>version: smartcitizen hardware version</li> </ul> <p>For <strong>high end</strong> sensors, the fields are:</p> <ul> <li>channels: which channels the device was recording for internal convertion <ul> <li>names: which are the columns in the csv file</li> <li>pollutants: which pollutants do they respectively refer to</li> <li>units: the units of these pollutants</li> </ul> </li> <li>equipment: the brand of the analyser</li> <li>fileNameProc: same as above</li> <li>fileNameRaw: same as above</li> <li>index: format in which the timeindex is done, for parsing purposes <ul> <li>format: (example '%Y-%m-%d %H:%M:%S')</li> <li>frequency: frequency at which the device was recorded</li> <li>name: column name</li> </ul> </li> <li>location: same as above</li> <li>name: name of the device</li> <li>type: REFERENCE (always for these devices)</li> <li>source: csv</li> </ul> <p><strong>iSCAPE Dataset Reference Numbers:</strong></p> <p>The datasets here presented are related to the following iSCAPE dataset reference numbers:</p> <ul> <li>DS_TS_054</li> <li>DS_TS_062</li> <li>DS_TS_063</li> <li>DS_TS_065</li> <li>DS_TS_067</li> <li>DS_TS_068</li> <li>DS_TS_069</li> <li>DS_TS_070</li> <li>DS_TS_071</li> <li>DS_TS_072</li> <li>DS_TS_073</li> <li>DS_TS_074</li> <li>DS_TS_075</li> <li>DS_TS_076</li> <li>DS_TS_077</li> <li>DS_TS_078</li> <li>DS_TS_079</li> <li>DS_TS_080</li> <li>DS_TS_081</li> <li>DS_TS_084</li> <li>DS_TS_088</li> <li>DS_TS_089</li> <li>DS_TS_090</li> <li>DS_TS_092</li> </ul> <p> </p>
Temperature sensor data for locations in Smart-Santander testbed of Fed4FIRE+
<p>This is a database dump with temperature sensor-values (called "phenomenons" in testbed API), collected during the Fed4FIRE+ project SECTOR (Algorithm to determine a cost-effective, optimal SpatiaL-dEployment for smart-City environmenTal sensOr netwoRks).</p> <p> </p>
Indoor low-cost sensor system data
<p>Data used for a comparison of the Matterport Pro2 3D, Ricoh Theta V, and Leica BLK360 in two indoor settings using the Leica RTC360 as reference. The test sites are lecture hall 101 at the Aalto University Department of Machine Engineering in Espoo, Finland, and the Tetra Conference Hall at the Hanaholmen Swedish-Finnish Cultural Centre in Espoo, Finland.</p> <p>The data are divided into four sets - one room geometry with the furniture removed and one detailed segment for both test sites - with four registered point clouds and three registered meshes being provided for each, as well as the reference. The point clouds stem from data obtained with each of the three sensor systems and processed with Matterport's processing system, with the Leica BLK360 data also being processed with Leica's proprietary processing system for a fourth point cloud. For the meshes, the Matterport processing system has been used to produce one mesh from each sensor system.</p>
Binary data files from a Sequoia Scientific, Inc Laser In Situ Scattering and Transmissometry sensor integrated into a Teledyne Webb Slocum Glider
<p>This dataset includes raw binary files from a Laser In Situ Scattering and Transmissometry (LISST) sensor integrated into a Teledyne Webb Slocum Glider. The glider deployment data can be found here: <a href="https://marine.rutgers.edu/cool/data/gliders/deployments/deployment.php?deployment=ru28-20170915T1416">(Rutgers Glider RU28</a>). The Sequoia Scientific, Inc. developed LISST particle sizing instrument was modified to fit within a Slocum glider science bay. Binary files can be processed with standard Sequoia Scientific, Inc processing software available here <a href="https://www.sequoiasci.com/product/lisst-200x/">https://www.sequoiasci.com/product/lisst-200x/</a>. Each binary file corresponds to a Slocum glider dive segment and is time synced with the glider RU28 data files. the LISST-Glider instrument was deployed on 09/15/2017 and recovered after 18 days on 10/03/2017.</p>
Data from: Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors
Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.
[data]Pollution source detection with low-cost low-accuracy sensors through coupling forward data assimilation and inverse optimization
<p>The data used in the case study(Cases-S1,S2,S3)in manuscript "Pollution source detection with low-cost low-accuracy sensors through coupling forward data assimilation and inverse optimization"</p>
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.