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.
412
datasets available to search
ShareScore release 0.7.1
Dataset results
412 results for “sensor data”
MINDS-Libras Dataset (RGB-D sensor data)
<p>Brazilian Sign Language (Libras) data set with 20 signs for sign language and gesture recognition benchmark:<br> <br> - Acontecer (To happen)<br> - Aluno (Student)<br> - Amarelo (Yellow)<br> - América (America)<br> - Aproveitar (To enjoy)<br> - Bala (Candy)<br> - Banco (Bank)<br> - Banheiro (Bathroom)<br> - Barulho (Noise)<br> - Cinco (Five)<br> - Conhecer (To know)<br> - Espelho (Mirror)<br> - Esquina (Corner)<br> - Filho (Son)<br> - Maçã (Apple)<br> - Medo (Fear)<br> - Ruim (Bad)<br> - Sapo (Frog)<br> - Vacina (Vaccine)<br> - Vontade (Will)<br> <br> Each one of the signs was recorded 5 times by 12 signers, using a Chroma Key background. Among the signers are men and women with basic to advanced knowledge in Libras. </p> <p>The RGB-D sensor (kinect v2) available the RGB videos (1920 x 1080) and depth videos (640 x 480) in "mp4" format, and the body points and face data in "txt" file.</p> <ul> <li>The body file has seven different information (Position X, Y and Z; Orientation X, Y and Z; TrackingState; LeftHandState; RightHandState; ColorPosition X and Y; and DepthPosition X and Y) about the 25 points: (1) Spine Base, (2) Spine Mid, (3) Neck, (4) Head, (5) Shoulder Left, (6) Elbow Left, (7) Wrist Left, (8) Hand Left, (9) Shoulder Right, (10) Elbow Right, (11) Wrist Right, (12) Hand Right, (13) Hip Left, (14) Knee Left, (15) Ankle Left, (16) Foot Left, (17) Hip Right, (18) Knee Right, (19) Ankle Right, (20) Foot Right, (21) Spine Shoulder, (22) Hand Tip Left, (23) Thumb Left, (24) Hand Tip Right and (25) Thumb Right. There are 13 lines (or data) for each frame. This order is repeated sequentially up to 1950 lines (13 lines $\times$ 150 frames), representing the sign video.</li> </ul> <p> </p> <ul> <li>Regarding to the face data, the same organisation was adopted. In this case, we have seven information (FaceBox, FaceRotation, HeadPivot, AnimationUnit, FaceModel X, Y and Z; ColorFaceModel X and Y; and DepthFaceModel X and Y), describing 11 data, distributed in 1650 (11 lines $\times$ 150 frames) lines in the ``.txt'' file.</li> </ul> <p>(Former name: Libras-20)</p>
Data for Secure communication in IP-based wireless sensor networks via a trusted gateway publication
<p>This archive file contains the raw data obtained from Contiki sensor nodes during Cooja experiments in the folders e2e, terminate, terminate_1st and plaintext.</p> <p>The archive accompagnies the IEEE ISSNIP 2015 publication titled "Secure communication in IP-based wireless sensor networks via a trusted gateway" by Floris Van den Abeele, Tom Vandewinckele, Jeroen Hoebeke, Ingrid Moerman and Piet Demeester.</p> <p><br /> Also included is the data_parser python script that converts the raw data into CSV files that are parseable by R. The script contains the definitions of the contents of the raw data files.<br /> Finally, the R scripts that use the CSV files to generate the plots from the paper are also included.</p>
Ultimaker 2 Sensor Data
<p>This research data set includes accelerometer sensory data collected during a print job on Ultimaker 2 3D printer. We used ViFDAQ which is a mobile and wireless data-acquisition system developed at the VIRTUAL VEHICLE to capture the data. Sensors were installed on printer head (x,y,z achses) and on printing plate (x,y,z achses). During the print job, we conducted a series of events (e.g. vibrations on the printer plate, table ... ), to influence the quality of the printed object. We then applied a series of machine learning algorithms in order to detect the disturbances. A research paper is in production.</p> <p>ViFDAQ Info: http://www.v2c2.at/produkte/vifdaq/</p>
Automotive Sensor Data. An Example Dataset from the AEGIS Big Data Project
<p>This is an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected by using a BeagleBone single plate computer which has been developed at VIF to collect data for driving analytics. The BeagleBoard can be connected to the OBD2 interface of a vehicle to capture data from CAN bus and has been additionally equipped with further sensors (GPS, gyroscope, acceleration). The data in this research dataset was collected during 35 different trips conducted by one driver driving one vehicle in the Graz area in Austria.</p>
Preliminary Data 9 sensor plate
<p>Preliminary Data 9 sensor plate. Before Impact and initial investigations.</p> <p>Data sheet and plate dims. attached</p> <p>Layup [0,45,-45,90]2s, </p> <p>914 TS 5 34</p> <p> </p>
Data for A miniaturized magnetic field sensor based on nitrogen-vacancy centers
<p>Here, data sets as plotted in the preprint "A miniaturized magnetic field sensor based on nitrogen-vacancy centers" are uploaded. <br><br>The zip file Data_Zenodo_v3.zip contains a folder for each figure and one additional table supporting the findings in the preprint/publication. Folders of figures containing only images include the images in the preprint/publication as .png and .svg or .pdf data. Folders of figures containing plots include the plot as .png and .pdf aswell as the data points and fit parameters collected in a .xlsx, .csv or .h5 file.</p>
FloodNet flood sensor data - October 2020 to October 2023
<p>Before accessing our data, please review our <a href="https://docs.google.com/document/d/1jd5Q2UYj_0PwMRplFISmhT6LswpS08D9/edit">Data Access License Agreement</a>, which outlines the terms and conditions associated with the use of FloodNet data.</p><p>We also encourage you to thoroughly review this description, particularly the section regarding noise in sensor data. Floods are not the only events that show up in this dataset, and we want to reduce misinterpretation of data as much as possible. If you have questions about the dataset, please contact <a href="mailto:info@floodnet.nyc">info@floodnet.nyc</a>.</p><h2><strong>Data Summary</strong></h2><p>The contents of the CSVs are as follows:</p><p><strong>deployment_id - </strong>This is the unique identifier assigned to the sensor deployment</p><p><strong>time - </strong>The ISO-8601 date-time format in <strong>UTC</strong>. e.g. <strong>2023-02-22T14:20:30.000Z</strong></p><p><strong>depth_proc_mm - </strong>Time-series filtered depth in mm, sampled every ~1min using a series of custom noise filters designed to remove common manifestations of sensor noise. <strong>This is the recommended depth field to use.</strong></p><p><strong>depth_filt_mm - </strong>Time-series raw depth in mm, sampled every ~1min, except that values below 10mm are set to zero. This data has more noise, but you can use this to verify that depth_proc_mm did not accidentally filter a flood.</p><p><strong>depth_raw_mm - </strong>Time-series raw depth in mm, sampled every ~1min.</p><h3><strong>How do the sensors capture data?</strong></h3><p>The FloodNet sensors <strong>measure distance</strong> at a regular interval, currently <strong>once every 60 seconds</strong>. Certain older sensors were programmed to upload once every 5 minutes, so don't be alarmed if you see sensors with different upload rates.</p><p>The sensors work using ultrasound. They send out sound waves at a frequency outside the range of human hearing and then capture the echo when it bounces back off the closest reflective surface. That means that what the sensor is ultimately measuring is a difference in time. The sensor then uses the travel time to calculate the distance to that reflective surface (hopefully the ground!).</p><p>Sometimes the sensor will send out a ping, but will never receive an echo back. In these cases, the measurement is invalid and we record a null value. <strong>If you see a null measurement, this is likely the cause</strong>.</p><p>Distance is calculated using d=v∆t2 where d is the distance to the ground, v is the speed of sound, and ∆t/2 is the time that it takes for the sound to bounce back and return to the sensor (divided by two because it's traveling twice the distance we're measuring).</p><p>If you know a bit of physics, you might be saying "Wait a second, but the speed of sound isn't constant! It varies depending on the properties of the medium that it's traveling through!" Good catch! The speed of sound depends on a few factors including the molecular composition and density of the medium, in this case, "air". Assuming dry air (low humidity), the speed of sound, in units of m/s, is represented as v=20.05TC where TC is the ambient temperature in Celsius. The FloodNet sensors have an internal temperature sensor that it uses to do these calculations.</p><p>In order to calculate flood depth, the measured distance between the sensor and the ground below must be known under non-flooded conditions. This value is calculated through a dynamic calibration procedure that occurs at 5 AM daily, in which the distance measurements z collected over the previous three nights (between 10 PM and 5 AM are analyzed to determine the median value (z_nighttime~median). Day-time measurements are excluded from the calibration because of their temperature-related variance caused by direct sunlight producing erroneously high measurements on the sensor's internal temperature sensors. If the standard deviation of the night-time distance measurements exceeds 5 mm signifying either a flood or erroneous high variance), the previous day's z_nighttime~median calculation is used. To calculate flood depth D_t at time point t, the sensor's distance measurement at that time z_t) is subtracted from z_nighttime~median: Dt = z_nighttime~median - z_t</p><p>This dynamic calibration approach allows data collection to adapt to changes in sensor height, caused for example by a shift in signpost position if there is a vehicle impact, or seasonal variation in baseline z_nighttime~median readings.</p><h3><strong>Why is there noise in the data?</strong></h3><p>It may have occurred to you "How do you know that you're measuring the ground, and not a pigeon?" Well, the short answer is, we don't! That's why you may see some values that are not zero but are not actually floods.</p><p>The issue of noise is a challenge that our team is focused on and is actively researching. We have developed some methods designed to remove common manifestations of noise. The noise categories we have characterized are:</p><p><strong>Blips:</strong> a momentary jump in the data, where it returns to the previous value a sample or two later. This is often caused by someone or something passing under the sensor while it is taking its measurement.</p><p><strong>Boxes:</strong> a sudden and persistent jump in depth. This is commonly because something is placed beneath the sensor, such as garbage bags, bicycles, or loose trash.</p><p><strong>Pulse Chain:</strong> a chaotic chain of pulse/box-like noise that can occur for an extended period of time. This can be caused by aberrant reflections on uneven, sloped, or complex surfaces such as the spokes on a bike wheel. </p><p>The FloodNet data analysis pipeline uses a series of custom filters to address and filter some of this noise, and includes blip filters, detecting momentary jumps in the data, and box filters, detecting sustained jumps to a higher, near-constant value. These filters were optimized to minimize the risk of distorting the data and removing floods. We also employ a gradient filter that looks at the rate of change in depth to determine if the change observed is physically plausible. The gradient threshold used is 10 inches per minute, which is 7 times the maximum rate observed in Hurricane Ida in late summer of 2021.</p><h3><strong>Why isn't the sensor showing a flood?</strong></h3><p>As ultrasonic sensors need to be perpendicular and directly above the location that they are measuring, they are highly dependent on available mounting locations. To deploy these sensors, we are using existing street infrastructure such as signposts. This largely limits the locations on a street that we can use to install sensors, meaning that the sensors are not always located at the lowest point on the street, where floods would first develop. </p><p>Streets have variable topography and therefore the depth measurements our sensors capture will not always reflect the depth at every part of the street. For example, if our sensor is 4 inches higher than the lowest point in the area, our sensor would be reading zero for any flooding below 4 inches, and a reading of 10 inches actually corresponds to 14 inches of depth at the lowest point. That offset calculation requires detailed elevation maps and is information that our data scientists are working to compile for any of our sensor deployments, but is not yet available.</p>
Data for: Melt electrowriting enabled 3D liquid crystal elastomer structures for cross-scale actuators and temperature field sensors
<p>Liquid crystal elastomers have garnered significant attention due to their remarkable capability to undergo reversible strains and shape transformations under various stimuli. Early studies on LCE primarily focused on limited shape changes of macrostructures or quasi-3D microstructures. However, fabricating complex cross-scale LCE-based 3D structures still remains challenging. Here, we report a compatible method, the Melt-Electrohydrodynamic (Melt-EHD) 3D printing, to create LCE-based microfiber actuators and various 3D actuators across micrometer to centimeter scales, showcasing their actuation to thermal airflow stimulus. By controlling printing parameters, microfiber actuators with different diameters (5 μm~70 μm), and tunable properties including actuation strain (10%~55%), actuation stress (0~0.6 MPa), and large work density (~160J/kg) have been demonstrated. Under dynamic thermal airflow stimulus at 15 Hz, the microfiber actuators lift weights over 3500 times heavier than themselves. These 3D structures were obtained by depositing LCE microfibers along pre-programmed paths, including various gradient-responsive elementary structural units, 1 mm-sized microgripper, and various large area 3D lattice structures. In addition, by integrating a Deep Learning model, we have demonstrated, for the first time, large area (≥ centimeter scale), real-time (24 Hz sampling frequency), high-precision (~95%) LCE grid based spatial temperature field sensors with a spatial resolution of only 4 mm.</p>
LI-COR (LI-850) sensor data obtained by the Antarctic Modeling Observation System (ATMOS) project during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of CO2 (pCO2)
<p>LI-COR (LI 850) sensor data obtained by the "Antarctic Modeling Observation System" (ATMOS) project. These data were collected in the southern summer of 2021/2022 during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of seawater CO2 (pCO2sea)</p> <p>The LI-850 carbon dioxide analyzer was installed in the laboratory aft of H41 together with a balancer to measure the CO2 concentration of the water. The collection system occurs as follows: the ship's saltwater piping system collects seawater, when this water enters the balancer it generates turbulence. The turbulence generated causes the CO2 present in the water to come into balance with the air. The air that comes out of the balancer is pumped into the LI-850, by its internal pump, and thus, the equipment measures the concentration of CO2 present in the water. To ensure that the air inside the balancer is actually balanced with the seawater, the air leaving the LI-850 is pumped back into the balancer, closing the circuit. From these data it is possible to calculate pCO2sea.</p>
Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors"
<p>Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors".</p>
Data from: Handheld lidar sensors can accurately measure herbaceous biomass
<p>Data and code used in <em>Handheld lidar sensors can accurately measure herbaceous biomass</em>. Lidar data is provided for MLS and iPad sensors in <em>las_files.zip</em>. Las files are named by site, plot and subplot (e.g., FP-1-5). Data for response and predictors are avilable in <em>data.zip</em>. R code is provided for predictor creation, modeling, and figure creation in <em>Rcode.zip</em>. </p>
Raw data for the application of temperature and light intensity as intermittency sensors in a temporary pond in Jamaica
<p>Data provided represent the raw data collected for the paper on the application of temperature and light intensity as intermittency sensors in a temporary pond in Jamaica.</p>
Sensor data collected at the Nowe Czarnowo carp farm in Poland
<p>This dataset includes data collected from a land-based Carp farm in Nowe Czarnowo, Poland. Included are temperature, dissolved oxygen, and PH data collected at the farm at two locations. More details on the data can be found in the <a href="https://www.unive.it/pag/fileadmin/user_upload/progetti_ricerca/gain/documenti/GAIN_D11_Report_on_instrumentation_of_GAIN_pilot_sites.pdf">Report on instrumentation of site</a></p>
Sensor data collected at the Preore trout farm in Italy
<p>This dataset includes data collected from a land-based trout farm in Preore, Italy. Included are temperature, dissolved oxygen, nitrates, salinity collected at the farm. Further sample measurements of fish size are included. More details on the data can be found in the <a href="https://www.unive.it/pag/fileadmin/user_upload/progetti_ricerca/gain/documenti/GAIN_D11_Report_on_instrumentation_of_GAIN_pilot_sites.pdf">Report on instrumentation of site</a></p>
Diffuse-optical data set measured with a smartphone-based sensor on Potato Hill, Oregon, USA
<p>This data set contains both raw data and derived data obtained on Potato Hill, Oregon, on December 17th 2021 using a diffuse-optical, smartphone-based sensor. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the blue (405nm) or red (650nm) laser or spatial calibration data using a 9mm x 9mm calibration pattern. Spectral albedo measurements are contained in the subfilder ./Albedo, the raw images in ./Phone. The root directory contains the matlab code (Matlab R2021b) needed for analysis as well as the derived data.</p> <p>For analyzing the raw data set, use "CameraMatchPotatoHillFinal.m". It wraps around the function "CameraAnalysisFinal.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfGInf.m". It saves a derived data set (attenuation, absorption and scattering coefficients, albedos, absorption enhancement factor and snow density.</p> <p>The script "Albedo.m" analyzes the derived data set along with measured albedo and simulated albedo deposited in the file "snicar_120ppb.txt". The obtained albedo curves and black carbon mixing ratio are as shown in the below manuscript.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022)</p>
Raw Sensor Data for STRIDE Project J "Improving Work Zone Mobility through Planning, Design and Operations"
<p>This dataset contains the raw traffic data from 9 sensors located on I-59 southbound near Tuscaloosa, Alabama, from October 3 to October 16, 2016. These data were used in the research for STRIDE Project J "Improving Work Zone Mobility through Planning, Design and Operations" and described in the STRIDE Final Project for this project.</p>
Data corresponding to "The Impact of Multi-sensor Land Data Assimilation on River Discharge Estimation"
<p>This dataset is corresponding to the input and output files that were used in this study:</p> <p>Wu, W.-Y., Z.-L. Yang, L. Zhao, P. Lin (2022), Joint Multi-sensor Data Assimilation for Constraining Water Storages and its Impact on Global Discharge Estimation (<em>in revision, RSE</em>)</p>
Data accompanying: Performance characterization of low-cost air sensors for off-grid deployment in rural Malawi
<p>Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a one year deployment to rural Malawi using collocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modelling approaches to calibrate the electrochemical gas sensors: k-Nearest Neighbor (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, O<sub>x</sub>, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated. Hybrid models also were the most transferable approach when applied to deployment data collected in Malawi. We compared kNN-hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, due to proximity to residential biomass combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using collocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH > 70%) conditions and influence from emissions from nearby residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the one year deployment to Malawi. Data recovery (30-80%) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory grade monitoring infrastructure. </p>
ImPure Injection Molding Sensor Data - Trial 12th May
<p>ImPure project, open access data from PASCOE IM line. </p>
ImPure Injection Molding Sensor Data - Trial 17th May
<p>ImPure project, open access data from PASCOE IM line. </p>
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.