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.
12
datasets available to search
ShareScore release 0.7.1
Dataset results
12 results for “mmWave radar”
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition
<p>This is a human activity recognition dataset with measurements from both mmWave radar and camera sensor. Meanwhile, we set multiple people scenario to mimic more realistic scenes. The other dataset collected in non-LOS(line-of-sight) environment, you can visit https://zenodo.org/record/7096889#.YynBvuhBwQ8 to get it. The mmWave radar sensors used in our experiments are composed of TI IWR6843ISK-ODS, eradar ESRR(corner radar), eradar EMRR(front radar). We appreciate the support of the eradar company, that provides corner radars and front radars for us, you can visit http://en.eradartech.com/ to get more information. </p>
An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting
<p>This repository is related with the next paper. Please cite us if this code is useful to you.</p> <p>Barral, V., Dominguez-Bolano, T., Escudero, C. J., & Garcia-Naya, J. A. An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting.</p> <h2>Python Scripts</h2> <ul> <li><em>count_plot.py</em>: Generates a plot comparing image tracking estimation, radar with DBSCAN, and radar with OPTICS. Use example:</li> </ul> <pre><code>python count_plot.py count_video_full.log count_radar_moving_average_30_optics.log count_radar_moving_average_30_dbscan.log "Video" "Radar with OPTICS" "Radar with DBSCAN" average_count_full_optics_dbscan.pdf </code></pre> <h2>Radar measurements</h2> <ul> <li><em>test_0.bag</em>: FMCW mmWave radar measurements from three IWR6843 devices (ISK and AOP). Is a ROS (Robotic Operative System) log, can be played with <code>rosbag play test_0.bag</code></li> </ul> <h3>Radar people counting logs</h3> <ul> <li> <p><strong>count_radar_moving_average_30_dbscan.log</strong>: radar people counting estimation using a 30 seconds moving average and DBSCAN as clustering algorithm.</p> </li> <li> <p><strong>count_radar_moving_average_30_optics.log</strong>: radar people counting estimation using a 30 seconds moving average and OPTICS as clustering algorithm.</p> </li> </ul> <h2>Image tracking</h2> <h3>Videos</h3> <ul> <li><em>test_0_cam_0_anonymized.mp4</em>: Camera 0 capture.</li> <li><em>test_0_cam_1_anonymized.mp4</em>: Camera 1 capture.</li> </ul> <h3>Image tracking logs</h3> <ul> <li><em>test_0_cam_0_interp.txt</em>: People count in camera 0</li> <li><em>test_0_cam_1_interp.txt</em>: People count in camera 1</li> </ul> <p>The files have the following format:</p> <p>frame_number, track_id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z The coordinates x, y, z are always -1 The bb fields define the bounding box of the detection If multiple people are detected in a frame, there are several lines with that frame_number, but with different track_ids.</p> <h3>Image people counting logs</h3> <ul> <li><strong>count_video_full.log</strong>: image tracking people counting estimation using a 30 seconds moving average.</li> </ul>
Data-driven Soil Moisture Sensing with mmWave Radar In Environment 1
<p>This is a millimeter-wave radar soil moisture dataset, which includes soil samples with 20 different moisture levels, ranging from 6.20% to 43.82% with approximately 2% intervals. In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 6.20%, 8.30%, 10.80%, 12.60%, 14.44%, 16.14%, 18.96%, 19.70%, 23.34%, 25.24%, 26.24%, 28.90%, 31.10%, 32.46%, 33.80%, 36.44%, 38.20%, 40.56%, 42.68%, and 43.82%, respectively.In the bin file names, the second number after "data" represents the height of the radar development board above the soil surface, measured in centimeters, specifically 16 cm, 20 cm, and 24 cm. The data was collected using the IWR1843 radar board, with 1 transmitting antenna and 4 receiving antennas. Each bin file contains 128 radar frames, with each frame consisting of 32 chirps, and each chirp containing 384 sampling points.</p>
Data-driven Soil Moisture Sensing with mmWave Radar
<p>This is a mmWave radar soil moisture dataset, which includes two different environments, specifically two conference rooms with different layouts. In Environment 1, soil with 20 different moisture levels was collected, ranging from 6.20% to 43.82%, with an interval of approximately 2%.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 6.20%, 8.30%, 10.80%, 12.60%, 14.44%, 16.14%, 18.96%, 19.70%, 23.34%, 25.24%, 26.24%, 28.90%, 31.10%, 32.46%, 33.80%, 36.44%, 38.20%, 40.56%, 42.68%, and 43.82%, respectively.In the bin file names, the second number after "data" represents the height of the radar development board above the soil surface, measured in centimeters, specifically 16 cm, 20 cm, and 24 cm. The data was collected using the IWR1843 radar board, with 1 transmitting antenna and 4 receiving antennas. Each bin file contains 128 radar frames, with each frame consisting of 32 chirps, and each chirp containing 384 sampling points.</p> <p>In Environment 2, soil with 20 different moisture levels was collected, ranging from10.0% to 48.0%, with an interval of 2%.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 10.0%, 12.0%, 14.0%, 16.0%, 18.0%, 20.0%, 22.0%, 24.0%, 26.0%, 28.0%, 30.0%, 32.0%, 34.0%, 36.0%, 38.0%, 40.0%, 42.0%, 44.0%, 46.0%, and 48.0%, respectively.In the bin file name, the second number after 'data' indicates the environment: 0 represents a static environment, and 1 represents a dynamic environment, where there is human movement.In the bin file name, 'train' and 'test' after 'data' represent the training and testing data, which were collected in two separate sessions.The data was collected using the IWR1843 radar board, which has 3 transmitting antennas and 4 receiving antennas. Each bin file contains 64 radar frames, with each frame consisting of 96 chirps, and each chirp containing 384 sampling points.</p>
Data-driven Soil Moisture Sensing with mmWave Radar In Environment 2
<p>This is a millimeter-wave radar soil moisture dataset, which includes soil samples with 20 different moisture levels, ranging from 10.0% to 48.0% with approximately 2% intervals.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 10.0%, 12.0%, 14.0%, 16.0%, 18.0%, 20.0%, 22.0%, 24.0%, 26.0%, 28.0%, 30.0%, 32.0%, 34.0%, 36.0%, 38.0%, 40.0%, 42.0%, 44.0%, 46.0%, and 48.0%, respectively.In the bin file name, the second number after 'data' indicates the environment: 0 represents a static environment, and 1 represents a dynamic environment, where there is human movement.In the bin file name, 'train' and 'test' after 'data' represent the training and testing data, which were collected in two separate sessions.The data was collected using the IWR1843 radar board, which has 3 transmitting antennas and 4 receiving antennas. Each bin file contains 64 radar frames, with each frame consisting of 96 chirps, and each chirp containing 384 sampling points.</p>
The mmWave radar point cloud dataset for person identification under various occluded conditions
<p>The dataset is collected by a COTS, Freqency Modulated Continuous Wave radar and an RGB-D camera. The dataset has been collected from 9 recruited individuals, which are instructed to walk behind the obstacle in an inbound/outbound manner, with each subject perfoms 5 consecutive minutes of walking. The RGB dataset is accessible on https://zenodo.org/record/8401329</p>
Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring
<h2><strong>Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring</strong></h2><h3><strong>Description</strong></h3><p>The archive contains a dataset that can be used for multi-sensor-based vehicle detection/classification. Each part of the dataset is divided into four separate subfolders. All the footage was collected from different parts of Tallinn during the late winter and early spring. The dataset contains 8393 frames. Each frame comes with a corresponding annotation in XML and YOLO formats and a JSON file containing mmWave radar point cloud data. </p>
mmWave IWR1642 dataset with interfering radar
<p>Dataset consists of IQ data for multiple targets taken in the presence of an interfering radar for short range mode of mmWave radar sensor IWR1642. AWR1642 is an interfering radar. Each receiving elements data is in the form of number of ADC (analog to digital converter) samples by number of chirps by number of frames collected experimentally. </p> <p>An interfering radar was placed at 30deg line of sight and -30deg angles for 2m, 3m and 5m distances.</p> <p>Eg: Receiver1_srr_twotarget_withjammer_5m_1 has one of the receiver's element data for which radar was configured to srr mode, interfering radar is at 5m at an angle of -30deg .</p> <p>Receiver2_srr_twotarget_withjammer_5m has one of the receiver's element data for which radar was configured to srr mode, interfering radar is at 5m at an angle of 30deg .</p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition(2)
<p>This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors. </p>
Breathing Rate and Heart Rate Dataset using Integrated mmWave FMCW Radar and Camera Steering System
<p>The presented dataset consists of raw and transformed CWT images of the breathing and heart waveforms obtained from a radar<br> and IP camera setup. The setup is a self-proposed setup with a mmWave radar mounted over an IP camera and can capture<br> the breathing and heart waveforms of the person in the room in front of the system in any orientation. The dataset can be used to estimate the vital signs using any machine learning model and important information about the respiration rate and pulse rate<br> can then be obtained. The dataset is a total of 1280 images- 720 raw and 720 processed. The processed dataset is labeled in six different classes. The first bifurcation is between breath and heart, breath signal is classified as low, normal, and high whereas the heart signal is classified as low, normal, and slightly low. The subject is oriented in differently so that the setup can steer towards the person and capture the breath and heart signals accordingly.</p>
A Clinical Trial Evaluating the Effectiveness and Safety of a mmWave Radar Based Sleep Respiratory Monitoring System
ClinicalTrials.gov study NCT06038006. IPD Sharing: Not stated. Countries: 1. Publications: 1.
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition(2)
<p>This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors. </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.