Find research datasets worth reusing

Year 2012, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2012 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2013, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2013 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2014, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2014 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2015, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2015 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2016, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2016 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

openCC (other)Jan 2018View details →

Year 2017, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2017 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

openCC (other)Jan 2020View details →

SBC LTER: Comparison of spherical and planar light sensors

Seafloor irradiance in permanent plots at three subtidal reefs off Santa Barbara, California was compared in spring 2016 using data collected from paired planar and spherical PAR sensors. The three reefs (Isla Vista 34° 23.275’N, 119° 32.792’W; Mohawk 34° 23.649’N, 119° 43.762’W; and Naples 34° 25.342’N, 119° 57.102’W) ranged in depth from 5.8 m to 8.9 m (MLLW) and were chosen to represent a range of physical and biological characteristics. The purpose of the comparison was to develop a method for calibrating irradiance data collected from the two types of sensors.

opencc-by-4.0Dec 2019View details →

Sensor data from three different fishing ships for a period of one month

The purpose of the data is to analyse data produced by fishing ships in order to reduce fuel consumption. The data has been used in WP3 of DataBio project. Data were processed and supplied by the VTT and UPV/EHU DataBio project team.

opencc-by-4.0Dec 2019View details →

Stanford Hopkins Marine Laboratory Offshore Pressure Sensors

RBR bottom-mounted pressure sensor data for two stations located outside of wave breaking. Data details are described within the Matlab files. Data are used to estimated wave characteristics. Data used for manuscript "Wave dissipation by bottom friction on a rough rocky reef" by Gon, MacMahan, and Thornton in submission for JGR Oceans.

opencc-by-4.0May 2019View details →

EMG and Video Dataset for sensor fusion based hand gestures recognition

This dataset contains data for hand gesture recognition recorded with 3 different sensors.  sEMG: recorded via the Myo armband that is composed of 8 equally spaced non-invasive sEMG sensors that can be placed approximately around the middle of the forearm. The sampling frequency of Myo is 200 Hz. The output of the Myo is a.u  DVS: Dynamic Video Sensor which is a very low power event-based camera with 128x128 resolution DAVIS: Dynamic Video Sensor which is a very low power event-based camera with 240x180 resolution that also acquires APS frames. The dataset contains recordings of 21 subjects. Each subject performed 3 sessions, where each of the 5 hand gesture was recorded 5 times, each lasting for 2s. Between the gestures a relaxing phase of 1s is present where the muscles could go to the rest position, removing any residual muscular activation.   Note: All the information for the DVS sensor has been extracted and can be found in the *.npy files. In case the raw data (.aedat) was needed please contact   enea.ceolini@ini.uzh.ch elisa@ini.uzh.ch ==== README ====   DATASET STRUCTURE: EMG, DVS and APS recordings 21 subjects 3 sessions for each subject 5 gestures in each session ('pinky', 'elle', 'yo', 'index', 'thumb')   SINGLE DATASETS: - relax21_raw_emg.zip: contains raw sEMG and annotations (ground truth of gestures) in the format `subjectXX_sessionYY_ZZZ` with `XX` subject ID (01 to 21), `YY` session ID (01-03) and `ZZZ` that can be ‘emg’ or ‘ann’.   - relax21_raw_dvs.zip: contains the full-frame dvs events in an array with dimensions 0 -> addr_x, 1 -> addr_y, 2 -> timestamp, 3 -> polarity. The timestamps are in seconds and synchronized with the Myo. Each file is in the format `subjectXX_sessionYY_dvs` with `XX` subject ID (01 to 21), `YY` session ID (01-03).   - relax21_cropped_aps.zip: contains the 40x40 pixel aps frames for all subjects and trials in the format `subjectXX_sessionYY_Z_W_K` with `XX` subject ID (01 to 21), `YY` session ID (01-03), Z gesture ('pinky', 'elle', 'yo', 'index', 'thumb’), W trial ID (1-5), `K` frame index.   - relax21_cropped_dvs_emg_spikes.pkl: spiking dataset that can be used to reproduce the results in the paper. The dataset is a dictionary with the following keys: <ul> <li>- y: array of size 1xN with the class (0->4).</li> <li>- sub: array of size 1xN with the subject id (1->10).</li> <li>- sess: array of size 1xN with the session id (1->3).</li> <li>- dvs: list of length N, each object in the list is a 2d array of size 4xT_n where T_n is the number of events in the trial and the 4 dimensions rappresent: 0 -> addr_x, 1 -> addr_y, 2 -> timestamp, 3 -> polarity .</li> <li>- emg: list of length N, each object in the list is a 2d array of size 3xT_n where T_n is the number of events in the trial and the 3 dimensions rappresent: 0 -> addr, 1 -> timestamp, 3 -> polarity.</li> </ul>

opencc-by-4.0Feb 2020View details →

Dataset/Development of a novel calorimetry setup based on metallic paramagnetic temperature sensors

Dataset related to the publication "Development of a novel calorimetry setup based on metallic paramagnetic temperature sensors" submitted to Review of Scientific Instruments.

opencc-by-4.0Apr 2020View details →

Test data for Sonic Kayaks particulate matter sensor, temperature and GPS

These data sets are the first trials for adding a particulate matter sensor to the Sonic Kayak project https://fo.am/activities/kayaks/ There are three data files - gps.csv is the GPS co-ordinates, pm.csv is the particulate matter data (using a PMS7003), and temp.csv is the temperature data (two separate but identical digital thermometer sensors). Time is included in all datasets and can be used to align them. Together the data can be used to make a fine scale heat line map of particulate matter and temperature.

opencc-by-4.0May 2020View details →

Five-minute average horizontal wind velocity data combined from both sensors (which has been corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.

Dataset abstract The horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4 has been corrected for air-flow distortion. The measurements from both the port and starboad side anemometer were averaged to five-minute resolution and have been combined via vector averaging of the data. The ten meter neutral wind speed (U10N) has been estimated using ERA-5 surface heat fluxes, which were interpolated onto the ship's track, and the COARE 3.5 drag coefficient. This data set provides a continous and high-resolution record of the wind speed and direction near to the ship's location. Dataset contents <ul> <li>wind-observations-port-stbd-corrected-combined-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> Dataset license This five-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Aug 2020View details →

Load position and weight classification during carrying gait using wearable inertial and electromyographic sensors

This repository contains data from our study titled "Load position and weight classification during carrying gait using wearable inertial and electromyographic sensors." The following file types are included: - Basic participant demographics can be found in participants.xls. - README.pdf contains a detailed description of what can be found in each file. - SX_EMG.mat contains the EMG data for participant X. The file consists of EMG data for left and right erector spinae together with the time vector from that participant. - SX_Xsens.rar contains the Xsens data for participant X. This includes all joint angles and gait step time stamps from the sensors.

dryad36/100

Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors

The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets. Data were collected at the University of Arizona Maricopa Agricultural Center in Maricopa, Arizona. This site hosts a large field scanner with fifteen sensors, many of which are capable of capturing mm-scale images and point clouds at daily to weekly intervals. These data are intended to be re-used, and are accessible as a combination of files and databases linked by spatial, temporal, and genomic information. In addition to providing open access data, the entire computational pipeline is open source, and we enable users to access high-performance computing environments. The study has evaluated a sorghum diversity panel, biparental cross populations, and elite lines and hybrids from structured sorghum breeding populations. In addition, a durum wheat diversity panel was grown and evaluated over three winter seasons. The initial release includes derived data from from two seasons in which the sorghum diversity panel was evaluated. Future releases will include data from additional seasons and locations. The TERRA-REF reference dataset can be used to characterize phenotype-to-genotype associations, on a genomic scale, that will enable knowledge-driven breeding and the development of higher-yielding cultivars of sorghum and wheat. The data is also being used to develop new algorithms for machine learning, image analysis, genomics, and optical sensor engineering.

opencc-zeroAug 2020View details →

opencc-by-4.0Aug 2020View details →

Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition

This data contain multisensor image dataset constructed for benchmarking purposes. It contains multiple images of the constructed scenes -- on which objects made of different materials are placed to test image recognition scenarios. The scene is recorded from various angles by imagining sensors, i.e. HSI camera, HD camera on mobile chassis and MS Kinect to provide complete information.   Equipment The imaging was performed with use of three devices for three different approaches to data. Those three devices' imaging characteristics are widely different when it comes to angle and resolution which required them to be separately positioned to acquire the matching images. Therefore while HD camera was being transferred on the moving platform (chassis), both Kinect and SOC710 were placed on a stationary position which was moved between the frames by hand. Hyperspectral data Hyperspectral data acquisition was performed with Surface Optics SOC710 camera.  This camera records spectra at VNIR range $377-1046$ nm; the output image has dimensions $696 \times 520$ with 128 bands and $12$ bit dynamic range. The camera is equipped with sensor line translation unit and can be used from static stand as a conventional camera (i.e. it does not require mechanical translation of the observed sample or rotary stand, as in traditional ‘push broom’ hyperspectral cameras). The lighting was provided with four ambient lamps and adjusted for each scenario separately, so that most of the dynamic range of the camera was used and image saturation is avoided. Captured hyperspectral images were subject to a standard calibration procedure, including: the removal of a dark frame, spectral and radiometric calibration as well as reflectance normalization using the calibration panel.  3D point clouds The Kinect sensor incorporates several advanced sensing hardware. The depth sensor consists of the IR projector combined with the IR camera, which is a monochrome complementary metaloxide semiconductor (CMOS) sensor. The IR projector is an IR laser that passes through a diffraction grating and turns into a set of IR dots. The relative geometry between the IR projector and the IR camera as well as the projected IR dot pattern are known. If we can match a dot observed in an image with a dot in the projector pattern, we can reconstruct it in 3D using triangulation. Because the dot pattern is relatively random, the matching between the IR image and the projector pattern can be done in a straightforward way by comparing small neighborhoods using, for example, normalized cross correlation. The depth value is encoded with gray values; the darker a pixel, the closer the point is to the camera in space. The black pixels indicate that no depth values are available for those pixels. This might happen if the points are too far (and the depth values cannot be computed accurately), are too close (there is a blind region due to limited fields of view for the projector and the camera), are in the cast shadow of the projector (there are no IR dots), or reflect poor IR lights  HD Images The HD images were acquired using 5 Megapixel HD camera mounted on a mobile chassis made by Dawn Robotics, that allowed the camera to be moved freely on the scene. Both camera and mobile chassis was controlled by a Raspberry PI unit which was also responsible to position the camera in accord to the data being collected by other sources.    Data The dataset consists of three scenes consisting of various objects -- minerals, fruit, wood plastic and metal -- placed on a stand. The objects, depending on the view are partially covered and seen from different perspective. Each scene is captured from 8 different angles. Scene 1 (denoted SceneEagle) uses mostly inorganic materials, such as wood, metal, plastic and glass all placed on the vertical stand. Scene 2 (SceneFruit) uses fruits normal and artificial, that are similar on HD photography and 3D cloud of point, but differs in hyperspectral image. Scene 3 (SceneFruit2) uses the fruits but also includes printed full colour images of same fruits that are 2-dimensional.   The data are formatted as follows: - The HIS images are available in both \text{*.hdr} and \text{*.cube} formats. The separate files with calibrating panel is provided for each frame. -  Kinect clouds are provided in \text{*.obj} format, typical for Kinect output files. - Matched Hyperspectral clouds are also provided as \text{*.obj} files - HD photo files are provided in \text{*.jpg} files.   Acknowledgements This work has been supported by the National Science Centre, based on decision no. DEC2012/07/N/ST6/03656.

opencc-by-4.0Dec 2020View details →

Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"

The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at: <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA</a> Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication: <pre><code class="language-bash">conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code class="language-bash">source activate ReTiSAR_FA_freeze</code></pre> Directory "SMA sampling grids": <ul> <li>Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1)</li> </ul> Shell script "record_snr.sh": <ul> <li>Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations</li> <li>All captured signals are contained in the "SNR" directory</li> </ul> Matlab script "calculate_snr.m": <ul> <li>Visualize the raw captured input and output signals (like Figure 1 for all configurations)</li> <li>Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations)</li> <li>Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting)</li> <li>All generated plots are contained in the "SNR" directory</li> </ul> Shell script "record_noise.sh": <ul> <li>Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix)</li> <li>All captured signals are contained in the "EM32 measurements" directory</li> <li>Pictures of the measurement setup are contained in the "Pictures" subdirectory</li> </ul> Matlab script "calculate_EM32_noise_levels.m": <ul> <li>Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2)</li> <li>Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations)</li> <li>Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations</li> <li>Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5  for all configurations)</li> </ul>

opencc-by-4.0Sep 2020View details →

Development, Education, and Implementation of a Low-Cost Audio Sensor-based Autonomous Surveillance System for Smart and Connected Transportation Infrastructure Construction and Maintenance

Each DOT has to govern and oversee an enormous number of transportation construction and maintenance projects. However, since a transportation construction project entails several miles of a job site including numerous work tasks and equipment operations, it has been increasingly challenging for each DOT to consistently monitor progress of all projects in each State as well as efficiently evaluate work performance. In particular, with limited human resources and time, DOTs in Region 6 States have managed large-scale transportation construction and maintenance projects by a human inspection and recovered direct and indirect damages of transportation infrastructure systems caused from the recent natural disasters. In this demanding situation, DOT practitioners and project managers have long recognized the importance of automated monitoring and surveillance of transportation construction and maintenance processes that helps consistently track work progress and take immediate remedial action. As one promising supplement for site monitoring and human inspection, this project proposes a new approach for low-cost audio sensor-based autonomous site and safety surveillance of transportation construction and maintenance, which allows for faster, more convenient, and more accurate work zone monitoring. The proposed innovation using the sound-based site and safety monitoring framework possesses several competitive advantages over traditional site management and existing vision-based work monitoring methods, which not only sounds can be easily recognized and instantly analyzed by diverse sound sensors. In addition, this sound-based monitoring approach supports an unlimited range of monitoring angles and illumination levels with lightweight data processing and comparatively quick analytics. To achieve these goals, this study developed a low-cost wearable audio-sensor for automated work zone monitoring and real-time activity log generation. This new intelligent site and safety surveillance system is expected to support real-time monitoring of construction progress, evaluation of task performance, and rapid identification of safety issues in transportation construction and maintenance projects.

opencc-by-4.0Nov 2020View details →

Upper-body movements: precise tracking of human motion using inertial sensors

The Upper-body movements: precise tracking of human motion using inertial sensors is a dataset composed of 11 participants' IMU data (5 women + 6 men). This collection is divided into 6 motion sets containing different motions for the upper-body. Folder Structure subject -> set -> IMU position -> file e.g. subject01 -> set6 -> forearm -> Accelerometer.txt IMU placement  For data collection participants wore 4 IMUs: <ul> <li>1 on the chest</li> <li>1 on the right arm</li> <li>1 on the right forearm</li> <li>1 on the right hand.</li> </ul> Sets Each set includes: <ul> <li> set1 - flexion/extension of the forearm; abduction/adduction of the arm; anatomical position</li> <li> set2 - flexion/extension of the wrist; radial/ulnar deviation of the wrist; anatomical position</li> <li> set3 - flexion/extension and lateral flexion of the torso; anatomical position</li> <li> set4 - flexion/extension of the arm; flexion/extension of the torso; anatomical position</li> <li> set5 - flexion/extension of the arm; anatomical position; anatomical position</li> <li> set6 - flexion/extension of the torso; flexion/extension of the arm; anatomical position</li> </ul> Annotations This dataset is accompanied by the annotations.csv file. Each file row present "Set,Subject,Category,Segment,Type,Init,End": <ul> <li>Set - sets 1-6</li> <li>Subject - participant ID</li> <li>Category - relative or absolute. Refers to the joint angle.</li> <li>Absolute if the angle is obtained considering an anatomical plane as reference.</li> <li>Relative if the angle is obtained from one segment in relation to another.</li> <li>Type - segment at action (torso; right_arm_forearm; wrist; right_arm_sagittal)</li> <li>Init/End - time in seconds, describing the begin and end of the motion, respectively.</li> </ul>