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1,772 results for “sensors”
NYU FloodSense Gowanus canal mounted sensor depth
<p>Water depth level in mm from a sensor mounted mounted above the Gowanus Canal, Brooklyn, NY (40.674490, -73.994458).</p> <p>The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. This one is used for data validation.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Data is collected at ~5min intervals. Time fields are in local time (New York). Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>One type of erroneous data has been observed:</p> <ul> <li>There are ~1% rises in distance measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is prelimary and is for prototyping purposes. Not to be used as a reliable data source as it is.</p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p> <p> </p>
Data Set of Extracted Summary Statistics from Equipment Sensor Data
<p>This data set was generated in accordance with the semiconductor industry and contains values of summary statistics from sensor recordings of the high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data. Out of the sensor data, values of summary statistics are extracted. These are values like mean, standard deviation and gradients. To keep the entire production as stable as possible, these values are used to monitor the whole production in order to intervene in case of deviations.</p> <p>After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given data is provided. The aim is to find correlations between the wafer test data and the values of summary statistics in order to identify the root cause.</p> <p>The given data is divided into four data sets: "XTrain.csv", "YTrain.csv", "XTest.csv" and "YTest.csv". "XTrain.csv" and "XTest.csv" represent the values of summary statistics originating in the production chain separated for the purpose of training and validating a statistical model. Included are 114 observations of 77 parameters (values of summary statistics). The "YTrain.csv" and "YTest.csv" contain the corresponding wafer test data (144 observations of one parameter).</p>
Migration Route of Swiss Ring Ouzels with Multi-Sensor Geolocator
<p>This GeoLocator Datapackage contains the raw data for 5 multi-sensor geolocators and 4 light-level geolocators data equipped on Alpine Ring Ouzels (Turdus torquatus alpestris) in Switzerland between 2017-2020. The data has been processed using the GeoPressureR package to produce trajectories for the 5 multi-sensor tags. Code can be found on Github <a href="https://github.com/Rafnuss/migration-route-of-swiss-ring-ouzels">Rafnuss/migration-route-of-swiss-ring-ouzels</a>. The raw data has been used in <a href="https://doi.org/10.1111/jav.02860">10.1111/jav.02860</a></p> <p> </p>
Nonlinearity corrections and bad pixel masks for the WINTER sensors
<p>Nonlinearity corrections and bad pixel masks for the WINTER sensors to be used with https://github.com/winter-telescope/winternlc. </p>
MOSID (Microcontroller On-chip Sensor IDentification): A dataset of readings from the internal monitoring sensors of STM32L152RTXX microcontrollers during the stimulation of their electronic activity
<p>The MOSID (Microcontroller On-chip Sensor IDentification) dataset consists of 5 acquired data subsets (6,72 GB total, compressed into 560 MB), each collected during different experiments and periods using various equipment (HMP4040, DF1731SB & HM305) and acquisition strategies. These subsets contain readings from the temperature and voltage sensors embedded in 20 STM32L-DISCOVERY devices. The data was captured during the execution of 5 different workloads as stimuli, repeated over 20 iterations. The stimuli employed are as follows:</p><ol><li>20x20 Long-type matrix product.</li><li>20x20 Float-type matrix product.</li><li>Algorithm for ascending sorting, Bubble Sort.</li><li>Algorithm for 2D-point clustering, Convex Hull.</li><li>Encryption algorithm AES 128-bit.</li></ol><p>The subsets are structured according to the folder format "X_Y," where X is the manually assigned number to the board, and Y is the corresponding number for the executed algorithm. Within each of these folders, files are present in the format "data_Z.txt," where Z represents the iteration number to which the file belongs. In total, the dataset comprises 9600 files with a final size of approximately 7 GB. The different presented subsets are as follows:</p><ul><li>ACQ1: Derived from the experiment named "Automatic Acquisition 1 (HMP4040)" conducted using a daisy-chain topology (20 out of 20 boards, 2000 files).</li><li>ACQ2: Derived from the experiment named "Automatic Acquisition 2 (HMP4040)" conducted using a daisy-chain topology (20 out of 20 boards, 2000 files).</li><li>ACQ3: Derived from the experiment named "Individual Acquisitions (HMP4040)", performed board by board from idle conditions (20 out of 20 boards, 2000 files).</li><li>ACQ4: Derived from the experiment named "GOLD SOURCE DF1731SB Acquisitions" conducted using a partial daisy-chain setup (2 devices at a time, 18 out of 20 boards excluding boards , 1800 files).</li><li>ACQ5: Derived from the experiment named "HANMATEK HM305 Acquisitions" conducted using a partial daisy-chain setup (2 devices at a time, 18 out of 20 boards, 1800 files).</li></ul><p>In each "data_Z.txt" file, starting from the 5th line, temperature and voltage raw ADC conversions from the sensors are provided, captured during the execution of the stimulus in successive lines. Additionally, a table (Table_UIDS.csv) with metadata for each of the boards used in the experiments is included, which is needed in order to normalize the data in terms of ºC and Volts.</p><ul><li>BOARD_NUM, which contains the manually assigned board number.</li><li>UID, which contains the Unique Identifier of the board assigned by the manufacturer.</li><li>T_CAL_1, which holds the calibration value of the board's temperature sensor at 30ºC.</li><li>T_CAL_2, which holds the calibration value of the board's temperature sensor at 100ºC.</li><li>VREFINT_CAL, which contains the calibration value of the board's voltage sensor.</li></ul>
Auralization of virtual microphone array sensors considering coherence loss by atmospheric turbulence for two moving monopole sources
Open the record for dataset details and reuse information.
Aircraft Marshaling Signals Dataset of FMCW Radar and Event-Based Camera for Sensor Fusion
<p><strong>Dataset Introduction</strong></p><p>The advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is arguably the most commonly explored application. Nevertheless, more suitable benchmarking datasets than currently available are needed to assess and compare the merits of the different proposed solutions and explore a broader range of scenarios than simple hand-gesturing a few centimeters away from a radar transmitter/receiver. Most current publicly available radar datasets used in gesture recognition provide limited diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and make available a new dataset that combines FMCW radar and dynamic vision camera of 10 aircraft marshalling signals (whole body) at several distances and angles from the sensors, recorded from 13 people. The two modalities are hardware synchronized using the radar's PRI signal. Moreover, in the supporting publication we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks), and can be used to create an sparse event-based representation of the radar data. In this way the dataset can be used as a two-modality neuromorphic dataset.</p><p><strong>Synchronization of the two modalities</strong></p><p>The PRI pulses from the radar have been hard-wired to the event stream of the DVS sensor, and timestamped using the DVS clock. Based on this signal the DVS event stream has been segmented such that groups of events (time-bins) of the DVS are mapped with individual radar pulses (chirps).</p><p><strong>Data storage</strong></p><p>DVS events (x,y coords and timestamps) are stored in structured arrays, and one such structured array object is associated with the data of a radar transmission (pulse/chirp). A radar transmission is a vector of 512 ADC levels that correspond to sampling points of chirping signal (FMCW radar) that lasts about ~1.3ms. Every 192 radar transmissions are stacked in a matrix called a radar frame (each transmission is a row in that matrix). A data capture (recording) consisting of some thousands of continuous radar transmissions is therefore segmented in a number of radar frames. Finally radar frames and the corresponding DVS structured arrays are stored in separate containers in a custom-made multi-container file format (extension .rad). We provide a (rad file) parser for extracting the data out of these files. There is one file per capture of continuous gesture recording of about 10s.</p><p>Note the number of 192 transmissions per radar frame is an ad-hoc segmentation that suits the purpose of obtaining sufficient signal resolution in a 2D FFT typical in radar signal processing, for the range resolution of the specific radar. It also served the purpose of fast streaming storing of the data during capture. For extracting individual data points for the dataset however, one can pool together (concat) all the radar frames from a single capture file and re-segment them according to liking. The data loader that we provide offers this, with a default of re-segmenting every 769 transmissions (about 1s of gesturing).</p><p><strong>Data captures directory organization (</strong><a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a><strong>)</strong></p><p>The dataset captures (recordings) are organized in a common directory structure which encompasses additional metadata information about the captures.</p><p>dataset_dir/<stage>/<room>/<person>-<gesture>-<distance>/ofxRadar8Ghz_yyyy-mm-dd_HH-MM-SS.rad</p><p>Identifiers</p><ul><li>stage [train, test].</li><li>room: [conference_room, foyer, open_space].</li><li>subject: [0-9]. Note that 0 stands for no person, and 1 for an unlabeled, random person (only present in test).</li><li>gesture: ['none', 'emergency_stop', 'move_ahead', 'move_back_v1', 'move_back_v2', 'slow_down' 'start_engines', 'stop_engines', 'straight_ahead', 'turn_left', 'turn_right'].</li><li>distance: ['xxx', '100', '150', '200', '250', '300', '350', '400', '450'] (in cm). Note that xxx is used for none gestures when there is no person present in front of the radar (i.e. background samples), or when a person is walking in front of the radar with varying distances but performing no gesture.</li></ul><p>The test data captures contain both subjects that appear in the train data as well as previously <i>unseen</i> subjects. Similarly the test data contain captures from the spaces that train data were recorded at, as well as from a new <i>unseen</i> open space.</p><p><strong>Files List</strong></p><p><a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a></p><p>This is the actual archive bundle with the data captures (recordings).</p><p><a href="https://zenodo.org/api/records/10359770/draft/files/rad_file_parser_2.py/content">rad_file_parser_2.py</a></p><p>Parser for individual .rad files, which contain capture data.</p><p><a href="https://zenodo.org/api/records/10359770/draft/files/loader.py/content">loader.py</a></p><p>A convenience PyTorch Dataset loader (partly Tonic compatible). You practically only need this to quick-start if you don't want to delve too much into code reading. When you init a DvsRadarAircraftMarshallingSignals class object it automatically downloads the dataset archive and the .rad file parser, unpacks the archive, and imports the .rad parser to load the data. One can then <i>request from it </i>a training set, a validation set and a test set as torch.Datasets to work with<i>.</i> </p><p><a href="https://zenodo.org/api/records/10359770/draft/files/aircraft_marshalling_signals_howto.ipynb/content">aircraft_marshalling_signals_howto.ipynb</a></p><p>Jupyter notebook for exemplary basic use of loader.py</p><p><strong>Contact</strong></p><p>For further information or questions try contacting first M. Sifalakis or F. Corradi.</p><p> </p>
GEDII Wearable Sensors Dataset of 10 Research Teams
<p>The dataset contains Bluetooth (proximity), Infrared (face-to-face), Speech (microphone) and Accelerometer (body activity) data of 10 research teams collected during 5 working days in each team. Altogether N=105 team members. Socio-demographic data as well as round-robin ratings regarding friendship and advice seeking is included. Data was collected using Sociometric badges by Humanyze (formerly Sociometric Solutions).</p> <p>The present dataset has been produced within the context of a EU funded H2020 research project called “Gender-Diversity-Impact: Improving Research and Innovation through Gender Diversity. (GEDII)”. The project has been running from 2015 to 2018 with the aim to develop new tools and methods for doing research on the impact of gender diversity in R&D teams. In order to address these questions, GEDII makes use of a variety of research methods, including a cross country survey, bibliometric & patent analysis and detailed case studies with R&D teams.</p> <p>The dataset is distributed as R package.</p>
Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Sensor Response Files for the Relativistic Proton Spectrometer aboard NASA's Van Allen Probes
<p>This data set provides the NASA Van Allen Probes Relativistic Proton Spectrometer (RPS) sensor response function files. These files provide the sensor’s response to protons and electrons as a function of energy and angle of incidence.</p>
Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement", published in <em>Journal of Micromechanics and Microengineering </em>on 05 Nov 2019.</p>
In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection (Data)
<p>Origin projects, figures and LabVIEW software used for the article "In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection", published in <em>Sensors </em>on 22 Jan 2020.</p>
Phase characteristic optimization of resonant MEMS environmental sensors (Data)
<p>Origin projects and figures used for the article "Phase characteristic optimization of resonant MEMS environmental sensors", published in the proceedings of Sensoren und Messsysteme 2018, 19. ITG/GMA-Fachtagung; 26.06.2018 to 27.06.2018; Nürnberg, Germany.</p>
MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles (Data)
<p>Origin project and figures used for the article "MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles", published in <em>Chemosensors</em> on 04 Aug 2021.</p>
Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution - Dataset
<p>This repository contains the data associated with the paper: Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution.</p> <p>Bulot, F.M.J.; Russell, H.S.; Rezaei, M.; Johnson, M.S.; Ossont, S.J.J.; Morris, A.K.R.; Basford, P.J.; Easton, N.H.C.; Foster, G.L.; Loxham, M.; Cox, S.J. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution. <em>Sensors</em> <strong>2020</strong>, <em>20</em>, 2219.</p> <p><a href="https://doi.org/10.3390/s20082219">https://doi.org/10.3390/s20082219</a> </p> <p>It contains:</p> <p>- DHT22.csv measurements from the DHT22 humidity and temperature sensor</p> <p>- dusttrak.csv measurements from the DustTrak</p> <p>- ops.csv measurements from the OPS TSI 3330</p> <p>- sensors.csv measurement from the low-cost PM sensors</p> <p>- sensors_blank.csv measurements from the low-cost PM sensors during the blank test</p> <p> </p> <p>sensors_blank.csv contains the following variables:</p> <ul> <li>Bin1 to Bin15: particle numbers for different bin sizes reported by the Alphasense OPCR1, as defined by its user's manual available here https://www.alphasense.com/products/optical-particle-counter/</li> <li>SamplingPeriod: sampling period of the Alphasense OPCR1 in seconds</li> <li>SFR: sampling flow rate of the Alphasense OPCR1 in ml/s</li> <li>PM1, PM25, PM4, PM10: PM concentrations reported by the sensors in ug/m3.</li> <li>gr03um to gr100um: particle number concentrations for different bin sizes for the Plantower PMS5003, in particle per 100ml, as defined by its user's manual https://aqicn.org/air/view/sensor/spec/pms5003-manual_v2-3</li> <li>n05 to n10: particle number concentrations for different bin sizes for the Sensirion SPS30, in particles per cm3, as defined by its user's manual: https://www.sensirion.com/fileadmin/user_upload/customers/sensirion/Dokumente/9.6_Particulate_Matter/Datasheets/Sensirion_PM_Sensors_Datasheet_SPS30.pdf</li> <li>humidity and temperature: relative humidity (%) and temperature (Celsius) recorded by the SHT35 sensors</li> <li>sensor: sensor identifier</li> <li>site: name of the air quality monitor containing the sensors</li> <li>exp: name of the experiment considered</li> <li>source: source of PM used</li> <li>variation: peak or stable concentration</li> <li>date: date and time of the experiment</li> </ul>
Supramolecular Self-Healing Sensor Fiber Composites for Damage Detection in Piezoresistive Electronic Skin for Soft Robots
<p>Self-healing materials can prolong the lifetime of structures and products by enabling the repairing of damage. However, detecting the damage and the progress of the healing process remains an important issue. In this study, self-healing, piezoresistive strain sensor fibers (ShSFs) are used for detecting strain deformation and damage in a self-healing elastomeric matrix. The ShSFs were embedded in the self-healing matrix for the development of self-healing sensor fiber composites (ShSFC) with elongation at break values of up to 100%. A quadruple hydrogen-bonded supramolecular elastomer was used as a matrix material. The ShSFCs exhibited a reproducible and monotonic response. The ShSFCs were investigated for use as sensorized electronic skin on 3D-printed soft robotic modules, such as bending actuators. Depending on the bending actuator module, the electronic skin was loaded under either compression (pneumatic-based module) or tension (tendon-based module). In both configurations, the ShSFs could be successfully used as deformation sensors, and in addition, detect the presence of damage based on the sensor signal drift. The sensor under tension showed better recovery of the signal after healing, and smaller signal relaxation. Even with the complete severing of the fiber, the piezoresistive properties returned after the healing, but in that case, thermal heat treatment was required. With their resilient response and self-healing properties, the supramolecular fiber composites can be used for the next generation of soft robotic modules</p>
Piezoresistive sensor fiber composites based on silicone elastomers for the monitoring of the position of a robot arm
<p>Combining conductive fillers like carbon black with elastomers allows the development of soft elastomer strain sensors that can reach very large elongations, an important requirement for many robotic applications. However, when the conductive filler is introduced in the polymer, significant stiffening occurs, affecting the mechanical properties, e.g. Young’s Modulus, of the soft structure. In this attempt, single piezoresistive fiber composites were successfully fabricated, without drastically increasing the stiffness. Two silicone elastomers that are widely used in robotic applications were examined as matrix materials. Furthermore, modeling the stresses exerted on the fiber inside the composite was successfully used to predict the detachment of fiber inside the matrix, observed by visual inspection. For the PDMS based composite, pre-straining improved sensor properties, which could be confirmed for the monitoring of the movement of the crane robot. The results showed that the pre-strained piezoresistive sensor fiber-matrix composites positions of the robot crane can be monitored even at low strains.</p>
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements
<p>Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function.</p>
Radar measurements on drones, birds and humans with a 77GHz FMCW sensor.
<p>This data set contains radar measurements on birds, humans and six different drones with a total of 75868 samples.</p> <p>The sensor was a frequency modulated continuous wave (FMCW) radar operating at 77 GHz with a mechanically scanning antenna.</p> <p>The 'ReadMe.txt' file contains a detailed description of the data.</p> <p>The data set is used in [1] where only FM-sweeps corresponding to azimuth index 54 to 203 are used (out of the provided 256), or 150 sweeps. </p> <p>When using this data set please refer to:</p> <p>[1] A. Karlsson, M. Jansson and M. Hämäläinen, "Model-Aided Drone Classification Using Convolutional Neural Networks," <em>2022 IEEE Radar Conference (RadarConf22)</em>, 2022, pp. 1-6, doi: 10.1109/RadarConf2248738.2022.9764194.</p> <p>The data in version 1.0 and 2.0 is identical apart from the format, ".mat" in 1.0 and ".npy" in 2.0</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.