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412
datasets available to search
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Dataset results
412 results for “sensor data”
ImPure Injection Molding Sensor Data - Trial 16th May
<p>ImPure project, open access data from PASCOE IM line. </p>
Sensor data for "Influence of X-Ray Radiation on Historical Paper"
<p>This is the raw and processed data used in the paper "Influence of X-Ray Radiation on Historical Paper". It consists of the following:</p> <ol> <li>all images in the CR3 format, taken before, during, and after irradiation of the paper,</li> <li>the images converted to JPEG, as well as crops of the relevant parts stored as nupy arrays</li> </ol>
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>
Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 1)
<p>Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 1). For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 2)
<p>Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 2). For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
A full year of sensor data regarding a smart building room
<p>This dataset concerns a full year of clean data regarding a room in a smart building. The data considers the following: Outside temperature (x10)(ºC), Temperature 103 (x10)(ºC), Humidity 103 (x10)(%), Heat Index 103 (x10)(ºC), Occupation, AC status 103 (bool).</p> <p>The columns idenfied by x10 indicate that their value was multiplied by 10, to observe the raw value, please divide it by 10.</p>
Data from DHT22 a sensor humidity and temperature
<p>data from DHT22 a sensor humidity and temperature</p>
Source data - Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output
<p>Research data supporting the findings of "<em>Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output</em>" by Lennart Grabenhorst, Martina Pfeiffer, Thea Schinkel, Mirjam Kümmerlin, Gereon A. Brüggenthies, Jasmin B. Maglic, Florian Selbach, Alexander T. Murr, Philip Tinnefeld and Viktorija Glembockyte. For questions concerning this data, please reach out to Philip Tinnefeld or Viktorija Glembockyte.</p>
Ultimaker 2 sensor data for several print jobs
<p>This datasets were measured the same way as <a href="https://doi.org/10.5281/zenodo.54574">https://doi.org/10.5281/zenodo.54574</a>. But instead of capturing only events, the positions of the printer head and the printer plate were measured for all print jobs of Ultimaker 2 3D printer until 2016 and combined into one file.</p>
Data for: Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors
<p>Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89 to 91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring smalls-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.</p>
Sensor data from Almeria and Barcelona for the implementation and optimisation of INCOVER's irrigation system (FINoT controller and scheduler).
<p>The purpose of the data is to help local irrigation communities, city's landscape gardeners and others that perform irrigation activities in INCOVER’s Demo Sites 1 and 2 to define site-specific thresholds that deficit irrigation can be achieved and set limits under which the automated irrigation profile can operate by optimising water consumption. These sensor values are associated with the sensor technology exploited by FINT in INCOVER (FDR). Moreover, sensor streams can also help technology modellers in the area of IoT to get an example of syntactic formulation of data services that are based on IoT networked devices.</p>
Raw environmental indoor sensor data
<p>Dataset used in Publication:</p> <p>C. Arendt, S. Böcker and C. Wietfeld, "Data-Driven Model-Predictive Communication for Resource-Efficient IoT Networks," <em>2020 IEEE 6th World Forum on Internet of Things (WF-IoT)</em>, New Orleans, LA, USA, 2020, pp. 1-6, doi: 10.1109/WF-IoT48130.2020.9221019. <a href="https://ieeexplore.ieee.org/document/9221019" target="_blank" rel="noopener">[link]</a> <a href="https://cni.etit.tu-dortmund.de/storages/cni-etit/r/Research/Publications/2020/Arendt_WF-IoT/Arendt_WF-IoT_04_2020.pdf" target="_blank" rel="noopener">[authors version]</a></p> <p>When utilizing this dataset, proper attribution to the original publication is required. Please ensure to reference the aforementioned publication in any derived works or research outputs.</p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
Magnetic Distance Estimation Data from Gait Experiments with Magnetoelectric Sensors
<h2>Overview</h2> <p><br>This is the "Magnetic Distance Estimation Data from Gait Experiemtns with Magnetoelectric Sensors" dataset. <br>It represents a pilot study on magnetic motion tracking with novel magnetoelectric sensors during treadmill walking.<br>Therefore, it contains both technical (calibration) data and clinical (gait) data of five healthy participants.</p> <p>Example scripts for loading and processing data are available in the linked respository.</p> <p>The dataset is formatted according to the Brain Imaging Data Structure. See the `dataset_description.json` file for the specific version used.</p> <p>The work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the Collaborative Research Center CRC 1261 Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics. The data was recorded in the project B9 on "Magnetoelectric Sensors for Movement Detection and Analysis".</p> <p>All measurements were approved by the ethics committee of Kiel University (File number: A122/20) and conducted in accordance with the Declaration of Helsinki.</p> <h2><br>Details about the experiment</h2> <p><br>Magnetic motion tracking enables a relative tracking, in which the distance between each sensor and actuator node can be estimated. <br>The full setup contains two actuator nodes (a0, a1) and four sensor nodes (s0, s1, s2, s3).<br>Each actuator-sensor pair produces nine magnetic signals (x,y,z by x,y,z) as well as three magnetic dipole moment signals that represent the currents through the coils (actuators).<br>Additionally, each node was tracked with an optical motion capture (OMC) system. The resulting position and orientation data of the attached rigid body act as a reference (ground truth) to evaluate the magnetic estimation. <br>Subfolders sub-01 to sub-08 each contain up to three calibration tasks which each contain between 60 and 120s of arbitrary movement of one coil (wand-mounted) around one stationary sensor (base).<br>Subfolders sub-09 to sub-13 each contain two 120s walking tasks (0.5 and 1 m/s) of five subjects in total with the full sensor and actuator setup. Two actuators were mounted to the shanks, two sensors to the thighs and two sensors were placed stationary next to the threadmill. <br>The dataset also contains a folder with derived data, which contains calibration parameters for each actuator-sensor pair. See the provided matlab script for details on how to load, visualize, and compare the results.</p>
Data in support to the manuscript: Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation by Gianessi et al. (2024)
<p>The files contain data presented and discussed in the manuscript: Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation by Gianessi et al. (2024).</p> <div> <div>Gianessi, Stefano, Matteo Polo, Luca Stevanato, Marcello Lunardon, Till Francke, Sascha E. Oswald, Hami Said Ahmed, et al. “Testing a Novel Sensor Design to Jointly Measure Cosmic-Ray Neutrons, Muons and Gamma Rays for Non-Invasive Soil Moisture Estimation.” <em>Geoscientific Instrumentation, Methods and Data Systems</em> 13, no. 1 (January 16, 2024): 9–25. <a href="https://doi.org/10.5194/gi-13-9-2024">https://doi.org/10.5194/gi-13-9-2024</a>.</div> </div> <p> </p>
Floating-Gate MOS Transistor with Dynamic Biasing as a Radiation Sensor (raw data from journal article)
<p>This upload contains raw data from the manuscript "Floating-Gate MOS Transistor with Dynamic Biasing as a Radiation Sensor". The manuscript was published in Sensors 20, no. 11 (2020): 3329; DOI: https://doi.org/10.3390/s20113329</p> <p>The upload consists of .pdf file of the manuscript and .vsz files with raw data related to the figures in the manuscript. Each .vsz file is linked with raw data from the text files (.txt) and placed in a folder with the name and ordinal number of the figure in the publication. Additionally, a .pdf output file of the Veusz program (freely available) is placed in each folder.</p> <p>This work was supported in part by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558) and the Ministry of Education, Science and Technology Development of the Republic of Serbia (Project No. 43011).</p>
Dataset supporting publication: "Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city"
<p>Dataset supporting publication: “Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city” (publication available for download: <a href="https://zenodo.org/record/3901556">GEOFIT Zenodo</a>)</p> <p>Datasets resulting from monitoring activities of Sant'Apollinare systems and climatic parameters inside and outside the building (post-intervention monitoring).</p> <p>The article presents the data collected through an extensive research work conducted in a historic hilly town in central Italy during the period 2016-2017. Data concern two different datasets: long-term hygrothermal histories collected in two specific positions of the town object of the research, and three environmental transects collected following on foot the same designed path at three different time of the same day, i.e. during a heat wave event in summer. The short-term monitoring campaign is carried out by means of an innovative wearable weather station specifically developed by the authors and settled upon a bike helmet. Data provided within the short-term monitoring campaign are analysed by computing the apparent temperature, a direct indicator of human thermal comfort in the outdoors. All provided environmental data are geo-referenced. These data are used in order to examine the intra-urban microclimate variability. Outcomes from both long- and short-term monitoring campaigns allow to confirm the existing correlation between the urban forms and functionalities and the corresponding local microclimate conditions, also generated by anthropogenic actions. In detail, higher fractions of built surfaces are associated to generally higher temperatures as emerges by comparing the two long-term air temperature data series, i.e. temperature collected at point 1 is higher than temperature collated at point 2 for the 75% of the monitored period with an average of þ2.8 [1]C. Furthermore, gathered environmental transects demonstrate the high variability of the main environmental parameters below the Urban Canopy. Diversification of the urban thermal behaviour leads to a computed apparent temperature range in between 33.2 [1]C and 46.7 [1]C at 2 p.m. along the monitoring path. Reuse of these data may be helpful for further investigating interesting correlations among urban configuration, anthropogenic actions and microclimate variables affecting outdoor comfort. Additionally, the proposed dataset may be compared to other similar datasets collected in other urban contexts around the world. Finally, it can be compared to other monitoring methodologies such as weather stations and satellite measurements available in the location at the same time.</p>
Transition and Drivers of Elastic to Inelastic Deformation in the Abarkuh Plain from InSAR Multi-Sensor Time Series and Hydrogeological Data
<p>This repository contains the datasets used in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026430">Mirzadeh et al., 2023</a>. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research.</p> <p>Dataset 1: Envisat descending track 292</p> <ul> <li>Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: ALOS-1 ascending track 569</p> <ul> <li>Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: Sentinel-1 ascending track 130 and descending track 137</p> <ul> <li>Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions)</li> <li>Processor: ISCE/topsStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.</p>
Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
<p>Python figures in pickle format</p> <p>matplotlib version 3.5.1 </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.