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1,961 results for “Sensing”
Dynamics of activation in the voltage-sensing domain of Ci-VSP
<p>This dataset contains unbiased molecular dynamics trajectories, stripped of water and lipid coordinates due to size constraints. It also contains full initial and final structures for all trajectories.</p><h3>Description of the data and file structure</h3><p>The data are deposited as a .tar.gz file: it can be unpacked by running tar -xzvf traj-data.tar.gz.</p><p>The trajectories are organized into two groups (group1 and group2), each containing a folder with stripped_trajs as .xtc files, as well as the full starting and ending coordinates (with waters and lipids) as intial_structs and final_structs. The models for the down--, down, up, and up+ states are in models.</p><p>The parameter file is civsd-amber-top.parm7 and the topology files with and without<br>the waters/lipids are civsd.psf and civsd-pro.psf</p><h3>Sharing/Access information</h3><p>Derived data (collective variables computed from the raw trajectories) are available as supplementary information to the accompanying publication.</p><h3>Code/Software</h3><p>Analysis of the trajectories can be performed using the MDAnalysis, MDTraj, PyEMMA, scikit-learn, and VMD, softwares along with custom codes written in Python/Jupyter notebooks and tcl. Custom codes are available at <a href="https://github.com/dinner-group/ci-vsd">https://github.com/dinner-group/ci-vsd</a>.</p>
GLAB-VOD: Global L-band AI-Based Vegetation Optical Depth Dataset Based on Machine Learning and Remote Sensing
<p>GLAB VOD is a Global L-band Ai-Based vegetation optical depth dataset with 18-day temporal and 25 km spatial resolution, covering 2002 to 2020. The dataset is created using a neural network with SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB) VOD product as a target (over 2015-2020) and brightness temperatures (TB) from the SMOS, AMSR-E, and AMSR-2 spaceborne missions alongside with a novel soil moisture dataset (CASM) as inputs. The GLAB-VOD dataset was created using a recently developed methodology previously used to create a long-term consistent soil moisture dataset CASM, adapted to the VOD retrievals. First, the TB and VOD signals were divided into fixed seasonal cycle and residuals, where the residual part of the signal contains sub-seasonal periodic signals, trends, extremes, and noise. Then, a multi-staged neural network training scheme was used to achieve internally consistent predictions by merging data from different sources without introducing biases or compromising data distribution. A side-product of this project is GLAB TB - a global long-term brightness temperature dataset that matches SMOS TB quality and spawns back to 2002. GLAB TB has daily temporal resolution and 25 km spatial resolution. </p>
Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media
<p>Dataset associated to the publication</p><p>"Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media"</p><p>By</p><p>David Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel and Pietro de Anna</p><p>Folder named "Figure_X" contains the original raw data, analysed data and source data for each plot within figure "X" on the manuscript and supplementary information.</p><p>We do not provide raw data for each replica as one flow&growth experiment consists in 50 large images for a total of about 12 GB per dataset. Thus, we provide here the original data for the Wild Type experiment and the control D-luxS mutant. The data for the replicas and other control experiment can be available upon request.</p><p>We provide Matlab scripts to read and analyze the original images.</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Automotive Environment
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the University of Birmingham using distributed radar sensors installed on the mobile laboratory. The data will be used to develop algorithms to extract the information needed for high-resolution multi-modal and multi-perspective sensing.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars.</p> <p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for the data collection campaign.</p> <p>Contact: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com, or m.s.gashinova@bham.ac.uk</p>
Dataset for "Degradable and Printed Microstrip Line for Chipless Temperature and Humidity Sensing"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Degradable and Printed Microstrip Line for Chipless Temperature and Humidity Sensing”. This work aims to study the humidity and temperature response of eco-friendly materials using a printed multi resonating microstrip line operating from 1.0 GHz to 2.6 GHz. The S12 signal of the resonator was measured using a vector network analyzer when varying the humidity inside a climatic chamber from 30% to 70% RH for temperatures of 15 °C, 25 °C and 35 °C. 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>
3DO Dataset | On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios
<p><strong>On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios</strong></p> <p>This repository contains the <strong>3DO dataset</strong> proposed in <a href="https://doi.org/10.1007/978-3-031-78354-8_13">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the 3DO dataset is provided at: <a href="https://github.com/StrohmayerJ/3DO/tree/main">https://github.com/StrohmayerJ/3DO</a></p> <p><strong>Dataset Description</strong></p> <p>The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (<strong>Note:</strong> The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)</p> <p>The directories <code>3DO/d1/</code>, <code>3DO/d2/</code>, and <code>3DO/d3/</code> contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., <code>3DO/d1/w1/</code>) contains a <code>csiposreg.csv</code> file storing the raw WiFi packet time series and a <code>csiposreg_complex.npy</code> cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, <code>csiposreg_complex.npy</code> is automatically generated by the provided dataloader.)</p> <p>Dataset Structure:</p> <p>/3DO</p> <p>├── d1 <em><-- day 1 subdirectory</em></p> <p> └── w1 <em><-- sequence subdirectory</em></p> <p> └── csiposreg.csv <em><-- raw WiFi packet time series</em></p> <p> └── csiposreg_complex.npy <em><-- CSI time series cache</em></p> <p>├── d2 <-- day 2 subdirectory</p> <p>├── d3 <-- day 3 subdirectory</p> <p> </p> <p>In [1], we use the following training, validation, and test split:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Day</strong></td> <td><strong>Sequences </strong></td> </tr> <tr> <td>Train</td> <td>1</td> <td>w1, w2, w3, s1, s2, s3, l1, l2, l3</td> </tr> <tr> <td>Val</td> <td>1</td> <td>w4, s4, l4</td> </tr> <tr> <td>Test</td> <td>1</td> <td>w5 , s5, l5</td> </tr> <tr> <td>Test</td> <td>2</td> <td>w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5</td> </tr> <tr> <td>Test</td> <td>3</td> <td>w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4</td> </tr> </tbody> </table> <p><em>w = walking, s = sitting and l= lying</em></p> <p><strong>Note: </strong>On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a>.</p> <p><a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a> Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-78354-8_13</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin",<br> title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios",<br> booktitle="Pattern Recognition",<br> year="2025",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="194--211",<br> isbn="978-3-031-78354-8" }</pre>
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>
Real-Time Frequency Tracking of an Electro-Thermal Piezoresistive Cantilever Resonator with ZnO Nanorods for Chemical Sensing (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Real-Time Frequency Tracking of an Electro-Thermal Piezoresistive Cantilever Resonator with ZnO Nanorods for Chemical Sensing", published in <em>Chemosensors</em> on 03 Jan 2019.</p>
Remotely-sensed Arctic Discharge Reanalysis (RADR)
<p>This repository contains the dataset RADR generated from the work <em>Recent changes to Arctic river discharge (2021), Nature Communications, DOI: 10.1038/s41467-021-27228-1</em></p> <p>The authors caution users that discrepancy in flow magnitude exists for a few small rivers between 1984-2013 and 2014-2019 due to the different climate forcings used for these two periods.</p>
Orthophotos, DSMs and interpretation files of the remote sensing assessment of archaeological damage and destruction at Nineveh, Iraq, during the ISIS occupation
<p>Archaeological heritage has long been threatened by damage or destruction during armed conflicts. Recently, however, deliberate destruction has increasingly become a major part of daily threats in some areas. In that context these datasets describe the results of a programme of remote sensing of damage at Nineveh, within a wider research initiative involving six years of monitoring in northern Iraq. Analysis of satellite imagery, low-and level airphotography observation were combined in a comprehensive assessment of the damage. These datasets present an updated topographic map of Nineveh and its city walls, with a summary of the damage encountered.</p>
Fabrication of a Soft Robotic Gripper With Integrated Strain Sensing Elements Using Multi-Material Additive Manufacturing
<p>With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the <em>in situ</em> printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated <em>in situ</em> strain sensing elements, showed that material selection affects the sensor signal significantly.</p>
A soft pneumatic actuator with integrated deformation sensing elements produced exclusively with extrusion based additive manufacturing
<p>In recent years, soft pneumatic actuators have come into the spotlight because of their simple control and the wide range of complex motions. To monitor the deformation of soft robotic systems, elastomer-based sensors are being used. However, the embedding of sensors into soft actuator modules by polymer casting is time consuming and difficult to upscale. In this study, it is shown how a pneumatic bending actuator with an integrated sensing element can be produced using an extrusion-based additive manufacturing method, e.g., fused deposition modeling (FDM). The advantage of FDM against direct printing or robocasting is the significantly higher resolution and the ability to print large objectives in a short amount of time. New, commercial launched, pellet-based FDM printers are able to 3D print thermoplastic elastomers of low shore hardness that are required for soft robotic applications, to avoid high pressure for activation. A soft pneumatic actuator with the in situ integrated piezoresistive sensor element was successfully printed using a commercial styrene-based thermoplastic elastomer (TPS) and a developed TPS/carbon black (CB) sensor composite. It has been demonstrated that the integrated sensing elements could monitor the deformation of the pneumatic soft robotic actuator. The findings of this study contribute to extending the applicability of additive manufacturing for integrated soft sensors in large soft robotic systems.</p>
Stacked remote sensing indices covering OAL-Austria
<p>Stacked indices derived from Sentinel-2A/B imagery (processing level 2A) covering the period from 2017/04/24 to 2022/01/16</p> <p># Normalized Difference Vegetation Index(Rouse etal. 1974) NDVI = (NIR ‒ R)/(NIR + R)<br> # Visible Difference Vegetation Index (Wang et al. 2015) VDVI = ((2*G) - R - B)/((2 * G) + R + B)<br> # Enhanced vegetation index (Schwieder et al. 2022) EVI=G*(nir-red)/(nir+C1*red-C2*blue+X)<br> # Excess green index ExGI=2*g-(r+b)<br> # Green chromatic coordinate GCC=g/(r+g+b)<br> # Normalized difference moisture index (Lastovicka et al. 2020) NDMI = (NIR − SWIR) / (NIR + SWIR)</p>
Data set for "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials"
<p>This excel file contains the raw data gathered with the measurements performed under different conditions of illumination for the different sensors. These data have been exploited in the paper "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials" DOI: 10.3390/s21113736</p>
Remote Sensing VQA - Low Resolution (RSVQA LR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page concerns the low resolution dataset.</p>
Remote Sensing VQA - High Resolution (RSVQA HR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page is about the high resolution dataset.</p>
Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"
<p>These datasets accompany the article published in <em>Remote Sensing </em>entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing".</p> <p>For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included: </p> <ul> <li><strong>Raw Metrics: </strong>the raw evaluations for each image returned by each of the 12 evaluation metrics. </li> <li><strong>Rankings:</strong> the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). </li> </ul> <p>The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.</p>
Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022
<p>This data set contains four parts:</p> <p>1) compressed folder with input parameters and results for the hydrodynamic model</p> <p>2) compressed folder with input parameters and results for the particle tracking</p> <p>3) compressed folder with satellite data </p> <p>4) code used in the article for hydrodynamic model, particle tracking and image processing</p> <p>Each folder contains a readme file,</p>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Remote sensing of river discharge (RSQ) estimates derived from multi-temporal Landsat width observations and BAM/geoBAM discharge inversion algorithms
<p><strong>This repository provides three data files in CSV format:</strong><br> 1. Gauge name, lat/lon information<br> 2. Gauge name, date, and multi-temporal river width extracted from Landsat<br> 3. Gauge name, date, and BAM/geoBAM estimates of river discharge with monthly Q priors</p> <p>Note: the multi-temporal river width data were extracted from Landsat imageries using RivWidthCloud, where the river centerline/orthogonal line definition and the cross-section sampling strategies were made prior to, and different from Feng et al. (2022). So the width values may be different from Feng et al. (2022) at some locations due to these differences. The discharge estimates were derived from BAM/geoBAM algorithms with width-only observations. More details of the technical workflow and the inner workings of BAM/geoBAM were provided in the literature below and papers therein.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Lin, P., D. Feng, C.J. Gleason, M. Pan, C.B. Brinkerhoff, X. Yang, H.E. Beck, R. Frasson (2023). Inversion of river discharge from remotely sensed river widths: a critical assessment at three-thousand global river gauges. <em>RSE</em>.</p> <p> </p> <p>Updated: 2022/6/17, 2023/1/19</p> <p> </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.