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1,961 results for “Sensing”
DATA REPOSITORY FOR: All-Optical Nuclear Quantum Sensing Using Nitrogen-Vacancy Centers in Diamond
<p><strong>DATA REPOSITORY:<br> ALL-OPTICAL NUCLEAR QUANTUM SENSING USING NITROGEN-VACANCY CENTERS IN DIAMOND</strong></p> <p>This data repository contains the raw data as measured on the experimental setup, the files required to do the data processing we performed on the raw data, the scripts to run the simulations described in the journal article, and the scripts to reproduce the plots shown in the article's figures.<br> <br> Use MatLab R2019b or later to run these files.<br> See ReadMe.txt for more information.</p>
Evaluation of forage quality in a pea breeding program using a hyperspectral sensing system
<p>The dataset includes the leaf reflectance spectra collected using a spectroradiometer and the associated biomass quality traits from laboratory-based near-infrared reflectance spectroscopy (NIRS) analysis from pea breeding trials. The data is used for analysis in the publication "Evaluation of Forage Quality in a Pea Breeding Program Using a Hyperspectral Sensing System".</p>
Our processed LoveDA dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed LoveDA dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>"</p>
Our processed CITY_OSM dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed CITY_OSM dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
A dataset of aerial images taken by UAV that we collected for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our private dataset of UAV aerial imagery for paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling
<p>This directory contains files related to the MSc graduation research project of Luc van Dijk of the Department of Physical Geography, Utrecht University. The project is titled "<em>Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling</em>" and was completed on April 14, 2023. Below is a description of the files in this directory.</p> <p><strong>Satellite_imagery.zip</strong><br> Folder containing all the pre-processed satellite images, that were exported from Google Earth Engine. Additional to the standard image bands, the bands 'NDSI', 'SC' and 'ASI' are present. These describe the Normalized Difference Snow Index, the Snow Cover and the Avalanche Susceptibility Index, respectively. The Google Earth Engine pre-processing script can be found here: https://code.earthengine.google.com/b7fe3ca48de410c9a3fff842f88a1e7f</p> <p><strong>SPHY_output_SnowStorage.zip</strong><br> Folder containing the SPHY output maps (variable: SnowStorage in mm) of the study domain.</p> <p><strong>SRCM.py</strong><br> The python-based Snow Redistribution Classification Model.</p> <p><strong>SRCM_output.zip</strong><br> Folder containing the SRCM output files. Each file has 8 bands: (1) snowmelt, (2) snow removal by avalanching, (3) eolian snow removal, (4) unexplained snow removal, (5) snowfall, (6) snow deposition by avalanching, (7) eolian snow deposition, (8) unexplained snow deposition.</p> <p><strong>SRCM_output_aggregated_wind_heatmaps.zip</strong><br> Folder containing the results of SRCM_output.zip, but only bands 3 and 7 and aggregated per month.</p> <p><strong>WindNinja_output_resampled.zip</strong><br> Folder containing the wind fields that were downscaled from ERA5-Land data using WindNinja. The wind fields were converted from vector (speed, direction), to 2-band raster layers (speed, direction) and resampled from 100 m to 30 m resolution.</p>
Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm
<p><strong>Aims</strong>: Remote sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because it is cost-effective and allows for coverage of large areas over a short period. This study investigated the relationship between multispectral remote sensing data from Landsat 8 and Sentinel 2 and species richness and diversity in mountainous and protected grasslands.</p> <p><strong>Locations</strong>: Golden Gate Highlands National Park, Free State, South Africa. </p> <p><strong>Methods</strong>: In-situ data of plant species composition and cover from 142 plots with 16 releves each were distributed across the study site and used to calculate species richness and Shannon-wiener species diversity index (species diversity. We used a machine-learning random forest algorithm to optimise the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetation indices were used to estimate species richness through random forest regression. </p> <p><strong>Results</strong>: This research found weak relationships between remote sensing vegetation indices and the diversity metrics, but significant relationships were found between some spectral bands and diversity metrics. Moreover, using machine learning random forest, the multispectral datasets exhibited strong predictive powers. In this investigation, for both sensors, near-infrared (NIR) seemed to be the most selected band to explain species diversity in mountainous grasslands.</p> <p><strong>Main</strong> <strong>conclusions</strong>: This finding further ascertains the efficiency of using NIR in vegetation mapping. This research shows that NIR, SAVI and EVI are the most adequate for predicting species richness and diversity in mountainous grasslands with relatively good accuracies.</p>
Dataset: Activation of skeletal muscle is controlled by a dual-filament mechano-sensing mechanism
<p>Steady-state and dynamic calcium dependence of force and <P2> for RLC/TNC probes in skeletal muscle. Dataset for article</p> <p>10.1073/pnas.2302837120</p>
Periodically disturbing biofilms reduces expression of quorum sensing regulated virulence factors in Pseudomonas aeruginosa
<p>The opportunistic pathogen <em>Pseudomonas aeruginosa</em> uses quorum sensing to control the expression of multiple virulence factors. In this dataset, we provide raw data demonstrating that periodically disturbing biofilms composed of <em>P. aeruginosa</em> using a physical force reduces the expression of multiple quorum sensing regulated virulence factors from the three major regulons. This dataset also contains cell density measurements of bacteria both the in biofilm and planktonic state under a variety of conditions that use physical force to manipulate the spatial positioning of bacteria. Finally, the data set contains raw outputs from a network logic model that described the core quorum sensing network. This data is affiliated with the manuscript entitled "Periodically disturbing biofilms reduces expression of quorum sensing regulated virulence factors in <em>Pseudomonas aeruginosa</em>."</p>
Lifestyle and sense of coherence: A comparative analysis among university students in different areas of knowledge
<p>Background: The concept of health has undergone profound changes. Lifestyle Medicine (LSM) consists of therapeutic approaches that focus on the prevention and treatment of diseases. It follows that the quality of life of university students directly affects their health and educational progress.</p> <p>Experimental Methodology: Socioeconomic, lifestyle (LS), and sense of coherence (SOC) questionnaires were administered to college students from three different areas. The results were analyzed for normality and homogeneity, followed by ANOVA variance analysis and Dunn and Tukey post hoc test for multiple comparisons. Spearman's correlation coefficient evaluated the correlation between lifestyle and sense of coherence; p values < 0.05 were considered statistically significant. </p> <p>Results: The correlation between LS and SOC was higher among males and higher among Medical and Human sciences students compared to Exact sciences. Medical students' scores were higher than Applied sciences and Human sciences students on the LS questionnaire. Exact science students' scores on the SOC questionnaire were higher than Human sciences students. In the LS areas related to alcohol intake, sleeping quality, and behavior, there were no differences between the areas. However, women scored better in the nutrition domain and alcohol intake. The SOC was also higher in men compared to women. </p> <p>Conclusion: The results obtained demonstrate in an unprecedented way in the literature that the correlation between the LS and SOC of college students varies according to gender and areas of knowledge, reflecting the importance of actions on improving students' quality of life and enabling better academic performance.</p>
Cross-Scene Hyperspectral Remote Sensing Wetland image data
<p>Two representative study areas in China, i.e., Yancheng and Huanghekou (i.e, Yellow River Estuary) wetlands, are selected.<br> For Yancheng wetland, there are two HSIs acquired by the Advanced Hyperspectral Imager (AHSI) aboard on China's Ziyuan1-02D (ZY1-02D) and GaoFen-5 (GF-5) satellites, respectively. For Huanghekou wetland, there are also two HSIs acquired by the AHSI aboard on China's ZY1-02D satellite in June 28, 2020 and September 29, 2021, respectively.</p>
Developing and Study of on-line Biotoxicity Sensing and Control Unit for Wastewater Treatment, based on a Cell Viability Monitoring Assay
<p>The quality of industrial, municipal or hospital waters varies greatly throughout the days and seasons. Because usually one of the first steps of water treatment plants (WWTPs) is biological treatment (after mechanical cleaning) it is vital that this step operates at full capacity. However, toxic compounds such as antibiotics, which are in use for human consumption or animal agriculture can greatly disrupt this stage of cleaning due to their antibiological nature of operation.</p> <p>Technion has developed a system in which toxicity to bacteria is measured on-line. It is based on following spectral changes in a dye, which is reduced as part of bacterial metabolism, which depends on the level of toxicity. The measured toxicity values are fed to a PID controller, which controls the operation of an AOP reactor.</p> <p>This system was developed as part of "Project O", a H2020 program of the European Commission.</p>
Mine Remote-Sensing Datasets
<p>While providing resources for socio-economic development, mine development has also given rise to problems such as disorderly and illegal mining, and has caused serious damage and pollution to the surface environment, among a series of sustainable development problems. Therefore, remote sensing interpretation of mine development (including mine scenes and mine targets) is automatically realized with the help of machine learning and deep learning techniques, which can provide important support for the assessment of sustainable development of mining areas at the regional scale. However, the current lack of high-precision mine scenes and target datasets at a wide-area scale restricts the development of intelligent interpretation of remote sensing for mine development. In this paper, a mine multi-scene classification dataset, as well as a mine target detection dataset, are constructed in Hubei and Jiangxi provinces in the middle reaches of the Yangtze River. The dataset is characterized by a wide coverage area, multiple mine types, high image resolution, and multi-scale. In particular, for the mine multi-scene classification dataset, a feature fusion mine scene classification method based on a stereo attention mechanism is proposed, in which the salient features of the mine scene in three directions, horizontal, vertical, and channel, are modeled by the constructed stereo attention mechanism, and a feature activation module is used to avoid generating feature disappearance; For the mine target detection dataset, a mine target detection method based on a multi-scale dual-attention mechanism is proposed, which avoids feature bias through a multi-scale feature fusion module, while using a dual-attention mechanism to highlight salient features of the mine. The experimental results of the two models show that the mine multi-scene and target detection dataset constructed in the paper has good quality and can provide a benchmark dataset for the intelligent interpretation research of remote sensing for mine development in the Yangtze River Basin and even and global scale.</p>
UrbAlytics - Remote Sensing tools for Urban Heat Island Assessment and Climate Change Adaptation through Nature-Based Solutions
<p>Urban Heat Island (UHI) is considered one of the significant problems posed to human beings due to the urbanization and industrialization of human civilization. The leading causes of UHI are the vast amounts of heat urban structures produce as they absorb and re-radiate solar radiation and anthropogenic heat sources. The issue mainly affects cities or metropolises with a vast population and a thriving economy. The problem will worsen significantly in the future due to the predicted three billion people living in urban areas worldwide. Due to the severity of the problem, accessing up-to-date information layers that can support city planners and decision-makers in the context of climate resilience is a demanding problem nowadays.</p> <p><strong>UrbAlytics</strong> is an experimental sub-project of the H2020-funded project <a href="https://ai4copernicus-project.eu/"><strong>AI4Copernicus</strong></a> that aims to bridge Artificial Intelligence with Earth Observations, producing information layers that can support city planners and decision-makers in the context of climate resilience and related challenges in urban areas. This research investigates, thanks to the joint expertise of the partners <a href="https://www.latitudo40.com/"><strong>Latitudo 40</strong></a> and <a href="https://www.landsrl.com/land-research-lab"><strong>LAND Research Lab®</strong></a>, the Urban Heat Island (UHI) effect, evaluating its impacts on cities, assessing Ecosystem Services provided by Blue and Green Infrastructures and proposing a set of Nature-Based Solutions (NBS) for climate adaptation and extreme heat mitigation. </p> <p><strong>The dataset</strong></p> <p>This dataset is the tool's output of a fully automated workflow realized during the project and tested for the cities of <strong>Milan</strong> and <strong>Naples</strong>, pilot users of the experiment. The choice of Milan and Naples allows for different readiness levels, data availability, and urban-climatic conditions.<br> For each city, the dataset contains the following layers for the analysis period 2018-2022.</p> <p><strong> HEATWAVE POTENTIAL RISK (HPR)</strong></p> <p>Risk Assessment mapping concerning extreme heat, considering the severity of the heat island phenomenons, the exposure of sensitive age groups and the vulnerability due to city morphology and surface materials. The risk assessment is the first step in defining a methodology that aims to assess the effectiveness of mitigation and adaptation strategies to climate extremes. It's a value in [0,1], where the higher the value higher the risk.</p> <p><strong> MICROCLIMATIC PERFORMANCE INDEX (MPI)</strong></p> <p>The role of vegetation in the city in abating the Heat Island effect has been widely demonstrated. In this context, deploying Urban Green Infrastructure is recognized as one of the most important strategies to mitigate UHI and promote a resilient city environment. The significance of the mitigation role of the Heat Island phenomenon that vegetation assumes makes it necessary to map Urban Green Infrastructure to estimate a cooling potential. Estimating the microclimatic performance of urban vegetation is crucial to plan adaptation and mitigation actions for the UHI effect. In this work, up-to-date Tree Cover Density and Land Cover maps have been produced using machine learning applied to Sentinel-2 satellite imagery. Those maps have been interpolated and combined, creating 20 Blue and Green Infrastructures classes. Each category's microclimatic performance score was attributed based on evapotranspiration potential, shading and albedo. The output is a map with integer values in [1, 20], where the lower the value higher the microclimatic performance. </p> <p><strong> PARK COOL ISLANDS (PCI)</strong></p> <p>Park Cool Islands layer identifies the most performing areas during extreme summer heatwaves, according to their size and relevant characteristics, providing reliable information to citizens and urban planners about the safest and coolest areas during extreme heatwaves. Since the green areas' type and composition can influence their cooling effects, we considered both the size and composition of urban parks to identify the most performing green areas in terms of the Park Cool Island effect. <strong> </strong>The layer distinguishes between major and minor Park Cool Islands. <em>Major PCI</em> includes areas covered by at least 50% of tree canopy coverage and bigger than 2 hectares with an estimated cooling distance of 300 m buffer<strong>. </strong><em>Minor PCI</em> includes green areas whose surface is between 1 and 2 hectares as well as those green areas bigger than 2 hectares but covered by less than 50% of tree canopy coverage, with an estimated cooling distance of 100 m buffer.</p> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us <a href="mailto:giovanni.giacco@latitudo40.com">giovanni.giacco@latitudo40.com</a>, <a href="mailto:giulia.castellazzi@landsrl.com">giulia.castellazzi@landsrl.com</a></p>
Peptide sequencing based on host-guest interaction-assisted nanopore sensing
<p>Source data files of "Peptide sequencing based on host-guest interaction-assisted nanopore sensing"</p>
Data set for "Implications of lidar depolarized signal for deformed raindrops remote sensing"
<p>This is the dataset used in the paper "Implications of lidar depolarized signal for deformed raindrops remote sensing"</p>
Peptide sequencing based on host-guest interaction-assisted nanopore sensing
<p>Source data for "Peptide sequencing based on host-guest interaction-assisted nanopore sensing"</p>
Dataset of in-situ and remote sensing measurements of precipitation in the inner Antarctic (Dome-C 75°S 123°E) for the years 2014-2021
<p>The database contains original data collected between 2014 and 2021 at the Concordia Station (Dome-C, Antarctica, 75°S, 123°E) using three automatic instruments:</p> <p>1) A flatbed scanner (ICECAMERA) provides information on the shape and size of precipitation on an hourly basis.</p> <p>2) An automatic depolarization LIDAR provides the height, structure and phase of the cloud that originated the precipitation on a 5-minute basis. The height range for the LIDAR is between 20 and 7000 meters.</p> <p>3) A microwave radiometer (HAMSTRAD) provides the local temperature at the altitude where precipitation is formed.</p> <p>The combination of three instruments made it possible to 'label' each precipitation grain with its size, shape parameters, temperature, altitude of formation, and surface meteorological data.</p> <p>Each yearly <strong>DATA_YYYY.rar</strong> data set is organized into daily directories, where all valid LIDAR false color plots, HAMSTRAD data, and ICECAMERA images are collected, along with processed numerical data for all the ice grains collected. HYSPLIT back trajectories with Dome-C as the final point are also included.</p> <p>The dataset's content is explained in the <strong>data legend.doc</strong> file</p> <p><strong>MATLAB models.rar </strong>includes multiple MATLAB Canonical and SVM models that automatically classify the type of cloud-originating precipitation (at Dome C) based on the relative abundance of different ice grain shapes. Details and instructions for their use can be found in the <strong>Legend for MATLAB classifiers.docx</strong> document.</p>
Upper Extremity Function, Shoulder Position Sense and Disability Level İn Patients With Multiple Sclerosis
ClinicalTrials.gov study NCT03846336. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Sense to Act: An Interoceptive Sensibility Intervention for Musculoskeletal Pain
ClinicalTrials.gov study NCT06285864. IPD Sharing: YES. Countries: 1. Publications: 16.
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.