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1,961 results for “Sensing”
Beat Pilot Tone (BPT): Simultaneous MR Imaging and RF Motion Sensing at Arbitrary Frequencies
<p>This data is for the manuscript "Beat Pilot Tone (BPT): Simultaneous MR Imaging and Radio-Frequency Motion Sensing at Arbitrary Frequencies" submitted to the journal Magnetic Resonance in Medicine. It is divided into one subfolder for each figure, with each folder containing the data necessary to reproduce the figure. Most of the data are saved in "cfl" format as used by the BART Computational Magnetic Resonance Imaging Toolbox (https://mrirecon.github.io/bart/). Other data are saved as Comma Separated Values (csv) or plain text files.</p> <p>Keywords and Categories: Magnetic Resonance Imaging, MRI, medical imaging, radio frequency, microwave, motion sensing, motion correction</p>
data_Systematic review and best practices for drone remote sensing of invasive plants
<p>We generated this dataset to compile a review article titled "Systematic Review and Best Practices for Drone Remote Sensing of Invasive Plants." </p>
ASSESSING THE CHLOROPHYLL-A VARIABILITY IN THE GULF OF GUINEA USING REMOTE SENSING DATA.
<h3>Introduction</h3> <p>The report begins by highlighting the importance of oceans in influencing the Earth’s climate and supporting marine life. It focuses on phytoplankton, which are crucial for the marine food web and global carbon cycle. The study aims to evaluate the variability of chlorophyll-a (Chl-a) and sea surface temperature (SST) in the Gulf of Guinea using satellite remote sensing data.</p> <h3>Materials and Methods</h3> <ul> <li><strong>Study Site</strong>: The Gulf of Guinea, located on the eastern edge of the Atlantic Ocean, bordered by several West African countries.</li> <li><strong>Data</strong>: Monthly Chl-a and SST data from the Aqua-MODIS satellite, covering the period from 2020 to 2022.</li> <li><strong>Methods</strong>: Analysis of satellite images using Python programming to evaluate spatiotemporal variability and conduct time series analysis.</li> </ul> <h3>Results and Discussion</h3> <ul> <li><strong>Chlorophyll-a Variability</strong>: The study found significant spatial and temporal variability in Chl-a concentrations, with higher values near the coastline due to nutrient inputs from rivers and coastal upwelling.</li> <li><strong>Sea Surface Temperature Variability</strong>: SST showed relatively uniform spatial distribution but notable seasonal and interannual variability, influenced by climatic phenomena like the West African Monsoon.</li> <li><strong>Interannual and Monthly Climatology Variability</strong>: The report discusses the seasonal patterns and the influence of environmental factors on Chl-a and SST.</li> </ul> <h3>Conclusion</h3> <p>The study concludes that Chl-a concentrations are higher near the coast due to nutrient inputs and coastal upwelling, while SST shows a consistent seasonal cycle. These findings provide insights into the dynamic nature of marine productivity in the Gulf of Guinea and the influence of environmental factors on phytoplankton biomass.</p>
Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations
<p>This repository contains the data and results associated to the publication submitted entitled "Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations".</p> <p>The results and data contain:</p> <ul> <li>Raw and processed ASTER DEM time series data stored in netcdf format (<em>Hala_surges_aster**.nc</em>): </li> </ul> <ol> <li>Raw DEM stack composed of 56 ASTER DEM.</li> <li>Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.</li> </ol> <ul> <li>Multi-temporal elevation change maps stored in geotiff format:</li> </ul> <ol> <li>multi-temporal elevation change results calculated from different DEMs during different period (<em>Hala_surges_[sensor]_[period]_dh_final.tif</em>).</li> <li>Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference ( <br><em>strm-c_x_n37_39_e96_e98_pentration_dh_final.tif</em>)</li> </ol> <ul> <li>Flow velocity time-series result processed by TICOI package stored in netcdf format:</li> </ul> <ol> <li>Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc</em>)</li> <li>Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc</em>)</li> </ol>
Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Remote Sensing with TerrSet Guide Tutorial Data
<p>The Remote Sensing Guide provides a comprehensive introduction to the TerrSet remote sensing software package. With clear instructions and more than 300 color illustrations, the text is ideal for students and professionals seeking a hands-on and guided exploration of the fundamental issues in remote sensing and image processing. This latest version is digital only (<a href="https://www.amazon.com/Remote-Sensing-TerrSet-2020-IDRISI-ebook/dp/B08V1LBT15/ref=sr_1_12?crid=34HSK8ZIPQ9PG&dchild=1&keywords=remote+sensing+guide&qid=1611854020&s=books&sprefix=remote+sens%2Cstripbooks%2C148&sr=1-12">buy on Amazon</a>) and works with TerrSet liberaGIS release as well as also previous versions of TerrSet. You can download data for each chapter separately, or use the Download All button to download all of them toether. Please note that all files are zipped.</p>
Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"
<p>Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"</p>
Data for publication: Developing self-calibrating system for fiber Bragg grating based guided wave sensing under changing temperature conditions
<p>Data for section 3 and section 4 Figure 7- Figure 12 i n. txt format for easy viewing without need for special licenses</p> <p>The work related to the automated system was supported by the Project ‘Guided waves based reference-free SHM using fiber Bragg grating sensors (REFFREE)’<br>(2020/39/D/ST8/00188) Granted by National Science Center, Poland.</p>
Remote sensing of multitemporal functional lake-to-channel connectivity and implications for water movement through the Mackenzie River Delta, Canada
<p>Dataset representing functional lake-to-channel connectivity in the Mackenzie Delta, NWT, Canada between 1984 and 2022 (final.class_20230324.feather), developed using Landsat 5, 7, and 9 optical imagery. </p> <p>Data in folders corresponds to data processing steps in scripts: https://doi.org/10.5281/zenodo.14618991</p> <p>Associated with manuscript: Remote sensing of multitemporal functional lake-to-channel connectivity and implications for water movement through the Mackenzie River Delta, Canada in WRR: Dolan, W., Pavelsky, T. M., & Piliouras, A. (2024). Remote sensing of multitemporal functional lake‐to‐channel connectivity and implications for water movement through the Mackenzie River Delta, Canada. <em>Water Resources Research</em>, <em>60</em>(4), e2023WR036614. https://doi.org/10.1029/2023WR036614</p>
Bifunctional upconverting luminescent-magnetic FeS2@NaYF4:Yb3+,Er3+ core@shell nanocomposites with tunable luminescence for temperature sensing†
<p>Advanced optically active materials have experienced significant development in recent years. Light-emitting materials combined with materials that exhibited magnetic properties result in bifunctional materials with an expanded range of capabilities and applications. New functionalities as a result of the core@shell structure enable interactions with the light and the external magnetic field as well. Continuous progress in materials science leads to innovations that enhance data storage and transmission, bioimaging, sensing, optical thermometry, and other applications crucial to advanced technology. In this research, we have focused on the optimization of the synthesis of a nano-sized FeS<span>2</span> material as an optically active, magnetic component of the core, and NaYF<span>4</span>:Yb<span>3+</span>,Er<span>3+</span> nanoparticles (NPs) as a temperature sensitive, up-conversion (UC) luminescence part of the shell. The synthesized core@shell type nanocomposite (NC) material FeS<span>2</span>@NaYF<span>4</span>:Yb<span>3+</span>,Er<span>3+</span> exhibits simultaneously the unique features of the core and shell components. The recorded UC emission spectra under 975 nm laser excitation show the possibility of tuning the UC luminescence color with the application of a highly absorbing FeS<span>2</span> component in the composite material. Moreover, the magnetic properties of the FeS<span>2</span> core nanoparticles and the synthesized NC were compared to confirm the potential application of the NC as a novel bifunctional luminescent-magnetic sensing platform. For both materials the luminescence color changed with the increasing laser power density, allowing color-tunable light generation. Additionally, optical temperature sensing properties of the NPs and NC were compared, resulting in a very high relative temperature sensitivity of <span>∼</span>2.0% K<span><span>−</span>1</span> for both materials.</p>
E-scape: consumer-specific landscapes of energetic resources derived from stable isotope analysis and remote sensing
<p>Energetic resources and habitat distribution are inherently linked. Energetic resource availability is a major driver of the distribution of consumers, but estimating how much specific habitats contribute to the energetic resource needs of a consumer can be problematic.</p> <p>We present a new approach that combines remote sensing information and stable isotope ecology to produce maps of energetic resources (<i>E</i>-scapes). <i>E</i>-scapes project species-specific resource use information onto the landscape to classify areas based on energetic importance.</p> <p>Using our <i>E</i>-scapes, we investigated the relationship between energetic resource distribution and white shrimp distribution and how the scale used to generate the <i>E</i>-scape mediated this relationship.</p> <p><i>E</i>-scapes successfully predicted the size, abundance, biomass, and total energy of a consumer in salt marsh habitats in coastal Louisiana, USA at scales relevant to the movement of the consumer.</p> <p>Our <i>E</i>-scape maps can be used alone or in combination with existing models to improve habitat management and restoration practices and have potential to be used to test fundamental movement theory. </p>
Preliminary on-ice remote sensing measurements during the MOSAiC expedition
<p>Several different remote sensing instruments were deployed on the sea ice floe next to RV Polarstern during the MOSAiC expedition (mosaic-expedition.org). Here, preliminary data from nine instruments for two observation periods (Nov 2019 and Sep 2020) is provided. Initial calibration was performed but data might change for the final datasets. Outliers were filtered and some time series smoothed. Data from Figure 10 in Nicolaus et al. (2021), "Overview of the MOSAiC expedition – Snow and Sea Ice", Elementa:</p> <p>Results from co-located active and passive remote sensing instruments (Table 2) looking at similar ice and snow conditions (Figure S4). (left) Measurements during a warming and storm event in November 2019 and (right) during a melting event in September 2020. (A) Air temperature and wind speed from the Polarstern weather station and snow surface temperature from the IR camera at the Remote Sensing Site (dashed blue line shows time periods with potential icing on the lens). (B) Radar backscatter at VV polarization from 2145 microwave scatterometers L-SCAT at 1.3 GHz and Ku/Ka-radar at 15 and 35 GHz (note the different y-scales). (C) Brightness temperature at V polarization from microwave radiometers: ELBARA at 1.4 GHz, ARIEL at 1.4 GHz looking at thin ice on a lead, HUTRAD at 7 and 11 GHz, SSMI at 19, 37, 89 GHz (not all available data shown). (D) Reflected GNSS data, i.e., reflectivity at the Remote Sensing Site (blue) and for sea ice next to Polarstern (red). In the plot titles the used incidence angle range is given. Vertical dashed lines mark the start of warming and/or storm events. (E) Exemple photographs of the remote sensing site during winter and summer.</p>
Data from: Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds
<p>This data repository contains the processed and extracted metrics from the Dutch land cover, country wide airborne laser scanning and Sentinel-1 and 2 datasets used as input predictor variables in the species distribution modelling step. The study area within the Netherlands comprised five Dutch provinces (Groningen, Drenthe, Overijssel, Gelderland, and Flevoland) for which both ALS and Sentinel data were available for the same year. The land cover metrics were derived using the Dutch land cover map from 2018 (LGN2018 or LGN8). The country-wide LiDAR point clouds were derived from the third Dutch national ALS flight campaign (AHN3, Actueel Hoogtebestand Nederland). The AHN3 dataset is openly accessible data available from (<a href="https://ahn.arcgisonline.nl/ahnviewer/">https://ahn.arcgisonline.nl/ahnviewer/</a>). The Sentinel datasets were processed using Google Earth Engine. </p>
Sensing Performance of Artificially Intelligent Nanopores Developed by Integrating Solid-State Nanopores with Machine Learning Methods
<p>Ionic current-time data obtained from measuring nanoparticles with the diameters of 90, 100, 150, 200, 220, 270, and 300 nm, using nanopores with a diameter of 300nm.</p> <p>Test_100nm_1 means a test data of nanoparticles with a diameter of 100 nm.</p> <p>Train_100nm_1 means a training data of nanoparticles with a diameter of 100 nm.</p>
Cross-correlated ambient data recorded on a distributed acoustic sensing array
<p>Distributed acoustic sensing (DAS) is a relatively new technology used in many geophysical applications. The versatility and high temporal-spatial resolution make DAS ideal for rapid deployment surveys such as earthquake-aftershock monitoring and hazard assessment. However, these applications often rely on trenched cable installations that are time consuming and cost prohibitive, or on existing telecom fibers that are limited in spatial coverage. We deploy a DAS array composed of six parallel linear subsections directly on ground surfaces with different conditions. We apply ambient interferometry and adopt a simplified spectral-analysis-of-surface waves (SASW) method to determine the average shear-wave velocity of the top 30 m (VS30). Our methodology results in robust VS30 estimates for each surface deployment subsection that are consistent with collocated 1 m-depth trenched cables. The implications of these findings support DAS as a viable method for non-invasive rapid deployment surface surveys for earthquake hazard assessment.</p>
Deep tissue localization and sensing using optical microcavity probes
<p>Data and code for publication Deep tissue localization and sensing using optical microcavity probes.</p>
Distributed sensing via the ensemble spectra of uncoupled electronic chaotic oscillators
<p>These are time-domain and frequency-domain data recorded from 4 realizations of a chaos-generating integrated circuit as a function of a control voltage and various input signals. They are provided to support replication of the results reported in the associated publication, as well as any further public-domain academic research in the field of chaotic oscillators, distributed sensing and related aspects, in compliance with the specified license terms and all applicable legal clauses.</p> <p>The following reference must be cited when using these data: Minati L, Tokgoz KK, Ito H, Distributed sensing via the ensemble spectra of uncoupled electronic chaotic oscillators, <em>Chaos Solitons & Fractals,</em> vol. 155, 111749, 2022, DOI 10.1016/j.chaos.2021.111749</p> <p>This work was partially supported by JSPS KAKENHI Grant Number 19H02191. Device realization was also supported by SCOPE (No. 0159-0013) from the Japan Ministry of Internal Affairs and Communications (MIC), with the assistance of the National Institute of Information and Communications Technology (NICT), and through the activities of VDEC, the University of Tokyo, in collaboration with Cadence Design Systems and Mentor Graphics.</p>
Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry"
<p>Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan, derived from distributed acoustic sensing data collected using a seafloor cable with seismic interferometry", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Code and data for Nguyen Le et al. "Robust optimal control of interacting multi-qubit systems for quantum sensing"
<p>This is the code and simulation data for the paper "Robust optimal control of interacting multi-qubit systems for quantum sensing" by Nguyen Le et al.</p>
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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)
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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.