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1,961 results for “Sensing”
Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020
<p>Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.</p> <p> </p> <p>All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.</p> <p> </p> <p> </p> <p>Reference:</p> <p>Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.</p> <p>Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.</p> <p>Zhong, B., A. Yang, Q. Liu, S. Wu, X. Shan, and X Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.</p>
Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data
Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible, and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30,040 hectares) along a 3,322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in productivity, opening up possibilities to fully harness remote sensing data and reliably scale up from traits to map global productivity in a more direct, efficient, and cost-effective manner.
Extended Supplement for De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter
<p>This electronical extended supplement presents an adaptive frequency-wavenumber filter suppresses the incoherent seismic noise while amplifying the coherent wave field in seismic distributed acoustic sensing (DAS) data. We analyse the response of the filter in time and spectral domain, and we demonstrate its performance on a noisy data set that was recorded in a vertical borehole observatory showing active and passive seismic phase arrivals. In these data we can suppress the noise up to 20 dB. This data publication serves as a repository for data an plotting scripts.</p>
Training course materials: Introduction to Remote Sensing of Water Quality in Lakes
<p>Dataset including ESA Sentinel-2 MSI and Sentinel-3 OLCI L1B scenes (L1B), atmospherically corrected reflectance products (C2RCC), image subsets, and intermediate water quality products from the Copernicus Land Service and ESA Lakes_cci processing chain <em>Calimnos. </em>Data are in support of a recorded course introducing the theoretical and practical basis for water quality observation of lakes through remote sensing. See https://monocle-h2020.eu/Resources/Training</p> <p>The data package contains the following:</p> <p><strong>input_data.zip </strong>- unprocessed (L1B / L1C) satellite scenes of MSI and OLCI instruments over the Razelm/Sinoe lagoon and Black Sea</p> <p><strong>Output_S3_OLCI.zip - </strong>all outputs derived from the Sentinel-3 OLCI scene, including C2RCC and <em>Calimnos </em>(including POLYMER atmospheric correction and biogeochemical estimates as in the Lakes_cci)</p> <p><strong>Output_S2_MSI_Full_Scene_C2RCC.zip</strong> - atmospherically corrected (C2RCC algorithm) full Sentinel-2 MSI scene</p> <p><strong>Output_S2_MSI_Subset_C2RCC.zip </strong>- subset of Sentinel-2 MSI scene and atmospherically corrected results (C2RCC algorithm) </p> <p><strong>Output_S2_MSI_Calimnos_PML.zip </strong>- subset of Sentinel-2 MSI scene atmospherically corrected using POLYMER and biogeochemical estimates, using <em>Calimnos </em>as in the Copernicus Land Service. </p>
Conjugated polymers for microwave applications: untethered sensing platforms and multifunctional devices
<p>Raw data for the manuscript "Conjugated polymers for microwave applications: untethered sensing platforms and multifunctional devices".</p> <p>In reference to the manuscript, the dataset is organized in three subsets, each related to one of the Figures in the main texts of the article:</p> <ol> <li>The return losses (S11 spectra) from the reconfigurable microwave resonators, in the form of .s1p files, realized with different tuning conjugated polymers;</li> <li>The electrochemical and microwave characterization of an enzymatic reaction cell / microwave resonator assembly;</li> <li>The microwave characterization of a amplitude- and frequency-tunable resonator.</li> </ol>
Place re-making and sense of place after quarrying and social-ecological restoration - dataset
<p>The dataset provides coded input data for the study "Place re-making and sense of place after quarrying and social-ecological restoration". In this study, we examine how people’s place making and sense of place are reconfigured through quarrying. We investigate perceptions of sense of place after quarrying and social-ecological restoration in a limestone region of the Czech Karst in the Czech Republic. The dataset contains the result of our survey of 400 participants - visitors to the PLA Czech Karst who visited one of the eight study quarries and were questioned in the quarry. When selecting participants for our study, purposive sampling was applied to receive a gender-balanced sample of participants of various ages. We used a paper questionnaire suitable for the data collection directly in the field. The questionnaire was anonymous. </p>
APRA500: a 500 m annual paddy rice dataset for monsoon Asia using multisource remote sensing data
<p>This dataset provides 500m-grid paddy rice maps of monsoon Asia (some countries) from 2000 to 2021.</p> <p>*** Updated paddy rice map for 2021</p> <p>*** The data file is in “.tif" format</p> <p>*** Temporal Resolution: Yearly</p> <p>*** Pixel size: 500 m</p> <p>*** Projection information: EPSG: 4326</p> <p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
Remote Sensing Satellite Video Dataset for Super-resolution
<p>This is a satellite video super-resolution dataset generated from "Jilin-1" video satellite.</p> <p>Training set: 189 clips; Test set: 12 clips.</p> <p>More details can be found in our paper published in IEEE TGRS: https://ieeexplore.ieee.org/document/9530280</p> <p>If you find our work helpful, please cite our paper. Thank you very much!</p>
Remote sensing and field information aid in predicting the presence of the terrestrial orchid Cyclopogon lute-albus
<p>Tropical montane cloud forest is one of the most threatened ecosystems, in central Veracruz, Mexico. Within this ecosystem, terrestrial orchids are strongly dependent on forest conditions and may be sensitive to environmental change. We applied field surveys of abiotic and biotic factors associated with the presence of the terrestrial orchid <em>Cyclopogon luteo-albus</em> and combined this with correlative niche modeling approaches to evaluate its potential distribution under two different environmental sets and two different extents. Layers of environmental information were obtained from Landsat imagery and interpolated bioclimatic and soil property layers. Existing species records were used as training data by sampling five forest fragments in central Veracruz, utilizing herbarium and global biodiversity information facility databases. The resulting predictions were tested by sampling at 15 sites of potential distribution. The model predicted the presence of <em>C. luteo-albus</em> with a reliability of 80%. The most important variables of models derived from interpolated layers coincide with site-relevant parameters suggesting the utility of these tools to further explore species suitabilities at larger geographic extents. Potential distribution mapping is an important tool to identify key areas for conservation and priority areas for future studies of species and partially resolves the lack of records of many orchid species.</p>
RASTtk annotations of Chromobacterium vaccinii MWU328 and quorum sensing mutant MWU328W
<p>RASTtk genome annotations of Chromobacterium vaccinii MWU328 (wild type) and a spontaneous frameshift mutant in the cviR (quorum sensing autoinducer receptor) gene MWU328W.</p>
Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water
<p>As: Raman spectra from E. coli lysate sample after exposing to As in DI water</p> <p>Cr: Raman spectra from E. coli lysate sample after exposing to Cr in DI water</p> <p>As_TapWater: Raman spectra from E. coli lysate sample after exposing to As in tap water</p> <p>WasteWater_FineTune_Dataset: Raman spectra from E. coli lysate sample after exposing to As in waste water</p> <p>WasteWater 'Unknow' Dataset: Raman spectra from E. coli lysate sample after exposing to waste water</p>
Nearness sensing and interest traces
<p>performed with psychology students, in two different institutions, where the purpose was to study interest influence in psychological proximity. NSense has been installed in Android smartphones carried by a population of 50 students of the two different universities, numbered User1 to User50. The students carried NSense around during their daily routines, for 2 days: 05.04.2017-06.04.2017. The readings obtained show a total of 15 students out of the original universe of 50. The traces collected comprise the following fields: - date: DD/MM HH:mm - own_device_name: unique identifier of the device, Userx - connected_device_name: peer observed via Wi-Fi Direct (range of 0 to 100 meters) at date. - latitude, longitude: GPS coordinates of the device - distance: relative distance computed in meters via Wi-Fi Direct. - sound: discrete value for the surrounding sound activity: ALERT, NORMAL, QUIET - physical activity: discrete value for the type of activity (MOVING or STATIONARY) - tct: total contact time. Starts counting after 1 day of use. Corresponds to the sum of contact duration between i and j on a time window of duration h. - social_strength_minute: level of social interaction derived from our work NSense: A People-centric, non-intrusive Opportunistic Sensing Tool for Contextualizing Social Interaction, IEEE Healthcom2016.Social strength of node i towards node j, in a specific hourly sample h, for day d. Node i computes the social strength towards j by adding the different weighted ADs. t has been set for 24 hours - si: Social interaction of node i towards j, computed at instant t - p: propinquity: measures the probability of social interaction occurring over time. - ema_cd. Exponential moving average of the total contact duration between nodes i and j - additional columns: types of interests defined in NSense.</p>
Immersive Analytics: The Influence of Flow, Sense of Agency, and Presence on Performance and Satisfaction
<p>This are the datasets and R scripts for the experiments described in the paper "Immersive Analytics: The Influence of Flow, Sense of Agency, and Presence on Performance and Satisfaction".</p>
Data and code for "Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"
<p>Data and code for <br>"Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"</p> <p>All R code used for the analysis is provided in the folder <em>code</em>. <br>Data and intermediate results are provided or stored in the folders <em>data </em>and <em>tmp_data</em>.<br>All results including figures will be stored in the folder <em>results</em>. </p> <p>R version: 4.3.1</p> <p>To carry out the entire analysis the code should be run in the provided order:</p> <p>1) Code to run non-metric multidimensional scaling (NMDS) for habitat groups and <br>produce Fig. 1b (habitat map and legend for Fig 1a: data/eunis_gl_habitat_ger_990m.tif,eunis_gl_habitat_ger_990m_legend.clr)<br> <br>2) Code to generate grassland vitality maps and time series from 1985 to 2021 (Fig. 3). <br>Grassland vitality maps on 30m for all grasslands in Germany provided in data/glv_1985-2021.zip.</p> <p>3) Code to model relation of grassland vitality to five drought indices (VPD, temperature, CWB, soil moisture, precipitation),<br>output are Fig. 4, Fig. S1, Tab. 1.</p> <p>4) Code for trend analysis of drought sensitivity based on 5-, 10-, and 15-year moving windows, output are Fig. 5, Fig. S2. </p> <p>5) Code to model drought sensitivity of grassland habitat groups and habitat types, output are Fig. 6 and table with sensitivity per habitat type. </p>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data Fusion and Sensing Technology_sensing system design for grippers_v0
<p><span>The dataset includes pr</span><span>ecisely made Computer-Aided Design (CAD) models in .step format, representing essential components such as metallic frame, silicone pad, plastic case, plastic grid, and tactile board with integrated proximity sensor. Additionally, the dataset provides a complete assembly of multi-modal sensor model in .f3z format.</span></p> <p><span>Furthermore, the dataset includes essential design files for the electronic infrastructure, including a precisely engineered circuit schematic design file in .sch format and a printed circuit board layout design file in .brd format. Additionally, the dataset offers visual aids in the form of digital images (.png) showcasing top view, PCB model, ToF module, and sensor assembly configuration. These resources enable researchers to leverage the dataset's comprehensive capabilities for advanced investigations and practical implementations in sensor technology and design.</span></p>
Data and scripts for "Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling"
<div> <div> <div>This repository contains field observations and processed data from the Weather Research and Forecasting (WRF) model simulations and the Cloud Resolving Model Radar Simulator (CR-SIM), alongside scripts designed to reproduce the figures presented in the paper titled "Unraveling Secondary Ice Production in Winter Orographic Clouds through a Synergy of In-Situ Observations, Remote Sensing, and Modeling." The in-situ and remote sensing measurements were conducted at Mount Helmos in Peloponnese as part of the CALISHTO campaign (https://calishto.panacea-ri.gr/).</div> </div> </div> <div>Preprint accessible at: https://doi.org/10.21203/rs.3.rs-3502790/v1</div>
Mutant IDH1 inhibition induces dsDNA sensing to activate tumor immunity
<p>Isocitrate Dehydrogenase 1 (IDH1) is the most commonly mutated metabolic gene across human cancers. Mutant IDH1 (mIDH1) generates the oncometabolite (R)-2-hydroxyglutarate, disrupting enzymes involved in epigenetics and other processes. A hallmark of IDH1-mutant solid tumors is T cell exclusion, whereas mIDH1 inhibition in preclinical models restores anti-tumor immunity. Here, we define a cell-autonomous mechanism of mIDH1-driven immune evasion. IDH1-mutant solid tumors show striking, selective hypermethylation and silencing of the cytoplasmic dsDNA sensor, CGAS, compromising innate immune signaling. mIDH1 inhibition restores DNA demethylation, derepressing CGAS and transposable element (TE) subclasses. dsDNA produced by TE-reverse transcriptase activates cGAS, triggering viral mimicry and stimulating anti-tumor immunity. Thus, we demonstrate that mIDH1 epigenetically suppresses innate immunity and link endogenous reverse transcriptase activity to the mechanism of action of an FDA-approved oncology drug.</p>
SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense
<h3>Data for the SemEval 2024 paper "SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense"</h3> <p> </p> <p>The data of the two subtasks is saved in the <strong>data</strong> folder, <em>BTDATA.zip</em>, which contains the data for the sentence puzzle and word puzzle.</p> <p>The data contained in <em>BTDATA.zip</em> are as follows:</p> <ul> <li>Semeval Competition <ul> <li>Training Data <ul> <li><code>SP_train.npy</code> (Semeval training data)</li> <li><code>WP_train.npy</code> (Semeval training data)</li> </ul> </li> <li>Test Data <ul> <li><code>SP_test.npy</code> (Semeval test data)</li> <li><code>WP_test.npy</code> (Semeval test data)</li> <li><code>SP_test_answer.npy</code> (Semeval test data answer)</li> <li><code>WP_test_answer.npy</code> (Semeval test data answer)</li> </ul> </li> </ul> </li> </ul> <div> <h3><strong>Relation to EMNLP 2023 Paper</strong></h3> <a href="https://github.com/1171-jpg/BrainTeaser#2-relation-to-semeval2024-task9"></a></div> <p>The relationship between EMNLP and SemEval involves using the same dataset but with different data splitting and utilization methods. In EMNLP, the entire dataset is employed for testing, while in SemEval, the dataset is divided into training and testing sets, with the training set comprising a significant majority.</p> <p>Our EMNLP paper results on GitHub are tested on the entire data in a <strong>zero-shot manner</strong>. In the SemEval2024-Task9, although the whole dataset is the same as our EMNLP paper, we allow people to <strong>train on 80% of the whole dataset</strong>, and we <strong>evaluate the system on the 20% left</strong>.</p> <ul> <li>EMNLP Zero-Shot Experiment <ul> <li><code>sentence_puzzle.npy</code> (on all sentence puzzle data)</li> <li><code>word_puzzle.npy</code> (on all word puzzle data)</li> </ul> </li> </ul> <p><strong>Note:</strong> To prevent automatic data crawlers, <em>BTDATA.zip</em> needs a password: <strong>brainteaser</strong></p>
Data from: Preparation of Pt and bamboo charcoal co-modified TiO2 for formaldehyde sensing at room temperature
<p>Anatase TiO<sub>2</sub> has evolved to be one of the most attractive materials for gas sensing due to its strong oxidation activity and excellent sensing properties. In this study, we prepared a Pt and bamboo charcoal co-modified nano-TiO<sub>2</sub> using one-pot hydrothermal process and applied it for detection of formaldehyde. The successful incorporation of precious metal Pt and bamboo charcoal onto TiO<sub>2</sub> was confirmed by SEM, TEM, EDS, XRD, and XPS. These modifiers significantly improved the response of TiO<sub>2</sub> to formaldehyde, e.g., the response signal increased by 4 times, while the response time decreased from 91.05 to 67.76 s. The sample with 0.5@Pt and 0.5@C bamboo charcoal performed the best. Our work showed the potential of using biomass-derived carbon to improve the detection of formaldehyde.</p>
Sensing Reflector Height Variation on Seconds-Scale Using Low Earth Orbit Interferometric Reflectometry (LEO-IR)
<p><span>The simulated data (SNR, elevation and azimuth angles etc.) used in this study.</span></p> <p><span>File name format: arc number for GNSS (or LEO) <span>constellation</span> '_' PRN '_' 'seg'+arc number for a GNSS (or LEO) satellite</span></p> <p><span>File content format: 1[year] 2[month] 3[day] 4[hour] 5[minute] 6[seconds] 7[elevation angle] 8[azimuth angle] 9[chang rate of elevation angle] 10[SNR] 11[rh_ref] 12[rhdot_ref] 13[t_seconds]</span></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.