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1,961 results for “Sensing”
Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Orthomosaic_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Orthomosaic of the aerial images_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Case stuides of ground-based remote sensing observations from Cabauw, NL, as obtained during the ACCEPT campaign in 2014.
<p>Datasets of Mira-35 NMRA and Mira-35 MBR4 cloud radars, PollyXT multiwavelength polarization lidar, and the corresponding Cloudnet categorization data for the case-study periods of 3 November and 7 November 2014. A detailed description of each dataset is provided below.</p> <p>1. 20141103_cesar_categorize.nc</p> <ul> <li>Categorization file produced with Cloudnet algorithm, as document on http://cloudnet.fmi.fi.</li> <li>The data were collected on November 3, 2014. They are based on Mira-35 NMRA radar, microwave radiometer, lidar ceilometer, and optical disdrometer. </li> </ul> <p>2. 20141107_cesar_categorize.nc</p> <ul> <li>Categorization file produced with Cloudnet algorithm, as document on http://cloudnet.fmi.fi.</li> <li>The data were collected on November 7, 2014. They are based on Mira-35 NMRA radar, microwave radiometer, lidar ceilometer, and optical disdrometer. </li> </ul> <p>3. 20141103_2000.pdm</p> <ul> <li>This dataset was collected on November 3, 2014, between 20:00 and 20:15 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> <p>4. 20141107_0915.pdm</p> <ul> <li>This dataset was collected on November 7, 2014, between 09:15 and 09:30 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> <p>5. 20141107_0945.pdm</p> <ul> <li>This dataset was collected on November 7, 2014, between 09:45 and 10:00 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> <p>6. 20141107_0800.mmclx</p> <ul> <li>This dataset was collected on November 7, 2014, between 08:00 and 10:00 UTC, using the Mira-35 MBR4 radar. During this period, the radar performed eight PPI scans, with the data from each scan used to calculate wind speed and direction in 15-minute intervals. In this study, the data from the sixth and eighth scans were utilized to calculate wind speed and direction for the time intervals 09:15–09:30 UTC and 09:45–10:00 UTC, respectively.</li> </ul> <p>7. 20141103_2000.mmclx</p> <ul> <li>This dataset was collected on November 3, 2014, between 20:00 and 22:00 UTC, using the Mira-35 MBR4 radar. During this period, the radar performed eight PPI scans, with the data from each scan used to calculate wind speed and direction in 15-minute intervals. In this study, the data from the first scan were utilized to calculate wind speed and direction for the time intervals 20:00–2015 UTC.</li> </ul> <p>8. 20141103_cesar_pollyxt.nc</p> <ul> <li>This dataset was collected on November 03, 2014, between 08:00 and 10:00 UTC. Amongst others, the file provides time-height cross sections of 1064-nm attenuated backscatter coefficient and 532-nm volume depolarization ratio, which can be used to evaluate an observed cloud and aerosol scene for the occurrence of ice crystals and liquid water.</li> </ul> <p>9. 20141107_cesar_pollyxt.nc</p> <ul> <li>This dataset was collected on November 07, 2014, between 20:00 and 21:00 UTC. Amongst others, the file provides time-height cross sections of 1064-nm attenuated backscatter coefficient and 532-nm volume depolarization ratio, which can be used to evaluate an observed cloud and aerosol scene for the occurrence of ice crystals and liquid water.</li> </ul> <p> </p> <p> </p>
Datasets associated with paper "Five decades of Abramov glacier dynamics reconstructed with multi-sensor optical remote sensing", by Enrico Mattea et al.
<p>This archive contains Digital Elevation Models and orthoimages produced within the study "Five decades of Abramov glacier dynamics reconstructed with multi-sensor optical remote sensing", published on The Cryosphere by Enrico Mattea et al.</p> <p>Assets include derivative products of SPOT satellite scenes, acquired by CNES’s Spot World Heritage Programme, and of Pléiades <span><span><strong>©</strong></span></span>CNES 2015, 2020, 2022, Distribution AIRBUS DS.</p>
Remote-sensing detectability of airborne Arctic dust - code
<p>The Matlab codes employed in the generation of the figures of the paper "Remote-sensing detectability of airborne Arctic dust", submitted to Atmospheric Chemistry and Physics (https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1057/).</p>
Performance Evaluation of Seven Remote Sensing Datasets for TRB Cropland Area Estimation (Accuracy Metrics and Temporal Trends)
<p>This dataset contains accuracy assessments and trend analyses for cropland area estimation using seven remote sensing datasets in the TRB region. The data is organized into the following structure:<br>1. Accuracy Evaluation ("RMSE+Rt+MPE" directory)</p> <p>Contains individual evaluation files for each of the seven remote sensing products<br>File naming convention: [DatasetName]_Evaluation.csv<br>Each file contains four columns:</p> <p>County: Administrative region<br>Rt: Temporal correlation<br>RMSE: Root Mean Square Error<br>MPE: Mean Percentage Error</p> <p>2. Area Change Trends ("Trend" directory)</p> <p>Contains trend analysis files for seven remote sensing products and reference observations (OBS)<br>File naming convention: [DatasetName/OBS]_trend.csv<br>Each file contains three columns:</p> <p>County: Administrative region<br>Slope: Trend slope coefficient<br>P_value: Statistical significance value</p> <p>3. Spatial Correlation (Rs.csv)</p> <p>Single file containing annual spatial correlation coefficients (Rs) for all seven remote sensing datasets.</p>
Remote sensing measurements of the nighttime D-region ionosphere based on very low frequency tweek observations in China
<p>Figures, data, and code used in my paper describing "Remote sensing measurements of the nighttime D-region ionosphere based on very low frequency tweek observations in China"</p>
GPS data and plotting codes for Remote Sensing paper titled Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity
<p>Data files (meteorological data, sattelite derived velocity time series, and seismic station GPS data) and plotting codes to reproduce the dataset and plots presented in the paper Gajek et al., Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity</p>
SemEval-2013 Task 13: Word Sense Induction for Graded and Non-Graded Senses
<p>Data originally provided by the SemEval-2013 Task 13 organizers and which was available through the following link:</p> <p>https://www.cs.york.ac.uk/semeval-2013/task13/data/uploads/semeval-2013-task-13-test-data.zip</p>
SemEval-2010 Task 14: Word Sense Induction & Disambiguation
<p>Data originally provided by the SemEval-2010 Task 14 organizers and which was available through the following links:</p> <p>1. https://www.cs.york.ac.uk/semeval2010_WSI/files/evaluation.zip<br> 2. https://www.cs.york.ac.uk/semeval2010_WSI/files/training_data.tar.gz<br> 3. https://www.cs.york.ac.uk/semeval2010_WSI/files/test_data.tar.gz</p>
Spatio-temporal analysis of remotely sensed forest loss data in the Cordillera Administrative Region, Philippines
<p>The Cordillera Administrative Region (CAR) in the Philippines is among the last forest frontiers in the country and is also home to 13 major watersheds in Northern Luzon that supply irrigation and hydroelectricity to other regions. However, it is faced with the deterioration of the quality of its watersheds due to forest loss driven mainly by agricultural expansion and illegal logging. Thus, this study was conducted to analyze the spatial and temporal patterns of forest loss that could serve as a basis for policy decisions. Also, this paper determined the strength of relationships using Pearson's correlation coefficient (<i>r</i>) between forest loss and seven independent variables, which includes forest cover, agricultural areas, built-up, road network, and socio-economic data. This study utilized the Hansen Global Forest Change (HGFC), a Landsat-derived dataset from 2001 to 2019. Results revealed that 70,925 hectares (ha) of forest loss were detected with an annual deforestation rate of 3,744 ha/year across the region. Based on the validation, the accuracy of the HGFC data is 72%, but great caution should be observed when using the data with less than 0.2 ha due to very low accuracy. On a region-wide analysis, only the forest cover had a strong association with the forest loss with a computed <i>r-value</i> of 0.78. Conversely, on a provincial level, the explanatory variables had a strong to moderately strong correlation with deforestation. Hence, immediate, science-based, and sustained regional and multi-stakeholder efforts are necessary to conserve and protect the remaining forest cover in the region.</p>
Single-scattering properties of ice particles in the microwave regime: Temperature effect on the ice refractive index with implications in remote sensing
<p>This database is from the paper: Ding, J., L. Bi, P. Yang, G. W. Kattawar, F. Weng, Q. Liu, and T. Greenwald, 2017: Single-scattering properties of ice particles in the microwave regime: temperature effect on the ice refractive index with implications in remote sensing, Journal of Quantitative Spectroscopy & Radiative Transfer, 190, 26-37. DOI: 10.1016/j.jqsrt.2016.11.026.</p>
A consistency evaluation of marine aquaculture with marine spatial planning by remote sensing and GIS
<p>This study used multi-phase satellite remote sensing images of Shandong Province and an automatic extraction algorithm for aquaculture to obtain aquaculture spatial distribution data. GIS spatial overlay analysis technology was used to superimpose marine functional zoning (2010–2020) data for comparative analysis to evaluate implementation effectiveness and existing problems in marine functional zoning. </p>
Data-Linking remote sensing data to the estimation of pollination services in agroecosystems
<p>Wild bees are key providers of pollination services in agroecosystems. The abundance of these pollinators, and the service they provide, relies on the availability of supporting resources in the landscape. Because of this, spatially explicit models have been developed to quantify wild bee abundance and pollination services in food crops, while accounting for the influence of locally available foraging and nesting resources. However, model implementation is limited by the availability of land cover maps and experts on pollinators capable of establishing the quality of local habitats for pollinators. In this study, we present how remote sensing data can be linked to the estimation of wild bee abundance, and act as an alternative to the conventional use of land cover maps and local expertise in spatially explicit models estimating pollination services. For this, we used landscape characteristics derived from remote sensors to qualify nesting resources in the landscape and thereafter estimate the delivery of pollination services by mining bees (<i>Andrena</i> spp.) in 30 fruit orchards located in the Flemish region of Belgium. Mining bees were selected for this study for their major role as local pollinators and underground nesting habits. Estimated pollination services were compared with those derived from conventional qualifications of nesting resources and showed no significant differences (P=0.68) in the amount of explained variation in activity of mining bees on the studied orchards. Estimates derived from remote sensing data and conventional inputs explaining 69% and 72% of the total variation, respectively. These results confirmed that remote sensing data can deliver nesting suitability characterizations suitable for the estimation of pollination services, This research also illustrates the relevance of nesting resources and highlights the importance of considering soil resources in the estimation of pollination services provided by pollinators like mining bees. Our results support the development of holistic agro-environmental policies that rely on the use of modern tools like remote sensors and promote pollinators by considering nesting resources.</p>
The sense of body ownership shapes the visual representation of body size
<p>Data collected and analyzed in the manuscript: Giurgola S, Crico C, Farnè A, Bolognini N. The sense of body ownership shapes the visual representation of body size. J Exp Psychol Gen. 2021 Oct 25. doi: 10.1037/xge0001111; PMID: 34694859.</p>
Classification of a synthetic strain rate dataset acquired with Distributed Acoustic Sensing (DAS).
<p>The videos show the real-time acquisition of a strain rate dataset acquired with a Distributed Acoustic Sensing (DAS). The observations are collected per bloc of 4s and are segmented in six sources: noise, pedestrian, impact, backhoe, compactor, leaks. The first video shows the source identification and segmentation using a Random Forest classifier; the second video combines a Random Markov Field to the Random Forest classifier.</p>
Active sensing in bees through antennal movements is independent of odor molecule (Exemple videos)
<p>Exemple videos demanded by the reviewers to illustrate particular behaviors of the insects or details of the experimental setup. Please see the relevant section in the response document.</p>
Active sensing in bees through antennal movements is independent of odor molecule (Article videos)
<p>Exemple videos referenced in the paper to illustrate particular behaviors of the insects. Please see the relevant section in the supplementary material.</p>
ScienceDex guides
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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.