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1,961 results for “Sensing”
Design of sensing device for estimation of disease in Bovine, Avian and Caprine
<p>Five biological samples of blood serum of bovine(cow), avian(poultry) and caprine(goat) mixed with ZnO are tested by clinical test and designed sensing device and are compared. Statistical analysis is further done.i.e.correlation test and t-test using IBMSPSS v 20 which helped in making the conclusion that developed low cost nanostructured ZnO based sensing device is efficient.</p> <p>In table 1 :The datasets of four biological parameters are measured corresponding to their resistive values using arduino uno and data are maintained in microsoft access10</p> <p>In table 2:Datasets obtained by clinical test in laboratory and by designed sensing devics are compared</p> <p>In table 3 and 4: Statistical analysis is done to find standard deviation and standard error mean and also correlation test and t test tells us how both datasets are correlated.</p> <p> </p>
FIGURE 109 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae)
FIGURE 109. Habitat of Cheilonycha a. auripennis (Lucas), Paraguay, Guaira. ditto, with one imago on the mound surface.
FIGURE 110 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae)
FIGURE 110. Habitat of Cheilonycha a. auripennis (Lucas), Paraguay, Guaira. the same but more heavily burned area with termite mounds.
FIGURES 93–103 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae)
FIGURES 93–103. Cheilonycha bucephalauripennis sp. nov., all from Brazil, Jataí (Jatahy on labels). 93–95––head: 93––♂, HT (MNHN); 94––♂, PT (MNHN); 95––♂, PT (SDEI); 96––ditto, mandibles; 97–99––head in dorsal view: 97––♂, PT (SDEI); 98––♂, HT (MNHN); 99––♂, PT (MNHN); 100–103––labrum: 100––♂, HT; 101––♂, PT (MNHN); 102––♂, PT (SDEI); 103––♀, AT (MNHN). Bars = 1 mm.
FIGURES 43–51. Cheilonycha a in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae)
FIGURES 43–51. Cheilonycha a. auripennis (Lucas). 43–46––pronotum: 43––♀, "Brésil intérieur", LT (MNHN); 44––♂, Brazil, Cuyaba (SDEI); 45––♂, Mbaracayu (CCJM); 46––♀, ibid. (CCJM); 47–51––male aedeagus: 47––Paraguay, LT of syn. C. a. angustedilatata W. Horn (SDEI); 48–49––Paraguay, Mbaracayu (CCJM); 50–51––ditto, internal sac in left and right lateral view. Bars = 1 mm.
FIGURES 71–84. Cheilonycha a in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae)
FIGURES 71–84. Cheilonycha a. chiquitosiana ssp. nov., all from Bolivia, San Chose de Chiquitos. 71–72––head: 71–– ♂, HT (UASC); 72––♀, PT (CCJM); 73–78––labrum: 73––♂, HT; 74––♂, PT (DBCN); 75––♂, PT (CCJM ex COSJ); 76––♀, AT; 77–78––♀, PT (CCJM and DBCN); 79–80—pronotum: 79––♂, HT; 80––♀, AT (DBCN); 81–84––elytron: 81––♂, HT; 82––♀, PT (CCJM); 83––♀, PT (DBCN); 84––♂, PT (DBCN). Bars = 1 mm.
FIGURES 7–18 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae)
FIGURES 7–18. Cheilonycha chalybea (Dejean). 7––head, ♂ ex Chaudoir (MNHN); 8–11––labrum: 8––♂ ex Chaudoir (MNHN); 9––♂, Brazil, Ypanema (NHMW); 10––♀, "partie méridionale du Brésil", LT (MNHN); 11–– ♀, Brésil" (MNHN); 12–15––pronotum: 12––♀, LT; 13––♀, Brésil" (MNHN); 14––♂, Ypanema (NHMW); 15––♂ ex Chaudoir (MNHN); 16–18–elytron: 16––♀, LT; 17––♀, ex Chaudoir (MNHN); 18––♂, Ypanema (NHMW). Bars = 1 mm.
FIGURES 62–70. Cheilonycha a in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae)
FIGURES 62–70. Cheilonycha a. chiquitosiana ssp. nov., all from Bolivia, San Chose de Chiquitos. 62–63––habitus: 62–– ♂, 11.4 mm, HT (UASC); 63––♂, 11.5 mm, PT (CCJM ex COSJ); 64––head in dorsal view, ♂, PT (DBCN); 65–70––aedeagus or its apex: 65––HT; 66––PT (DBCN); 67––PT (CCJM) 68—ditto, dry; 69–70–– ditto, internal sac in left and right lateral views. Bars = 1 mm.
Figure 2 in Remote sensing and citizen science to characterize the ecological niche of an endemic and endangered Costa Rican poison frog
Figure 2. Land cover classification of the study area in the South Pacific of Costa Rica for 2019 using Sentinel-2 spatial data.
Figure 1 in Remote sensing and citizen science to characterize the ecological niche of an endemic and endangered Costa Rican poison frog
Figure 1. Map of the study area in the South Pacific of Costa Rica (1:1,250,000 scale and Coordinate Reference System (CRS) WGS84) for the analysis of the ecological niche of P. vittatus using data generated during 2020. Main towns are shown. Large protected areas are represented by their management category: 1: Corcovado National Park; 2: Piedras Blancas National Park; 3: Golfo Dulce Forest Reserve; 4: Paso de la Danta Biological Corridor. Sources: Costa Rica Atlas 2014, Costa Rica Conservation Areas National System (SINAC) 2016 and 2020. Photograph of Phyllobates vittatus by Marina Garrido-Priego.
Figure 3 in Remote sensing and citizen science to characterize the ecological niche of an endemic and endangered Costa Rican poison frog
Figure 3. Relative niche suitability for Phyllobates vittatus across its distribution in the South Pacific of Costa Rica. The suitability map was generated during 2020 through the combination of eleven different environmental predictors, with elevation, forest percentage, distance to lakes and distance to ASADAS explaining the greatest proportion of the variance. The legend shows niche suitability ranging from low suitability (0; white) to high suitability (1; dark green). We represent in white high-altitude areas (>1500 m) and large plantations identified during the classification of the land cover, which were not included in the model. This prediction was made with a model built using a 5 km buffer area around the known occurrence points. Large protected areas are represented by their management category: 1: Corcovado National Park; 2: Piedras Blancas National Park; 3: Golfo Dulce Forest Reserve; 4: Paso de la Danta Biological Corridor. Sources: Costa Rica Atlas 2014, Costa Rica Conservation Areas National System (SINAC) 2016 and 2020. This map is at a 1:1,150,000 scale and CRS WGS84.
Water, energy and carbon fluxes and ancillary meteorological and remote sensing measurements at Encanto Golf Course, Phoenix, Arizona during 2019-2020
<p>Water, energy and carbon fluxes as wells as ancillary meteorological and NDVI measurements from the Encanto Park Golf Course in Phoenix, AZ. Measurements were done from March 2019 to March 2020.</p> <p>The database includes three files:</p> <ul> <li>Encanto_AZMET.xlsx, that include hourly values and daily average meteorological variables (T<sub>air</sub>, RH, VPD, R<sub>s</sub>, T<sub>soil</sub>, etc.) obtained from the Phoenix Encanto meteorological station of The Arizona Meteorological Network (AZMET). Please refer to https://cals.arizona.edu/AZMET/15.htm and the spreadsheet ReadMe within the excel file for additional information.</li> <li>Encanto_EddyCovariance.xlsx includes the half-hour water, energy and carbon fluxes obtained using an eddy covariance (EC) system. The file also includes half-hour ancillary meteorological and soil measurements obtained from the EC tower. Water, energy and carbon fluxes were processed using the software EddyPro 7.0. For more information, please refer to Vivoni <em>et al</em> (2020). Note: This new version includes Eddy Covariance and Met data up to July, 2020.</li> <li>Encanto_NDVI.xlsx includes the average NDVI for the net radiometer footprint installed in the EC tower. NDVI was obtained from 4-band PlanetScope scene images obtained from Planet website (www.planet.com). For more information, please refer to Vivoni <em>et al</em> (2020).</li> </ul> <p>The use of the datasets requires the citation of the next paper:</p> <ul> <li>Vivoni, E. R., Kindler, M., Wang, Z., and Perez-Ruiz, E. R. 2020.Abiotic Mechanisms Drive Enhanced Evaporative Losses under Urban Oasis Conditions . <em>Geophysical Research Letters</em>. 47: e2020GL090123. <a href="https://doi.org/10.1029/2020GL090123">https://doi.org/10.1029/2020GL090123</a></li> </ul>
Supplementary materials for Quantifying historical and future surface soil moisture drying using deep learning and remote sensing
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Position sense in altered gravity
<p>These data originate from a parabolic flight experiment in which the influence of altered gravity (hypergravity, microgravity) on the human sense of position was investigated using three classical methods (two-arm matching, two-arm pointing, one-arm repositioning).</p>
Estimation of landslide volume by machine learning and remote sensing techniques in Himalayan Regions
<p>This dataset was prepared for the development of a machine learning model so as to use this model to estimate the potential landslide volume in the Himalayan Regions, especially the Gyirong. This area serves as the only land route between China and Nepal, suffering from severe landslides each year and causing losses of human lives and properties. Therefore, developing an effective model for reliable estimation of landslide volume is of much significance for local people and decision-makers.</p>
GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
<p>GDCLD : A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images</p> <p>Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., and Catani, F.: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-239, in review, 2024.</p> <p> </p> <p>Data description:</p> <p> </p> <p>The training dataset and the validation dataset are composed of UAV, PlanetScope, Gaofen-6 and Map World images of the 5 earthquake regions of Luding, Nippes, Hokkaido, Jiuzhaigou and Mainling. There is no overlapping area in each TIFF. The training dataset and the validation dataset are randomly divided at a ratio of approximately 0.75:0.25.</p> <p> </p> <p>train_dataset:</p> <p>train_data: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>train_label: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Validation_dataset</p> <p>val_data: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>val_label: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Test_dataset (Lushan, Sumatra, Mesetas and Palu dataset)</p> <p>This package contains the original files of remote sensing images from three sources: UAV, Map World, and PlaneScope belonging to the Lushan, Sumatra, Mesetas and Palu earthquake regiones, which are used to display the test area.<br><br>Future work:<br>The future work includes additional landslide data that the authors will continue to upload. In this 2.0 version update, we have added UAV imagery and interpreted data for loess landslides triggered by the December 2023 M6.2 earthquake in Gansu, China, with a resolution of 0.1 m. Due to authorization constraints, we can only provide PNG files without geographic coordinates. Additionally, this update includes PlanetScope imagery of landslides induced by heavy rainfall in Guangdong, China, in 2024, as well as PlanetScope imagery and landslide labels for events triggered by the Hualien earthquake in Taiwan.<br><br>Please note that this landslide dataset is publicly available exclusively for scientific research purposes and must not be used for commercial purposes.</p>
Characterization of N variations in different organs of winter wheat and mapping NUE using UAV-based remote sensing
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Lonati (2024) - Remote sensing to measure the physiology and foraging ecology of North Atlantic right whales in the Gulf of St. Lawrence, Canada
<h1>Supplementary Material A1.S4 Videos</h1> <h2>Selection of pixels and frames for evaluating intranasal heat</h2> <p>Video A1.S4.1. Time-aligned visible-spectrum (RGB) and infrared thermography (IRT) videos with plot of maximum corrected sensor intensity over time for a normal respiratory cycle from North Atlantic right whale (NARW) Catalog ID #4129 (aligns with Figure A1.S4.1).</p> <p><br>Video A1.S4.2. Time-aligned RGB and IRT videos with plot of maximum corrected sensor intensity over time for a normal respiratory cycle from NARW Catalog ID #3845 (aligns with Figure A1.S4.2).</p> <p><br>Video A1.S4.3. Time-aligned RGB and IRT videos with plot of maximum corrected sensor intensity over time for an anomalous respiratory cycle from NARW Catalog ID #4129, where the exhaled respiratory vapor lingers over the blowholes, obscuring and reducing intranasal heat received by the IRT sensor.</p> <p><br>Video A1.S4.4. Time-aligned RGB and IRT videos with plot of maximum corrected sensor intensity over time for an anomalous respiratory cycle from NARW Catalog ID #3845, where exhaled respiratory vapor and a small wave obscure and reduce intranasal heat received by the IRT sensor.</p> <p><br>Video A1.S4.5. Time-aligned RGB and IRT videos with plot of maximum corrected sensor intensity over time for an anomalous respiratory cycle from the 2021 calf of NARW Catalog ID #4040, where the exhaled respiratory vapor lingers over the blowholes and a non-uniformity correction occurs mid-way through the respiration.</p> <h3><em>G. Lonati - PhD Thesis - University of New Brunswick Saint John</em></h3>
Dataset 1 for Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing
<p>Datasets for the paper "Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing"</p> <p>Yue Zhanga,1, Alina Sigaevaa,1 , Arturo Elías-Llumbeta, Siyu Fana , Willem Woudstraa, Rinse de Boerc, Elkin Escobara,<br>Claudia Reyes-San-Martina, Robin Kisabacaka , Dorenda Oosterhuisa, Alan R. Gortera, Britt Coenena,c , Felipe P. Perona Martineza,<br>Geert van den Bogaartc, Peter Olingaa, and Romana Schirhagla</p>
Dataset 2 for Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing
<p>Datasets 2 for the paper "Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing"</p> <p>Yue Zhanga,1, Alina Sigaevaa,1 , Arturo Elías-Llumbeta, Siyu Fana , Willem Woudstraa, Rinse de Boerc, Elkin Escobara,<br>Claudia Reyes-San-Martina, Robin Kisabacaka , Dorenda Oosterhuisa, Alan R. Gortera, Britt Coenena,c , Felipe P. Perona Martineza,<br>Geert van den Bogaartc, Peter Olingaa, and Romana Schirhagla</p> <p> </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.