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9,302 results for “DETECT”
BIM-Speed training dataset for HVAC detection using Deep Learning
<p>Training dataset formed by two types of elements (for now) to be detected by the model using Deep Learning: Boiler and Radiator. Of the boiler type 107 images have been collected and of the radiator type 123 images. Although there are few images to get good training, it can be used as a first approximation and to evaluate the precision obtained with few training images. These images have been distributed in 80% of the images for the training phase and 20% for the test phase. All the images have the bounding box data for each element in the xml file with the same name. Additionally, 33 other images (not previously used) have been used for the validation of the tool.</p> <p>More information can be found here (subsection 3.1 Detection and classification of HVAC components in images): https://www.bim-speed.eu/en/Project%20Results%20%20Documents/Deliverables/D1.2.pdf</p>
Dataset for Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories
<p>Dataset utilized for our tested data in our accepted paper "Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories"</p>
ERT geophysical surveys for detecting gravitational morpho-structures in the Becca France area (Aosta Valley, Italy)
<p>This dataset contains the raw and processed data of an Electrical Resistivity Tomography (ERT) geophysical survey conducted on November 2023 in the Becca France area (Aosta Valley, Italy), in order to help the geological reconstruction of gravitational morpho-structures.</p> <p>It represents the supplementary materials of a publication submitted on Geohazards journal: Forno et al., 2024. Deep electrical resistivity tomography for detecting gravitational morpho-structures in the Becca France area (Aosta Valley, Italy).</p> <p> </p> <p>For details, please refer to the above mentioned publication and to the readme file.</p> <p> </p> <p>7z software (free and open source) can be used to unzip the dataset.</p> <p> </p>
The data for Newly Detected Old Galactic Globular Cluster Candidates Using Gaia DR3
<pre><br><br></pre> <p>GAIA_input - Raw data downloaded by GAIA</p> <p>GAOSI - Gaussian interpolation and testing AND concer fits correlational graph values</p> <p>The result after clustering_output</p> <p>mh_gspphot - [Fe/H] ag_gspphot - AG m_M - distance modulus - This is also the header of GC01.csv and GC02.csv under .data\gaosi\GAOS, which are copied from the csv file in .data\gaosi\GAOSI\output2</p> <p>Because GAIA lacked part of the data of [Fe/H] and AG after clustering, we adopted the Gaussian RV completion method of Qin et al. (2023) doi: 10.3847/1538-4365/acadd6 to supplement the data of [Fe/H] and AG. The data in these 3 folders is the result of the process we performed. .data\gaosi\GAOSI\output and .data\gaosi\GAOSI\output1 and .data\gaosi\GAOSI\output2 </p> <p>.data\gaosi\GAOSI\output3 - Test the data after we finish the Gaussian interpolation</p> <p>.data\gaosi\GAOSI\medain.csv -The median of the data before Gaussian interpolation</p> <p>.data\gaosi\GAOSI\pyngc01_1sigma.csv and .data\gaosi\GAOSI\pyngc02_1sigma.csv -The mean, standard deviation, and error range of m_M and mh_gspphot and ag_gspphot</p> <p> </p>
Figure 2 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 2. The Rapid Environmental (e)DNA Assessment and Deployment Initiative & Network (READI-Net) project components being developed to support molecular detection of invasive species. Molecular tools, like eDNA sampling, are sensitive and costeffective for early detection of invasive species and are one component of the National Early Detection Rapid Response framework.
Figure 1 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 1. The National Early Detection Rapid Response framework developed by the U.S. Department of the Interior Invasive Species Task Force. Open circles represent the components to be enacted for effective early detection and rapid response of invasive species. The associated commentary reflects the primary questions, observations, and directives that the process from one component to the next. At the core of the process, are the informational inputs necessary for management decision-making.
Figure 2 in Eradication of African sacred ibis (Threskiornis aethiopicus) from South Florida, USA: a collaborative early detection and rapid response case study
Figure 2. Two African Sacred Ibis (Threskiornis aethiopicus) affixed with wing tags and GPS backpack transmitters in September of 2008 in order to employ the "scout" technique as part of a successful southern Florida, USA, eradication effort. Photo courtesy of USDA.
Figure 1 in Eradication of African sacred ibis (Threskiornis aethiopicus) from South Florida, USA: a collaborative early detection and rapid response case study
Figure 1. Map of Florida (left) and southeastern Florida (right) where a successful eradication of African Sacred Ibis (Threskiornis aethiopicus) took place in 2008. Early detection at the Loxahatchee National Wildlife Refuge by a University researcher prompted a concerted effort to capture birds and employ the scout technique on two transmitter affixed birds. The scout method and reported species sightings resulted in identifying additional roost locations (4) where shooting operations took place.
UAV Railroad Images for Segmentation and Obstacle Detection
<p><span>UAV-RSOD dataset contains five different types of obstacles including </span><span>person, boulder, barrel, branch, jerry can, and iron rod. This dataset supports the development and evaluation of computer vision models for railroad-related applications, such as autonomous train navigation, obstacle detection in railroad, railroad extraction and safety monitoring. </span><span> </span><span>The DJI Phantom 4 PRO UAV </span><span>Drone was used to take all the videos and images in the Indian Railways, Tiruchirapalli city of Tamilnadu, India.</span></p> <p><span>Version 1: 315 Images with Labelling, Masking for Segmentation Process and without any Data Augmentation techniques applied</span></p> <p><span>Version 2: 2002 Annotated Images with Data Augmentation techniques applied (Rotate, Flip, etc) for Obstacle Detection</span></p>
Figs. 1–12 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 1–12. Diplomaragna terricolor (Attems, 1899). Variation in structure of the posterior angiocoxal process of posterior gonopod. 1 — from Anuchinsky District, Primorsky Krai; 2 — from Mt. Ko, Khabarovsky Krai; 3 — from Vladivostok, Okeanskaya Station, Primorsky Krai; (1st copy); 4 — from Vladivostok, Okeanskaya Station, Primorsky Krai; (2st copy); 5 — from Kedrovaya Pad Nature Reserve, Primorsky Krai; 6 — from Popova Island, Primorsky Krai; (1st copy); 7 — from Ussuriysky Nature Reserve, Primorsky Krai; 8 — from Lazovsky Nature Reserve, Primorsky Krai; 9 — from Sikhote-Alin Nature Biosphere Reserve (floodplain terrace, Yasnaya River), Primorsky Krai; 10 — from Chuguevsky District, Verkhneussuriysky Research Station, Primorsky Krai; 11 — from Popova Island, Primorsky Krai; (2st copy); 12 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scale in mm (0.5)
Figs. 13–16 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 13–16. Diplomaragna terricolor (Attems, 1899). Posterior angiocoxal processes of posterior gonopods. 13 — from Mt. Ko, Khabarovsky Krai; 14 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 15 — from Lazovsky Nature Reserve, Primorsky Krai; 16 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scales: 100 μm
Figs. 21–24 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 21–24. Diplomaragna terricolor (Attems, 1899). Coxae of male leg pair 11, front view. 21 — from Mt. Ko, Khabarovsky Krai; 22 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 23 — from Lazovsky Nature Reserve, Primorsky Krai; 24 — from SikhoteAlin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; p — coxal process. Scales: 100 μm Рис. 21–24. Diplomaragna terricolor (Attems, 1899). Коксы 11-й пары ног самца, виΑ спереΑи: 21 — с горы Ко в Хабаровском крае; 22 — из ВΛаΑивостока, станция Океанская; 23 — из Αазовского заповеΑника в Приморском крае; 24 — из СихотэАΛинского заповеΑника (урочище БΛагоΑатное, кΛюч Озёрный) в Приморском крае; р — коксаΛьный отросток. Масштабы: 100 μm
Figs. 17–20 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 17–20. Diplomaragna terricolor (Attems, 1899). Gonopods, caudal view. 17 — from Mt. Ko, Khabarovsky Krai; 18 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 19 — from Lazovsky Nature Reserve, Primorsky Krai; 20 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scales: 100 μm
Map. Species diversity of Diplopoda within the Sikhote-Alin State Nature Biosphere Reserve. The numbers of the natural landmarks (1–18) are explained in the "Material and Methods section" Карта. ВиΑовое разнообразие Αвупарноногих многоножек в Сихотэ-АΛинском заповеΑнике. Номера урочищ (1–18) поясняются в разΑеΛе «МатериаΛ и метоΑы» in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Map. Species diversity of Diplopoda within the Sikhote-Alin State Nature Biosphere Reserve. The numbers of the natural landmarks (1–18) are explained in the "Material and Methods section" Карта. ВиΑовое разнообразие Αвупарноногих многоножек в Сихотэ-АΛинском заповеΑнике. Номера урочищ (1–18) поясняются в разΑеΛе «МатериаΛ и метоΑы»
FIGURE 8 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 8. Difference maps showing the percentage of events accurately detected by simulations without bioturbation subtracted from the percentage of events accurately detected by simulations with bioturbation. 100% sampling completeness and transition durations 0.001 times the event duration for A; 100% sampling completeness and transition durations five times the event duration for B; 25% sampling completeness and transition durations 0.001 times the event duration for C; 25% sampling completeness and transition durations five times the event duration for D. The solid black line marks the contour line for zero difference between the bioturbated and non-bioturbated simulations.
FIGURE 6 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 6. Effect of varying completeness with the duration of transition intervals. Simulation results with 25% completeness and transition lengths 0.001 times the event duration for A; and 25% completeness with transition lengths of five times the event duration for B. Background DCA-1 value is -0.5 and no bioturbation occurs. Excursion magnitude (y-axis) indicates the difference between the simulated background DCA-1 value and the simulated event DCA- 1 value. Resolution potential (x-axis) is the event duration divided by the time represented by the sample interval. Color shading indicates the median excursion magnitude of all samples that intersect an event. Contour lines show the parameter space where 50% (dashed line), 75% (dotted line) and 95% (solid line) of events are accurately detected.
FIGURE 12 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 12. Effect of sample thickness on accurate detection of events with excursion magnitudes of 2.8 DCA-1 units. Y-axis shows the percentage of accurately detected events at different sedimentation rates (x-axis) for three different event durations: 50 years, 100 years and 1000 years. Simulations are for the 2016 data set to approximate how a researcher might use a pilot data set to design a sampling procedure and are based on sampling with 25% completeness. A is simulations without the effect of bioturbation; B is simulations with the effect of bioturbation.
FIGURE 2 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 2. Simulation workflow in the paleontological assemblage mixer (paleoAM). A. Relative abundances of a given species (Epistominella pacifica) from the 355 samples of the 2021 data set, along the empirically derived DCA Axis 1 gradient. B. The per-bin mean of absolute abundance of E. pacifica across all samples within each bin. Absolute abundances are calculated from the relative abundances in A by rescaling the relative abundances to 10000 total specimens. C. Scaled kernel density estimates for E. pacifica, which depict the predicted abundance distribution of E. pacifica along DCA Axis 1 after fitting a kernel density estimate to the absolute abundances in B. D. Scaled kernel density estimates, like in C, for all taxa in the dataset showing their differing predicted abundance distributions along DCA Axis 1 with the kernel density of E. pacifica shown in C marked with an asterisk. E. Visual representation of parameters varied within the simulation along a vertical sediment core. From left to right: standard scenario, increased excursion magnitude, increased background value, increased resolution potential, sampling completeness, bioturbation and increased transition duration. Stacked rectangles represent potential sample intervals. In the first and last column, black dots indicate which intervals are sampled, gray dots indicate unsampled intervals. In the standard scenario, all potential sample intervals are sampled. Curved arrows denote sediment mixing among potential sample intervals.
FIGURE 1 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 1. Sample scores from detrended correspondence analysis (DCA) performed on benthic foraminiferal assemblages in the>63 µm size fraction from Integrated Ocean Drilling Program Expedition 341 Site U1419 in the Gulf of Alaska used in the simulation case study. A. DCA Axis 1 values for 355 assemblages from Sharon et al. (2021); B. DCA Axis 1 values for 47 assemblages representing a "pilot" data set of samples available and processed in 2016.
FIGURE 4 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 4. Percentage of events that are accurately detected and median excursion magnitudes for simulations performed with background DCA-1 values of -0.5, 0.5 and 1. For A and B, simulations used a background DCA-1 value of -0.5; for C and D, a background DCA-1 value of 0.5; for E and F, a background DCA-1 value of 1. Excursion magnitude (y-axis) indicates the difference between the simulated background DCA-1 value and the simulated event DCA- 1 value. Resolution potential (x-axis) is the event duration divided by the time represented by the sample interval. A, C and E. Color shading indicates the percentage of events that are accurately detected by at least one sample (i.e., the sample produces a DCA-1 value outside the 95% envelope of samples simulated at the background value, and within one DCA-1 unit of the simulated excursion magnitude). B, D and F. Color shading indicates the median excursion magnitude of all samples that intersect an event. Contour lines show the parameter space where 50% (dashed line), 75% (dotted line) and 95% (solid line) of events are accurately detected.
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.