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9,302 results for “DETECT”
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Test data set</p>
Dataset for CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection [ICRA2023]
<p>Data of lumen detection, including artifacts and clean images.</p>
Fig. 1 in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)
Fig. 1. Light micrographs of Lugol's solution preserved individuals and line drawings of Prorocentrum dentatum (A–I), P. obtusidens (J– AC) and P. shikokuense (AB, AD–AH). (A–B) Prorocentrum dentatum by Stein (1883). (C–I) Individuals from the Pacific Ocean. See Table 1 for location. (J) Prorocentrum obtusidens re-drawn from Schiller (1928). (K–AB) Individuals from a single sample collected offshore Taranto, Ionian Sea. (Y–AB) Valves of the same individual. (AB) One cell of P. obtusidens and two cells of P. shikokuense. (AC) P. obtusidens collected offshore Bari, Adriatic Sea. (AD) Prorocentrum shikokuense redrawn from Hada (1975). (AB, AE–AH) Individuals from a single sample of the harbor of Brindisi, Italy. (AE–AG) Chain-forming individuals. (AH) Note the intraspecific morphological variability. The last individual corresponds to a megacytic cell. Scale bar = 10 µm.
Video data describing bioluminescent detection of isothermal DNA amplification in microfluidic generated droplets and artificial cells
<p>Video data of loop-mediated isothermal nucleic acid amplification (LAMP) and bioluminescent assay in real time (BART) in microfluidicaly produced droplets. The videos are the data used to generate still images for figures in the source publication "Bioluminescent detection of isothermal DNA amplification in microfluidic generated droplets and artificial cells".</p> <p>Details of the videos acquisition parameters are as follows:</p> <ul> <li>Video 1: LAMP-BART droplets in tubing - no DNA template control (Figure 2a) (assay duration 600s, frames 1 per 10s, 60 frames, final</li> <li>Video 2 frames per second) Video 2: LAMP-BART droplets in tubing - positive DNA template control (Figure 2b) (assay duration 1800s, frames 1 per 10s, 180 frames, final video 10 frames per second)</li> <li>Video 3: LAMP-BART parked droplets (Figure 3) (assay duration 600s, frames 1 per 10s, 60 frames, final video 3.3 frames per second)</li> <li>Video 4: LAMP-BART DNA gradient in tubing (Figure 4) (assay duration 1800s, frames 1 per 10s, 180 frames, final video 10 frames per second)</li> <li>Video 5: LAMP-BART eDIBs (Figure 5) (assay duration 1570s, frames 1 per 10s, 157 frames, final video 5.2 frames per second)</li> <li>Video 6: One to four core LAMP-BART eDIBs (Figure 6) (assay duration 1200s, frames 1 per 10s, 120 frames, final video 5 frames per second)</li> </ul> <p>When referencing please use:</p> <p>Hardinge, P., Baxani, D. K., McCloy, T., Murray, J. A. H. & Castell, O. K. Bioluminescent detection of isothermal DNA amplification in microfluidic generated droplets and artificial cells. <em>Sci Rep</em> <strong>10</strong>, 21886 (2020).</p>
Replication package for: Anomaly Detection Through Container Testing: A Survey of Company Practices
<p>The file contains the survey questionnaire used to collect data for our research.</p>
Data set for Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies
<p><strong>Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies</strong></p>
Fig. 8 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 8. Energy distribution, on average, per IMF for the SD, SSC, TTC and EDT criteria.
Fig. 12 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 12. Block diagram illustrating the parameter combinations of the experiment performed.
Fig. 4 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 4. Preprocessing, analysis, SVM training and test steps.
Fig. 2 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 2. Setup for the proposed approach.
Fig. 3 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 3. Representation of the upper and lower envelopes (dashed lines) of a signal (solid line).
Fig. 9 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 9. Energy distribution, on average, per IMF for the BWC, RFC, OC and VDS criteria.
Fig. 1 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 1. QG specimen in the adult stage. By Douglas Henrique Bottura Maccagnan, 2021.
Fig. 20 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 20. ROC curves.
Replication Package: Architectural Smell Detection
<p>Replication package of architectural smell detection paper</p>
Figure 7 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 7. The SVM structure used in application two. Similar to AP1, the weights determined during the superoised part of the training are {w0, w1, …, wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.
Simulated HERA Dataset for "Remove First, Detect Later: A Counter-intuitive Approach For Detecting Radio Frequency Interference in Radio Sky Imagery"
Open the record for dataset details and reuse information.
Detecting Chirality-Induced Spin Selectivity in Randomly Oriented Radical Pairs Photogenerated by Hole Transfer. Open data set
<p>Data supporting the original figures 2 through 7 of the related publication.</p>
Figure 4 in The detection of weakly developed plicidentine in teleost teeth using 3D tomography
Figure 4. - Hyperopisus bebe (Mormyridae); tomographic image of the lingual dentary plate. A: Dorsal view of the lingual plate showing a number of side-by-side teeth with roundish apex; white arrow points to anterior; B: Virtual model of a part of the lingual plate showing on the foreground two pulp cavities with dentine folds (arrow heads); the teeth are constituted of a dentine core covered by a thick enameloid cap; white and black asterisks point to the roundish apex of teeth (de = dentine; en = enameloid); C: Virtual section crossing the pulp cavity of several teeth; the central one (b) shows sharp folds (arrowheads). Section of tooth (a) crosses the higher part of the pulp cavity and the dentine (de) appears as a circular section; it is overlain by a thin enameloid layer (en). Scale bars: A = 2.5 mm; B = 1 mm; C = 0.5 µm.
Magnetic Resonance Imaging to Detect Brain Damage in Patients With Multiple Sclerosis
ClinicalTrials.gov study NCT00393588. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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.