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9,302 results for “DETECT”
Evaluation of recombination detection methods for viral sequencing
<p>Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations, or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods have been developed over the past two decades, however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed five recombination detection methods (PhiPack (Profile), 3SEQ, GENECONV, VSEARCH (UCHIME), and gmos) to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Further, we provide a practical example for the analysis and validation of empirical data. We find that recombination detection methods need to be scalable, use an analytical approach and resolution that is suitable for the intended research application, and are accurate for the properties of a given dataset (e.g. sequence diversity and estimated recombination frequency). Analysis of simulated and empirical data revealed that the assessed methods exhibited considerable trade-offs between these criteria. Overall, we provide general guidelines for the validation of recombination detection results, the benefits and shortcomings of each assessed method, and future considerations for recombination detection methods for the assessment of large-scale viral sequencing data.</p>
SODD – Subaquatic Object Detection Dataset
<p>The SODD dataset consists of 3168 images of underwater objects consistent with underwater installations, including the following categories: propeller, pipe, pipe_type2, net, red_fin, qr_codes. The total number of annotated objects are 8934, with the following distribution among categories: propeller (1092 instances), pipe (2008), pipe_type2 (886), red_fin (760), net (1556), qr_codes (2632). </p><p>The images were acquired from a collection of videos (mp4 format, with HD resolution, and 16 FPS). The videos were acquired in an indoor pool using a Blue Robotics low-light USB camera. The vehicle used is a BlueROV2 from Blue Robotics with the heavy configuration retrofit kit, providing full actuation in 6 degrees of freedom.</p><p>More details in the documentation enclosed in the file SODD_Documentation. </p><p> </p><p><i>Acknowledgements</i></p><p>We would like to thank Professors Damiano Varagnolo and Annette Stahl from the Norwegian University for Science and Technology for the valuable advice during the planning phase of the data collection. </p><p> </p>
[data]Pollution source detection with low-cost low-accuracy sensors through coupling forward data assimilation and inverse optimization
<p>The data used in the case study(Cases-S1,S2,S3)in manuscript "Pollution source detection with low-cost low-accuracy sensors through coupling forward data assimilation and inverse optimization"</p>
Quantum Software Design Patterns Detection for Qiskit and QASM circuits
Open the record for dataset details and reuse information.
Dataset and codes of Conflict detection and resolution in macaque frontal eye fields
<p>The dataset and Matlab code related to the paper published in Communications Biology. </p>
A hippocampo-cortical pathway detects changes in the validity of an action as a predictor of reward
<p>This repository contains the source files for all behavioural data.</p>
Canine 230K Consortium chip data originated from the drug detection dogs
<p>Drug detection dogs play integral roles in society. However, the interplay between their behaviors and genetic characteristics underlying their performance remains uninvestigated. Herein, more than 120,000 genetic variants were evaluated in 326 German Shepherd or Labrador Retriever dogs to profile the genetic traits associated with various behavioral traits related to the successful training of drug detection dogs.</p>
POMABuster: Detecting Price Oracle Manipulation Attacks in Decentralized Finance
Open the record for dataset details and reuse information.
Fig. 3 in Allometry and morphological integration shape the chemical detection system in Liolaemus lizards (Squamata, Iguania)
Fig. 3. Allometry relationships of log10 SVL size with the palatal opening. Below, shapes reconstruction associated.
Fig. 4 in Allometry and morphological integration shape the chemical detection system in Liolaemus lizards (Squamata, Iguania)
Fig. 4. Phylomorphospace reconstruction showing in the X axis the first principal components (PC) scores and in the Y axis those second PC scores of: A-vomeronasal organ, B- palatal opening and C- tongue shape. In blue, those species belong to Argentinean group, and in red, those belong to Chilean group. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Allometry and morphological integration shape the chemical detection system in Liolaemus lizards (Squamata, Iguania)
Fig. 5. Integration patterns among vomeronasal organ (brown), palatal opening (black), tongue (cream) and the number of precloacal epidermal glands (orange). The number above indicates the coefficient of correlation, whereas black bars indicate those significant associations. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Allometry and morphological integration shape the chemical detection system in Liolaemus lizards (Squamata, Iguania)
Fig. 2. Phylogenetic relationships of Liolaemus lizards based on Esquerr´e et al. (2019), showing the relationships among studied species. In blue, those species belong to Argentinean group, and in red, those belong to Chilean group. Columns at the right show shapes symmetrized reconstruction of vomeronasal organ, palatal opening and tongue by each species. The size of the black circle represents the number of precloacal epidermal glands per studied species, whereas the black bars represent the size of snout-vent length. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6 in Allometry and morphological integration shape the chemical detection system in Liolaemus lizards (Squamata, Iguania)
Fig. 6. Two block analyses showing the integration patterns of tongue (cream) with: A-palatal opening (black) and B– VNO (brown). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Allometry and morphological integration shape the chemical detection system in Liolaemus lizards (Squamata, Iguania)
Fig. 1. Drawing of Liolaemus cuyanus showing A-vomeronasal organ, B- palatal opening and. C- tongue. Yellow points correspond to landmarks set, and green points correspond to those landmarks used to reach symmetry and posteriorly were removed. Red points correspond to semi-landmarks. All were set on the right side; scale below corresponds to 1 mm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Development of Dataset for Detection of Various Events
<p>This dataset presents a substantial benchmark dataset comprising a total of 790 videos. In which 360 instances are non-shoplifting and 460 are shoplifting. The videos are captured with mobile phones and IP cameras. The resoluton of the videos are 352 × 640 . This dataset can be used in human action recognition behaviors, including the development of robbery detection systems, human movement detection systems, safety systems, theft detection systems, and anomaly detection in automatic surveillance cameras.</p>
Hydrogen-Bonded Organic Frameworks Based on Endless-Stacked Amides for Iodine Capture and Detection
<p>Raw data of </p>
Grape detection
<p>This dataset was created during the AI-SPRINT project</p> <p>Concerns grapes from the Cognac region</p> <p>The dataset includes 464 images. with approximate 7000 labled objects<br>Grapebunches are annotated in YOLOv8 andYOLOv5 format.</p> <p>The following pre-processing was applied to each image:<br>* Auto-orientation of pixel data (with EXIF-orientation stripping)</p> <p>The following augmentation was applied to create 3 versions of each source image:<br>* 50% probability of horizontal flip<br>* Randomly crop between 0 and 20 percent of the image<br>* Random exposure adjustment of between -20 and +20 percent<br>* Random Gaussian blur of between 0 and 5 pixels<br>* Salt and pepper noise was applied to 3 percent of pixels</p>
Grape Disease Detection
<p><br>The dataset includes 75 images. and focuses on detecting general disease in wineplants.<br>Leaf-disease are annotated in YOLOv8 and YOLOv5 format.</p> <p>The following pre-processing was applied to each image:<br>* Auto-orientation of pixel data (with EXIF-orientation stripping)</p> <p>The following augmentation was applied to create 3 versions of each source image:<br>* 50% probability of horizontal flip<br>* Randomly crop between 0 and 25 percent of the image<br>* Random brigthness adjustment of between -25 and +25 percent<br>* Random exposure adjustment of between -25 and +25 percent<br>* Random Gaussian blur of between 0 and 2.5 pixels<br>* Salt and pepper noise was applied to 2 percent of pixels</p>
Supplementary material 1 from: Erdélyi A, Hartdégen J, Malatinszky Á, Vadász C (2023) Historical reconstruction of the invasions of four non-native tree species at local scale: a detective work on Ailanthus altissima, Celtis occidentalis, Prunus serotina and Acer negundo. One Ecosystem 8: e108683. https://doi.org/10.3897/oneeco.8.e108683
Derived data of Ailanthus altissima, Celtis occidentalis, Prunus serotina and Acer negundo from the National Forestry Database and its archives.
BSO-GBD:Behavior Sequence-Oriented GitHub Bot Detection model
<p>Our dataset is primarily divided into three parts. Firstly, there are human accounts (Human) and bot accounts (Bot). Both of these types appear similar to regular developer accounts, lacking any distinctive markings. The second category comprises self-bot accounts, typically formed by GitHub apps. These accounts generally bear an explicit "bot" label, and we exclude them in our classification.</p> <p>The dataset is structured as follows: a total of 4786 ordinary accounts, with 4325 being Human accounts and 461 being Bot accounts. Each account internally consists of a CSV table with the following fields: user_name; repo_name; event_type_L1; event_type_L2; event_type_L3; timestamp. These data constitute our raw dataset.</p>
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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.