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9,302 results for “DETECT”
DETECTION_PROSODIQUE_Stim_Audio
<p>ADEPT</p>
DETECTION_PROSODIQUE_Data_PRE
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Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA
<p>Scripts and methylation frequencies for "Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA" manuscript.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (SeisRox Pro modelling projects - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets used for seismic modelling based on geomodels created by interpretation of DOMs.</p> <p>The dataset includes:</p> <ul> <li>Five different SeisRox modelling projects of the Landnørdingsvika model;</li> </ul> <p>1) base-case model and base-case+Noise in one project,</p> <p>2) 30Hz model,</p> <p>3) no_thin_units model,</p> <p>4) no_karst model,</p> <p>5) perfect_illumination model.</p> <ul> <li>Two different SeisRox modelling projects of the Treskelodden model;</li> </ul> <p>1) base-case model, base-case+Noise, 30Hz model, perfect_illumination model all in one project,</p> <p>2) model with 10x thicker Kapp Starostin layer in the overburden model.</p> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Synthetic seismic models - supplementary data)
<p>This dataset includes pre-loaded seismic models based on digital outcrop models (DOMs) representing 11 different case scenarios with varying signal processing setting (frequency, illumination angle, noise) and different geological models for the DOMs. The case scenarios are set up to investigate detection tresholds in seismic models of thin beds with a high degree of lithological variation. </p> <p>The dataset includes:</p> <ul> <li>A Petrel 2022 project with 11 pre-loaded seismic models based on two different geomodels presented in the paper.</li> <li>The input SGY files for each seismic section and PSF used.</li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Well data for elastic rock parameters - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets of the well data from boreholes on Spitsbergen. it also includes various maps of Svalbard to see where the boreholes are located. Rock parameters were plotted and determined using the blueback toolbox add-on to Petrel. </p> <p>The dataset used for rock parameter statisitcs includes:</p> <ul> <li>A Petrel 2022 project with well data (gamma ray, density, sonic, P-velocity, lithology) from the two boreholes Reindalspasset I (7816/2-1) and Tromsøbreen II (7617/1-2).</li> <li>Excel sheets with the wireline-log data used from the two boreholes. </li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
ADVANCEMENTS IN AIRLINE SECURITY: EVALUATING MACHINE LEARNING MODELS FOR THREAT DETECTION
<p>This study assessed the performance of four machine learning algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)—for predicting airline security threats using a dataset of 100,000 entries with 30 features. The models were evaluated based on accuracy, precision, recall, F1-Score, and AUC-ROC. The Neural Network achieved the highest performance, with an accuracy of 88%, precision of 86%, recall of 85%, F1-Score of 85.5%, and AUC-ROC of 0.90, demonstrating superior capability in capturing complex, non-linear patterns. The Random Forest model followed, with an accuracy of 85%, precision of 83%, recall of 82%, F1-Score of 82.5%, and AUC-ROC of 0.87, offering a robust and generalizable solution. The SVM model attained an accuracy of 81%, precision of 80%, recall of 78%, F1-Score of 79%, and AUC-ROC of 0.84, showing effective binary classification but with higher computational costs. The Decision Tree model, while interpretable, had the lowest performance metrics: accuracy of 78%, precision of 76%, recall of 72%, F1-Score of 74%, and AUC-ROC of 0.79. The results indicate that Neural Networks and Random Forests are the most effective models for airline security threat detection, with Neural Networks providing the highest overall accuracy and AUC-ROC.</p>
ST-CDA-detection-results-mars
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Detections of interstellar aromatic nitriles 2-cyanopyrene and 4-cyanopyrene in TMC-1
<div>This is the dataset for the article:</div> <div>Detections of interstellar aromatic nitriles 2-cyanopyrene and 4-cyanopyrene in TMC-1</div> <div> </div> <div>Authors:</div> <div>GOTHAM collaboration</div> <div> </div> <div>DOI:</div> <div>10.1038/s41550-024-02410-9</div> <div> </div> <div>Journal:</div> <div>Nature Astronomy</div> <div> </div> <div>Corresponding authors:</div> <div>Gabi Wenzel, Ilsa R. Cooke, Brett A. McGuire</div> <div> </div> <div>#--------------------------------------------------------------------------------------</div> <div> </div> <div>Folders:</div> <div> - DATA_2-cyanopyrene_spectroscopy contains all SPCAT/SPFIT files for 2-cyanopyrene (Fig. A2)</div> <div>including the partition function as a .qpart file</div> <div> - DATA_4-cyanopyrene_spectroscopy contains all SPCAT/SPFIT files for 4-cyanopyrene (Fig. A3)</div> <div>including the partition function as a .qpart file</div> <div> - DATA_FigA2 contains the unsmoothed GOTHAM data as presented in Fig. A2</div> <div> - DATA_FigA3 contains the unsmoothed GOTHAM data as presented in Fig. A3</div> <div> - DATA_SNRLinesinStack_2-cyanopyrene contains the unsmoothed GOTHAM data centered around each</div> <div>of the 150 brightest SNR lines that are contained in the 2-cyanopyrene stack (Fig. 2)</div> <div> - DATA_SNRLinesinStack_4-cyanopyrene contains the unsmoothed GOTHAM data centered around each</div> <div>of the 150 brightest SNR lines that are contained in the 4-cyanopyrene stack (Fig. 2)</div> <div> - SupplementaryData1_CyanopyreneStructures.csv contains the structures for the (transition)</div> <div>states reported in the potential energy surface </div> <div> </div> <div>#--------------------------------------------------------------------------------------</div> <div> </div>
RBOD: An annotated satellite imagery dataset for automated river barrier object detection
<h3>Introduction:</h3> <p>Millions of river barriers have been constructed worldwide for flood control, hydropower generation, and agricultural irrigation. The lack of comprehensive records on the locations and types of river barriers, particularly small barriers such as weirs, limits our ability to assess their societal and environmental impacts. Integrating satellite imagery with object detection algorithms holds promise for the automatic identification of river barriers on a global scale. However, achieving this objective requires high-quality image datasets for algorithm training and testing. Hence, this study presents a large-scale dataset named the River Barrier Object Detection (RBOD), making it the first publicly available dataset specifically for river barrier object detection.</p> <p>The RBOD dataset comprises 4,872 high-resolution satellite images and 11,741 meticulously annotated oriented bounding boxes (OBBs). In this dataset, river barriers can be classified into five classes: dams, groynes, locks, sluices, and weirs. The effectiveness of the RBOD dataset was validated using five typical object detection algorithms, namely YOLOV8-OBB, Oriented R-CNN, Rotated Faster R-CNN, R3Det, and Rotated RetinaNet, which provide performance benchmarks for future applications.</p> <h3><span>Usage Notes:</span></h3> <p>The RBOD dataset consists of three folders (namely, '<em>images</em>', '<em>labels_voc</em>', and '<em>labels_yolo</em>') and a .txt file named '<em>class</em>':</p> <p>·'<em>images</em>' folder - contains 4872 satellite images (.jpg).</p> <p>·'<em>labels_voc</em>' folder - contains 11,741 .xml files for annotations in PASCAL VOC format. In these .xml files, the position of OBB is represented as (cx, cy, width, height, angle), where 'cx' and 'cy' denote the center coordinates, 'width' and 'height' are the lengths along the x- and y-axes, and 'angle' is the clockwise rotation angle relative to the x-axis.</p> <p>·'<em>labels_yolo</em>' folder - contains 11,741 .txt files for annotations in YOLO format. In these .txt files, the OBB is represented as (class_index, x1, y1, x2, y2, x3, y3, x4, y4), where ‘class_index’ denotes the target category, and ‘x1, y1, x2, y2, x3, y3, x4, y4’ are the normalized coordinates of the four corners of the bounding box.</p> <p>·'<em>class</em>' txt file - record the classifications of river barriers and their indices, which correspond to the ‘class_index’.</p> <p>Note that each folder splits into three subfolders: train (70%), test (20%), and val (10%).</p>
Building an eDNA surveillance toolkit for invasive rodents on islands: Can we detect wild-type and gene drive Mus musculus?
<p>R code, data, and qPCR efficiencies for experiments related to this paper.</p>
Power grid attack detection and state estimation with machine learning
<p>Detecting attacks and estimating states of power grids from partial observations with machine learning. A manuscript submitted to PRX Energy.</p>
Point Absorber Detection in LIGO Test Masses with YOLO
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Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels
<p>This folder contains datasets, code, and analysis scripts for reproducing the results in the paper "Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels". Specifically, it contains the following folders: </p> <ol> <li>Datasets <ol> <li>Dataset for each Vessel model in different maneuvers and conditions, i.e., waypoint, zigzag, ocean current (including IND and OOD).</li> <li>Configurations (Configurations used to train the DTM for each vessel in different maneuvers).</li> </ol> </li> <li>Results <ol> <li>Raw results for RQ1 and RQ2 for each vessel in different maneuvers, including the results from DTM-R and DTM-E.</li> <li>Results for RQ3 (statistical tests) derived from the above results. </li> </ol> </li> <li>Scripts and Code <ol> <li>Scripts used for statistical tests.</li> <li>DTC implementation (shown with one vessel as example).</li> <li>Code used for hyperparameter optimization.</li> <li>Inference class used for integration of DTM and DTC.</li> </ol> </li> </ol>
ClimateNet Dataset as used in "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data"
<p>ClimateNet dataset as it was used by us for the study: "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data" (https://gmd.copernicus.org/preprints/gmd-2024-60/).</p> <p> </p> <p>For the original dataset refer to: https://portal.nersc.gov/project/ClimateNet/</p>
No increase is detected and modelled for the seasonal cycle amplitude of δ13C of atmospheric carbon dioxide: scripts and data to prepare figures
<p>The file contains the scripts and data to plot the graphics displayed in Joos et al., No increase is detected and modelled for the seasonal cycle amplitude of δ13C of atmospheric carbon dioxide, Biogeosciences, in press, November 2024.</p>
ADSCAN: AI-Powered Multi-modal Framework for Enhanced Ad Detection and Web Safety
<p>We published codes and datasets (SITE-D, TEXT-D and part of IMG-D) for paper "ADSCAN: AI-Powered Multi-modal Framework for Enhanced Ad Detection and Web Safety."</p>
Molecular assays to detect the presence and viability of Phytophthora ramorum and Grosmannia clavigera
<p>Protocols for the RNA and DNA extractions, cDNA synthesis and qPCR and RT-qPCR for detecting and assessing viability of the Phytophthora ramorum and Grosmannia clavigera tree pathogens.</p>
Data from: Using a comprehensive DNA barcode library to detect novel egg and larval host plant associations in a Cephaloleia Rolled-leaf Beetle (Coleoptera: Chrysomelidae)
To fully understand the ecology and evolution of plant-herbivore interactions, information regarding the life history of both immature and adult insect stages is essential. However, most knowledge of plant-herbivore associations is derived from observations of adults. One reason for this bias is that species identification of immature stages is usually challenging. DNA barcodes can be used to identify immature stages to the species-level. This technique compares short sequences of the appropriate DNA barcode loci (e.g., mitochondrial COI gene for insects) of an unidentified specimen to a known DNA barcode library. The accuracy of DNA-based identifications depends on the comprehensiveness of the DNA barcode library. We generated a comprehensive DNA barcode library for a community of Rolled-leaf Beetles (Coleoptera: Chrysomelidae) in a premontane tropical forest in Costa Rica. The DNA barcode COI accurately identified all beetle species included in this study. Using this DNA barcode library, we identified eggs and larvae of Cephaloleia histrionica Baly with 100% confidence. This new record of C. histrionica is unique in that this species completes its life cycle on a bromeliad, whereas most Cephaloleia species are associated with plants from the order Zingiberales. The life cycle, diet breadth, immature stages, and sexual dimorphism are described for C. histrionica.
Data from: Detecting hybridization by likelihood calculation of gene tree extra lineages given explicit models
Explanations for gene tree discordance with respect to a species tree are commonly attributed to deep coalescence (also known as incomplete lineage sorting [ILS]), as well as different evolutionary processes such as hybridization, horizontal gene transfer and gene duplication. Among these, deep coalescence is usually quantified as the number of extra lineages and has been studied as the principal source of discordance among gene trees, while the other processes that could contribute to gene tree discordance have not been fully explored. This is an important issue for hybridization because interspecific gene flow is well documented and widespread across many plant and animal groups. Here, we propose a new way to detect gene flow when ILS is present that evaluates the likelihood of different models with various levels of gene flow, by comparing the expected gene tree discordance, using the number of extra lineages. This approach consists of proposing a model, simulating a set of gene trees to infer a distribution of expected extra lineages given the model, and calculating a likelihood function by comparing the fit of the real gene trees to the simulated distribution. To count extra lineages, the gene tree is first reconciled within the species tree, and for a given species tree branch the number of gene lineages minus one is counted. We develop a set of R functions to parallelize software to allow simulations, and to compare hypotheses via a likelihood ratio test to evaluate the presence of gene flow when ILS is present, in a fast and simple way. Our results show high accuracy under very challenging scenarios of high impact of ILS and low gene flow levels, even using a modest dataset of five to ten loci and five to ten individuals per species. We present a powerful and fast method to detect hybridization in presence of ILS. We discuss its advantage with large dataset (such as genomic scale), and also identifies possible issues that should be explored with more complex models in future studies.
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.