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9,302 results for “DETECT”
The SoccerSum Dataset for Automated Detection, Segmentation, and Tracking of Objects on the Soccer Pitch
<p>The SoccerSum dataset was curated by capturing and annotating soccer videos from the Norwegian Eliteserien league. This collection spans three years, covering 2021, 2022, and 2023. It comprises 750 frames from 41 unique sequences, with 4 to 40 frames per sequence, carefully chosen to represent a diverse selection of scenarios encountered in professional soccer games.</p>
Pulsars detected in the GP survey in both imaging and beamforming searches.
<p><span>S</span><span>I</span><span> </span><span>is the mean flux density of the pulsars </span><span>in Stokes I image from this work, S</span><span>V</span><span> </span><span>is the Stokes V for the pulsars that were </span><span>detected in Stokes V image in this work and S</span><span>lit</span><span> </span><span>is the low-frequency flux density </span><span>available in the literature: ’M’ stands for MWA-image detection by Murphy et al.</span><span>(2017), ’X’ stands for MWA-incoherent beam detection by Xue et al. (2017), ’S’ </span><span>stands for MWA SMART survey detections by Bhat et al. (2023b) and ’K’ stands </span><span>for LOFAR detection by Kondratiev et al. (2016).</span><span> </span><span>α</span><span> </span><span>is the spectral index of the </span><span>pulsars.</span><span> </span><span>α</span><span> </span><span>is calculated using the flux densities of the pulsars in MWA Stokes I </span><span>image (154 MHz) and the RACS Stokes I image (888 MHz).</span></p>
Comparative performance in the detection of four coronavirus genera from human, animal, and environmental specimens
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Figure 2 in Detection of Male Mediterranean Fruit Flies (Diptera: Tephritidae): Performance of Trimedlure Relative to Capilure and Enriched Ginger Root Oil
Figure 2. Capture of C. capitata males in TML- versus EGRO-baited Jackson traps for a single replicate in an Oahu coffee field. Abscissa represents period of lure ageing, where 0 weeks represents fresh lures. Bar heights indicate mean of 20 traps per lure type; error bars represent + 1 SE. Symbols above bars show results of the Tukey HSD test comparing the 2 lures for each ageing category, where an asterisk indicates P <0.001 and ns indicates no significant difference.
A UWB Radar and Machine Learning-Based Tool for Detecting Victims Through Foliage in Search and Rescue Operations
<h1>Project Description</h1> <p>During our research in University of West Attica (UniWA) we addressed the problem of victim detection through foliage in Search and Rescue operations. For this purpose, a dataset of respiration signal sessions in the field was collected using a proposed tool consiting of a UWB pulsed radar system, and then these data fed a machine learning tool to enhance FR's operations by providing predictions about human presence behind foliage. In addition, two anemometer sensors were used to record wind data, and a respiration belt was employed to obtain the ground truth measurements about the subject's respiration rate.</p> <p>The setup for each session was the same. The UWB radar [<a href="https://sensorlogic.ai/sensor-products">1</a>] was mounted on tripod facing the foliage, the subject was located behind the foliage wearing a respiration belt [<a href="https://www.zephyranywhere.com/">4</a>] for breath recording. On the same tripod two anemometers [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-speed-transmitter/">2</a>],[<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-direction-transmitter/">3</a>] were placed so a comprehesive image of the wind condiditon during the session could be obtained. These sensors were connected to a laptop via USB, about 3 meters away. The distance between the tripod and the foliage was fixed at 1 meter. Foliage (mostly bushes and small olive trees) had length varying from 1 to 3 meters and the subject (in case of presence session) was from 0.5 to 3 meters away from the foliage. In total we never exceeded the 9.2 meters range (unambiguous range) limit of the radar.</p> <h1>Dataset Description</h1> <p>The dataset consists of 268 sessions of radar, wind and respiration belt data, of which 141 sessions correspond to human presence and 127 to human absence. Each session has a duration of 150 seconds, thus amounting to approximately 6 hours of data for human presence and approximately 5.5 hours of data for human absence.</p> <h2>Dataset Contents</h2> <p>Each session folder is given an individual name X = posixtime; this name designates the exact time (in posixtime format) when the session was started. For example, in the dataset preview below there can be seen one folder named "1688457913"; this folder corresponds to the measurement session that was initiated exactly on 1688457913 in posixtime format (in this example, X = 1688457913). Furthermore, for the "X" posixtime-named folder, there are the following subfolders and files:</p> <p>1. One subfolder named Workspaces_X, containing:</p> <ul> <li>Files named "<em>Workspace_k.mat</em>", where k the number of the created workspaces containing radar signal recording at 16 FPS.</li> <li>A file named "<em>settings.mat</em>", containing the device settings and the session's distances regarding topology.</li> <li>A file named "<em>windData_original.mat</em>", containing the original data from anemometer sensors saved from the data stream at 4 FPS, provided from a microcontroller followed RS485 protocol.</li> </ul> <p>2. Two files containing the raw data recorded from the respiration belt (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data.)</p> <ul> <li>The "<em>1_YY_MM_DD_HH_MM_general.csv</em>", contains the timestamp in datetime of the sensor and the Android device, the heart rate estimation, the mean breaths per minute and the included IMU belt sensor measurement.</li> <li>The "<em>1_YY_MM_DD_HH_MM_wave.csv</em>", contains the timestamp in datetime of the sensor and the Android device, and 18 values (FPS) of the strain gauge sensor changes from the respiration belt.</li> </ul> <p>3. A file named "<em>X.xlsx</em>", containing the concatenation of the workspaces of the radar signal.</p> <p>4. A file named "<em>windData_X.csv</em>", containing the synchronized data of anemometer sensors with radar data.</p> <p>5. A file named "<em>BeltWfm_X.xlsx</em>", containing the synchronized data of respiration belt with radar data (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data).</p> <h1>Proposed Tool COTS components</h1> <ol> <li>SLMX4 UWB pulse radar [<a href="https://sensorlogic.ai/sensor-products">1</a>]</li> <li>Wind Speed [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-speed-transmitter/">2</a>] and Direction [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-direction-transmitter/">3</a>] sensors</li> <li>Wind data recording equipment (UART TTL to RS485 Converter, MT3608 DC/DC converter, Arduino)</li> <li>Respiration belt [<a href="https://www.zephyranywhere.com/">4</a>]</li> </ol>
Datasets for ICSE'25 submission: "Scalable and Adaptive Log-based Anomaly Detection: A Synergistic Approach"
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Data for UAV detection of river embankment piping
<p>Data for a manuscript submitted to Water Resources Research.</p>
DIOPSIS camera annotated detection and classification dataset 2021
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Effectiveness of passive sampling for the detection and genetic characterization of human viruses in wastewater (Dataset 1)
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Effectiveness of passive sampling for the detection and genetic characterization of human viruses in wastewater (Dataset 2)
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Analysis of the performance of Faster R-CNN and YOLOv8 in detecting fishing vessels and fishs in real time
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Ship-detection-YOLOv8-main
<p>Ship-detection-YOLOv8-main</p>
Drone Flight Videos for Vegetation Monitoring, Fire Sensor Detection, and Power Line Inspection
<p>This video set showcases drone flights conducted in three different scenarios: vegetation monitoring in natural areas, recognition and localization of a fire detection sensor, and visual inspection of a power line. The videos serve as practical examples of how drones can be used in environmental monitoring and critical infrastructure maintenance applications. The visualization of the results and analysis of the collected data is performed through the GridWatch platform, enabling efficient and detailed monitoring. </p>
Data and code for X-ray detected EPR manuscript
<p>Raw data and associated evaluation/simulation code for Figs. 1-3 in the main text</p>
Detection of insulin in insulin-deficient islets of patients with type 1 diabetes_original microscopy images
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Replication Package: Architectural Smell Detection
<p>Replication package of architectural smell detection paper</p>
Process Mining Algorithm for Online Intrusion Detection System
<p>The pre-processed data we used in the paper.</p>
Petal size in rapeseed: novel QTL and candidate genes detected through genome-wide association study and transcriptome comparison
<p>Petal size determines the value of ornamental plants, and thus their economic worth. However, the molecular mechanisms controlling petal size remain unclear in most non-model species. To identify quantitative trait loci and candidate genes regulating petal size in rapeseed (<i>Brassica napus</i>), we performed a genome-wide association study (GWAS) using data from 588 accessions over three consecutive years. We detected 17 significant single nucleotide polymorphisms (SNPs) associated with petal size, with the most significant SNPs located on chromosomes A05 and C06. A combination of GWAS and transcriptomic sequencing based on two accessions with extreme differences in petal size identified 11 differentially expressed genes (DEGs) that may control petal size variation in rapeseed. In particular, <i>BnaA05</i><i>.</i><i>RAP2.2</i> homologous to <i>RAP2.2</i> in rapeseed may be a critical gene negatively influencing petal size through the ethylene signaling pathway. In addition, a comparison of petal epidermal cells indicated that petal size differences between the two extreme accessions were determined mainly by cell number differences. Finally, we propose a preliminary model for the control of petal size in rapeseed. Our results provide insights into the genetic mechanisms regulating petal size, and also lay the foundation for a better understanding of petal development in plants.</p>
Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection
<p>In the last decade, High-Throughput Sequencing (HTS) has revolutionized biology and medicine. This technology allows the sequencing of huge amount of DNA and RNA fragments at a very low price. In medicine, HTS tests for disease diagnostics are already brought into routine practice. However, the adoption in plant health diagnostics is still limited. One of the main bottlenecks is the lack of expertise and consensus on the standardization of the data analysis. The Plant Health Bioinformatic Network (PHBN) is an Euphresco project aiming to build a community network of bioinformaticians/computational biologists working in plant health. One of the main goals of the project is to develop reference datasets that can be used for validation of bioinformatics pipelines and for standardization purposes.</p> <p>Semi-artificial datasets have been created for this purpose (Datasets 1 to 10). They are composed of a "real" HTS dataset spiked with artificial viral reads. It will allow researchers to adjust their pipeline/parameters as good as possible to approximate the actual viral composition of the semi-artificial datasets. Each semi-artificial dataset allows to test one or several limitations that could prevent virus detection or a correct virus identification from HTS data (<i>i.e.</i> low viral concentration, new viral species, non-complete genome).</p> <p>Eight artificial datasets only composed of viral reads (no background data) have also been created (Datasets 11 to 18). Each dataset consists of a mix of several isolates from the same viral species showing different frequencies. The viral species were selected to be as divergent as possible. These datasets can be used to test haplotype reconstruction software, the goal being to reconstruct all the isolates present in a dataset.</p> <p><span>A GitLab repository (<a href="https://gitlab.com/ilvo/VIROMOCKchallenge">https://gitlab.com/ilvo/VIROMOCKchallenge</a>) is available and provides a complete description of the composition of each dataset, the methods used to create them and their goals.</span></p>
Evaluation results from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This deposit contains the evaluation results for the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. See the <a href="https://doi.org/10.5281/zenodo.5105815">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
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.