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169 results for “timing of detection”
M100 dataset: time-aggregated data for anomaly detection
<p>This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, <a href="https://www.hpc.cineca.it/hardware/marconi100">https://www.hpc.cineca.it/hardware/marconi100</a>). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast. <br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.</p> <p>This particular dataset is made for anomaly detection purposes, it contains the same data as the main dataset but aggregated over time, with one Parquet file for each node. The data is distributed in tarballs, each one including all the files relative to the nodes contained in a given rack. For each file, the rows represent periods of 15 minutes, with the columns being aggregated values (average, standard deviation, min, max) over all the IPMI metrics that are available for the node; an additional column contains anomaly labels from Nagios.</p> <p>More details can be found in the companion repository: <a href="https://gitlab.com/ecs-lab/exadata">https://gitlab.com/ecs-lab/exadata</a>, including the spatial distribution of the nodes in the room.</p>
Simulation data and code used for the publication in Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes"
<p>Simulation data used in the publication Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes". The simulation data set, and the code developed to generate such data, are included. Details in the published paper </p>
Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation
<p>Automatic patient-level recognition of four <em>Plasmodium</em> species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation</p> <p>Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier</p> <p><strong>Abstract:</strong></p> <p>Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of <em>Plasmodium</em> species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate <em>Plasmodium</em> species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among <em>Plasmodium </em>positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for <em>P. falciparum</em>, <em>P. malariae </em>and <em>P. ovale/vivax,</em> respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking.</p> <p><strong>Data collection:</strong></p> <p>The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), <em>Trypanosoma brucei </em>(n=2,773), and red blood cells infected by <em>P. falciparum </em>(n=43,545), <em>P. ovale </em>(n=4,651), <em>P. vivax </em>(n=4,115), <em>P. malariae </em>(n=2,849) and <em>Babesia divergens</em> (n=5,142).</p> <p>The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients.</p> <p>Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).</p>
Data and code for implementing time-to-detection occupancy model
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Using a real-time location system to detect behavioral changes in ewes with subclinical mastitis and their lambs
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Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series
<p>The datasets contain extreme time series obtained from the post-processed 3D wave fields simulated using HOS-Ocean, a high-order spectral model (HOSM) that solves the deterministic propagation of nonlinear wave fields in deep water (Ducrozet et al., 2016).</p> <p>Voermans. (2020). Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4028014</p> <p> </p>
Data from: Disturbance detection in Landsat time series is influenced by tree mortality agent and severity, not by prior disturbance
<p><span>Landsat time series (LTS) and associated change detection algorithms are useful for monitoring the effects of global change on Earth's ecosystems. Because LTS algorithms can be easily applied across broad areas, they are commonly used to map changes in forest structure due to wildfire, insect attack, and other important drivers of tree mortality. But factors such as initial forest density, tree mortality agent, and disturbance severity (i.e., percent tree mortality) influence patterns of surface reflectance and may influence the accuracy of LTS algorithms. And while LTS algorithms are widely used in areas with a history of multiple disturbance events during the Landsat record, the effectiveness of LTS algorithms in these conditions is not well understood. We compared products from the LTS algorithm LandTrendr (<span>Landsat-based Detection of Trends in Disturbance and Recovery) with</span> a unique field dataset from a landscape heavily influenced by both wildfire and spruce beetles (<i>Dendroctonus rufipennis</i>) since c. 2000. We also compared LandTrendr to other common methods of mapping fire- and spruce beetle-affected areas. We found that LandTrendr more accurately detected wildfire than spruce beetle-induced tree mortality, and both mortality agents were more easily detected when they occurred at high severity. Surprisingly, prior spruce beetle outbreaks did not influence the detectability of subsequent wildfire. Compared to alternative disturbance mapping approaches, LandTrendr predicted a c. 40% lower area affected by wildfire or spruce beetle outbreaks. <span>Our findings indicate that disturbance type- and severity-specific differences in omission error may have broad implications for disturbance mapping efforts that utilize Landsat data. Gradual, low-severity disturbances (e.g., background tree mortality and non-stand replacing disturbance) are pervasive in forest ecosystems, yet they can be difficult to detect using automated LTS algorithms. Whenever possible, methods to account for these biases should be incorporated in LTS-based mapping efforts, including the use of multispectral ensembles and ancillary spatial data to refine predictions. However, our findings also indicate that LTS algorithms appear to be robust in areas with multiple disturbance events, which is important because these areas will increase as new acquisitions extend the length of the Landsat record. </span></span></p>
Dataset for Anomaly Detection Using Inter-Arrival Curves for Real-time Systems
<p>The dataset shows the input files and detailed results for the experiments discussed in the paper. A README file provide more details on the data.</p>
Fecal standing crop with real time correction using scat detection dogs to estimate population density
<p>Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Fecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find fecal samples in the field and to estimate the probability of which feces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten fecal samples of gray brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 x 1200 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental fecal samples to determine the FSC parameters that were subsequently used with naturally occurring fecal samples (also dog-detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 23% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.54 individuals km-2 (SD = 2.21, Dry) and 5.52 indiv. km-2 (SD = 3.71, Wet).</p> <p><em>Synthesis and applications:</em> Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability, and climate in fecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.</p>
Does scent attractiveness reveal women's ovulatory timing? Evidence from signal detection analyses and endocrine predictors of odor attractiveness
<p>Odor cues associated with shifts in ovarian hormones indicate ovulatory timing in females of many nonhuman species. Although prior evidence supports women's body odors smelling more attractive on days when conception is possible, that research has left ambiguous how diagnostic of ovulatory timing odor cues are, as well as whether shifts in odor attractiveness are correlated with shifts in ovarian hormones. Here, 46 women each provided 6 overnight scent and corresponding day saliva samples spaced 5 days apart, and completed luteinizing hormone tests to determine ovulatory timing. Scent samples collected near ovulation were rated more attractive, on average, relative to samples from the same women collected on other days. Importantly, however, signal detection analyses showed that rater discrimination of fertile window timing from odor attractiveness ratings was very poor. Within-women shifts in salivary estradiol and progesterone were not significantly associated with within-women shifts in odor attractiveness. Between-women, mean estradiol was positively associated with mean odor attractiveness. Our findings suggest that raters cannot reliably detect women's ovulatory timing from their scent attractiveness. The between-women effect of estradiol raises the possibility that women's scents provide information about overall cycle fecundity, though further research is necessary to rigorously investigate this possibility.</p>
Landmine Detection Using Electromagnetic Time Reversal-Based Methods. Part 1: Classical TR, Iterative TR, DORT and TR-MUSIC
<p>In this repository, you can find the simulation files used to reproduce the results presented in the paper titled "Landmine Detection Using Electromagnetic Time Reversal-Based Methods." If you find these files useful, please cite the paper as it helps acknowledge the work and support future research.</p> <p>This repository is organized into three main folders, each serving a distinct purpose and containing specific types of files:</p> <p><strong>CST-MWS Files</strong>: This folder contains the necessary files to simulate the examples discussed in the paper using CST Microwave Studio (CST-MWS). These files are essential for setting up and running the simulations that replicate the experimental results. </p> <p><strong>TR-MUSIC</strong>: In this folder, you will find the MATLAB m-file required to implement the TR-MUSIC (Time Reversal-MUltiple SIgnal Classification) method proposed in the paper. This MATLAB script is designed to help users apply the TR-MUSIC algorithm to their data, enabling them to detect landmines effectively. </p> <p><strong>2D-GFB</strong>: The 2D-GFB folder contains the MATLAB m-file necessary to run the 2D TR-MUSIC and DORT (Decomposition of the Time Reversal Operator) methods. These advanced techniques are crucial for analyzing two-dimensional data and enhancing the accuracy of landmine detection. </p> <p><br>By organizing the repository in this manner, we aim to make it easier for researchers and practitioners to access and utilize the simulation files effectively. Whether you are replicating the study's results or applying these methods to new datasets, the files and instructions provided should serve as a valuable resource.</p> <p>If you encounter any issues or have questions regarding the use of these files, please do not hesitate to reach out for support. We hope this repository aids your research and contributes to advancements in landmine detection technologies.</p> <p> </p>
An exploratory study (with and without time pressure) using mouse dynamics to detect faking-good behavior in the MMPI-2 and PPI-R validity scales
<p>Please find here the dataset generated and analyzed during the study entitled "Can mouse dynamics detect faking-good behavior in personality questionnaires? An exploratory study (with and without time pressure) using the MMPI-2 and PPI-R validity scales". Moreover, here you can find the source code of the experiment to execute the task using MouseTracker software, the code of the statistical analysis and a file containing the instructions to replicate ML model results reported in the original paper.</p>
Data from: Hiding among colors: Background color diversity impedes detection time
<p><span>Avoiding detection is vital for the survival of many animals. Factors extrinsic to animals, such as the visual complexity of the background, have been shown to impede the detection of animals. Studies using artificial and natural backgrounds have attributed background complexity to various visual features of the background. One feature that has received less attention is the diversity of color (hue) in the background. We used chickens and artificial backgrounds containing perceptually distinct elements in experiments to test whether color and luminance diversity affect detection time. We found that color diversity in the background impeded detection, while color diversity in prey and luminance diversity in the background did not impede detection. We also did not find an effect of luminance contrast on detection time. Our study suggests a prey animal can benefit in terms of increased detection times by predators when resting on backgrounds with enhanced color diversity.</span></p>
Machine Learning Integrated High Quantum Yield Blue Light Carbon Dots for Real-time and On-site Detection of Cr(VI) in Groundwater and Drinking Water
<p>RGB和Kmeans提取后含有Cr(VI)水样的图像数据</p>
Real-time Lymphatic Channel Visualization Improves Bilateral Sentinel Lymph Node Detection in Endometrial Cancer
ClinicalTrials.gov study NCT05191212. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Hiding among colors: Background color diversity impedes detection time
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Fecal standing crop with real time correction using scat detection dogs to estimate population density
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Does scent attractiveness reveal women’s ovulatory timing? Evidence from signal detection analyses and endocrine predictors of odor attractiveness
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Data from: Disturbance detection in Landsat time series is influenced by tree mortality agent and severity, not by prior disturbance
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Can time-to-detection models with fewer survey replicates provide a robust alternative to traditional site-occupancy models?
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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.