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9,302 results for “DETECT”
Assisted migration across fixed seed zones detects adaptation lags in two major North American tree species
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Environmental conditions and call-broadcast influence detection of eastern forest owls during standardized surveys
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Assessment of four in vitro phenotypic biofilm detection methods in relation to antimicrobial resistance in aerobic clinical bacterial isolates
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Iowa herptile detection histories and landcover metrics
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Detection of prey odors underpins dietary specialization in a Neotropical top-predator: how army ants find their ant prey
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Underwater caustics disrupt prey detection by a reef fish
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Ancient geological dynamics impact neutral biodiversity accumulation and are detectable in phylogenetic reconstructions
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HARP Echolocation click and broadband anthropogenic event detections Southern California Bight: 2017-2019 sites E and H
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Seismic detection of oceanic internal gravity waves from subaerial seismometers
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Data for: Sequencing and variant detection of eight abundant plant-infecting tobamoviruses across Southern California wastewater
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USFS Durham Lab water analysis method detection limit (MDL) limit of quantification (LOQ), 2010 - present
This dataset documents limits in the precision of water analyses produced by the USFS, NRS lab in Durham, NH. Generally precision of water analyses is limited at low concentrations due to limits of detection or limits of precise quantification. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Supplementary material 1 from: Grimm-Seyfarth A, Zarzycka A, Nitz T, Heynig L, Weissheimer N, Lampa S, Klenke R (2019) Performance of detection dogs and visual searches for scat detection and discrimination amongst related species with identical diets. Nature Conservation 37: 81-98. https://doi.org/10.3897/natureconservation.37.48208
: Data type: statistical data
LEN-DB - Local earthquakes detection: a benchmark dataset of 3-component seismograms built on a global scale
<p>In this study ( <a href="http://www.sciencedirect.com/science/article/pii/S2666544120300010">The paper</a> ) we present a large dataset of 1,249,411 3-component seismograms, recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide, including 631,105 3-component seismograms generated by 304,878 local earthquakes and labeled as earthquakes (EQ), and 618,306 ones labeled as noise (AN). The choice of collecting only local earthquake-data is motivated by the fact that small-magnitude events, which generate relatively small amplitudes and are easily attenuated, are often problematic to detect but provide valuable information about earthquake processes. The labeled data are split into HDF5-Groups: <em>EQ</em> and <em>AN</em>. Each of these groups contains as many HDF5-Datasets as the number of 3-component seismograms; these are labeled in accordance to the format <em>net_sta_starttime</em>, where <em>net</em>, <em>sta</em>, and <em>starttime</em> represent the seismic network, station, and start time of the seismograms. Each HDF5-Dataset (i.e. each triplet of seismograms) has an attribute, which allows accessing the respective metadata. In addition, the HDF5-Group <em>Stations</em> allows accessing stations’ metadata through as many HDF5-Datasets (which are labeled in accordance to the format <em>net_sta)</em> as the number of receivers employed for collecting the waveforms.</p> <p>This global dataset is intended to be used for carrying out a multitude of seismological and signal processing tasks on single-station recordings, and its size particularly suits machine learning (ML) applications.. Application of ML to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings with high accuracy (93.2%), even if applied in regions not investigated by the training set. We make the dataset publicly available as a unique file in HDF5 data format, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in seismology and signal processing.</p>
Using Bursty Announcements for Detecting BGP Routing Anomalies
<p>This dataset contains all the require data to reproduce the Indonesia incident. This is provided to facilitate reproducibility of results presented in the following paper:</p> <p>Pablo Moriano, Raquel Hill, and L. Jean Camp. "Using bursty announcements for detecting BGP routing anomalies." vol. 188, 107835, 2021. DOI: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.comnet.2021.107835" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.comnet.2021.107835</span></a></p> <p>These data are provided for non-commercial purposes only. If you use this dataset for research, please be sure to cite the above paper.</p>
Supplementary Information Table for J. Geophys. Res. article: A Survey of Small-Scale Waves and Wave-Like Phenomena in Jupiter's Atmosphere Detected by JunoCam
<p>Text version of the Table in the Supplementary Information file associated with the J. Geophys. Res. article by Orton et al. (2020) A Survey of Small-Scale Waves and Wave-Like Phenomena in Jupiter’s Atmosphere Detected by JunoCam. The columns provide values for various sizes and orientations associated with waves detected by the Juno spacecraft's JunoCam visible imaging instrument. The values include the perijove, PJ (orbit close approach), image number, number of wave crests detected, the mean longitude and latitude, the length and width of the wave packet, the wavelength (mean distance between wave crests), and the tilt (angle of the wave crest with respect to the direction of the wave).</p>
Juno Waves Detection of Dust Impacts near Jupiter
<p>This is the supporting data set for the paper by the same title published in AGU JGR Space Physics. DOI 10.1029/2019JE006367</p> <p>Abstract:</p> <p>The Juno spacecraft entered orbit at Jupiter on July 5, 2016. Since then, Juno has orbited Jupiter in high inclination orbits, crossing the ring plane near perijove 21 times. During 20 of the 21 crossings, the Waves instrument detected signals induced by dust impacts. The impact rate profiles show peaks of order 6/s around the ring plane with half width at half max ~2000-3000 km. The polarity ratio of the impact signals didn't follow the areas of the antennas exposed to dust impacts that change due to the rotation of the spacecraft, suggesting Waves detects impacts on the Juno spacecraft and not just on the Waves antennas. The impact rate profile changed during Perijove 19, when the spacecraft rotation axis was tilted to the south, increasing the area of the solar panels exposed to impacts, indicating that the detected impacts were on the spacecraft body. Grain sizes of order 1 micrometer are estimated, and the differential size distribution has a slope of -5.1.</p> <p>Notes on zip archive:</p> <p>There are subdirectories for each figure including a PDF of the figure and one or more text files with the data supporting the figure. Where the labeling in the text file is not quite complete, we've added a Fign-readme.txt file.</p> <p>Figures 9 and 10 are in the same directory as the two figures just have more of the same and all the data for both figures is in the same file.</p>
Deep learning models to detect microsatellite instability in colorectal cancer from histological images
<p>This repository contains trained deep learning models (based on shufflenet) for detecting microsatellite instability (MSI) in histological images of colorectal cancer. Expected input is 512x512x3, output is categorical (MSI vs. not MSI).</p> <p>The models are provided as MAT (Matlab R2019a) files. All models are described in detail in the publication "Clinical-grade detection of microsatellite instability in colorectal cancer by deep learning: an international multicenter study"</p> <p>More information on the original model: <a href="https://www.mathworks.com/help/deeplearning/ref/shufflenet.html">https://www.mathworks.com/help/deeplearning/ref/shufflenet.html</a></p>
Trained CNN for skin detection - https://arxiv.org/abs/1802.02531
<p>Trained CNN for skin detection</p> <p>https://arxiv.org/abs/1802.02531</p> <p> </p>
Detectable urbanization effect in observed surface air temperature data series in Pyongyang region, DPR Korea-Supporting Information-data
<p>This is the calculated dataset as supplemental information of a paper entitled "Detectable urbanization effect in observed surface air temperature data series in Pyongyang region, DPR Korea", which will be likely published in Geophysical Research Letters.</p> <p> </p> <p> </p> <p> </p>
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
ScienceDex guides
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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.