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625 results for “Anomaly”
Data for Estimation of 3D Moho depths beneath Southern Indian Shield by inverting seismic constraint gravity anomalies
<p>This is a help file for a description of all Data used for the implementation of our present paper<br> 'Estimation of 3D Moho depths beneath Southern Indian Shield by inverting seismic constraint gravity anomalies.' </p> <p> </p>
Dataset for Sound-based Anomalies Detection in Agricultural Robotics Application
<p>This data set contains data related to a Mowing Intelligent Tool (MowIT).</p> <p>Two different microphones were used to collect the sound samples, recording the audio with just one single channel, with a sampling rate of 44100 Hz and 16 bits resolution.</p> <p>The data provided by an inertial measurement unit (IMU) was also recorded since that was already integrated into the MowIT.</p> <p>Two different data collections were performed in different open-air environments with grass to cut.</p> <p>In each collection, eight different sample sets were made, five with the machine cutting using a trimmer line and the other three using the blades. Various combinations were used in each set, and tools were or were not placed on each of the three cutting axes of the MowIT. For each group, the acquisitions were designated from 0 to 7.</p> <p>Each folder of the first collection is a combination containing two audio files, one for each microphone used, the IMU data and a photograph of the lower part of the MowIT to understand the configuration used.</p> <p>In the second collection, to improve the variety of data, three distinct sub-sets were performed for combination: the first with the MowIT turned on but not cutting grass and the next two cutting grass. </p> <p>In samples 4 and 7, there is one audio where the MowIT cuts but stops due to motor stress. In sample 6, the initial recording was not made without cutting grass, and only the two recordings were made cutting grass.</p> <p> </p> <p> </p> <p> </p>
Dataset for model input of WRF model for the paper:Modulation of Extratropical Cyclones by Previous Cyclones via the Sea Surface Temperature Anomaly over the Sea of Japan in Winter
<p>This is the dataset and code for generating the lower boundary condition which used in our study submitted to the JGR-Atmospheres. The meteorological data for the initial condition are available on NCEP-FNL ftp database.</p>
Sulfate anomaly during CAFE-EU/BLUESKY 10 days LAGRANTO back trajectories
<p><strong>Short description</strong></p> <p>The dataset contains trajectories along one segment of the flight path during research flight 01 (RF01) during the CAFE-EU/BLUESKY mission. In this segment we observed a particulate sulfate anomaly and are interested in the air mass origin. Therefore, we built a cross structure to sample in total 231 trajectories for each release point. The detailed method and analysis is published in the corresponding and related work.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742, TPChange: The Tropopause Region in a Changing Atmosphere (<a href="https://tpchange.de/">https://tpchange.de/</a>).</p>
Dataset for Non-resonant Anomaly Detection with Background Extrapolation
<p>These are the datasets used in the journal version of the Non-resonant Anomaly Detection with Background Extrapolation paper. The datasets are simulated using MadGraph5 aMC@NLO, Pythia 8.310, and Delphes. There are 0.2M signal events of semi-visible jets in five sets of parameters (invisible-ratio, Z' mass) = { (1/3, 4 TeV), (1/3, 2 TeV), (1/3, 3 TeV), (0, 4 TeV), (2/3, 4 TeV) }, 18.6M background events of SM QCD jets (including background, ideal AD background, and simulated background) for training, and 21.4M background events for testing. The detailed breakdown of number of events after selections in different regions is listed in Table1 of the paper. The input parameter cards used for generating background and signal events are also included.</p>
Figure 1 in Morphological anomalies in polychaetes: Perinereis species (Polychaeta: Annelida) examples from the Brazilian coast
Figure 1. Location of sampling sites in states of Brazil.
Dataset: 2023 Aircraft traffic and GPS anomalies aggregated per hexbins
<p>We divided the globe into hexbins, each with an average area of 385 square kilometers. Once the hexbin grid was established, data from the GPS gaps, GPS deviations, and Traffic Density datasets were used to populate these hexbins with relevant information. On average, each hexbin has around 23,478 flights passing through it.</p> <ul> <li><strong>Total Records</strong>: 14,117 - total number of hexbin on a map, where number of flights > 0</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>id:</strong></li> <li><strong>WKT</strong>: Well-Known Text representation of a POINT (senter of each hexbin) in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>flights</strong>: number of flights traveled trough that hexbin in 2023</li> <li><strong>gaps</strong>: number of GPS gap incidents registered in that hexbin in 2023</li> <li><strong>deviations</strong>: number of GPS deviation incidents started in that hexbin in 2023</li> </ul> </ul>
Intrinsic negative magnetoresistance from the chiral anomaly of multifold fermions
<p>Data used for the plots in the publication "Intrinsic negative magnetoresistance from the chiral anomaly of multifold fermions" by F.Balduini et al</p>
Sound database of Industrial Machine for Audio Anomaly Detection
<p><span>Audio anomaly detection(AAD) can seamlessly determinefaults in industrial machines and improve the efficiency of predictive maintenance systems. However, the unavailability of audio sound recordings of real industrial machines operating in their actual industrial setup has limited the efficacy of detection systems. Many different audio databases exist having collections of sounds from dummy (or real) systems operating in controlled environments but a collection of audio sounds from actual industrial machines is missing. Therefore, audio sound recordings of an Air compressor machine working in its natural industrial environment are presented. Only real sounds of an actual machine are captured. Synthetic mixing of sounds is avoided. Damaging the machine to create an anomalous state is avoided. Yet fourteen different unhealthy states are identified and their audio recordings are presented. Dataset with varied values of SNRs is also presented. Spectrograms are plotted and spectral shape parameter values of the developed corpus are calculated. The findings demonstrate the divergence in the developed database and its usefulness in building an effective AAD system for a real industrial machine.</span> </p>
Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi
<p>This is the dataset and companion website to the paper <a href="https://www.usenix.org/conference/atc17/program/presentation/hoilan-jorgesen">Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi</a> which was published at USENIX ATC 17.</p> <p>Also published at <a href="https://www.cs.kau.se/tohojo/airtime-fairness/">https://www.cs.kau.se/tohojo/airtime-fairness/</a></p>
Hovmöller diagram of ARGO subsurface temperature anomalies with histograms of daily profile data count
<p>Hovmöller diagram of ARGO subsurface temperature anomalies with histograms of daily profile data count over two different zones in the pacific ocean. The anomalies were computed from GODAS reanalysis over a 1981-2010 climatology .</p>
Figure 5. A in On tooth anomalies and the loss of Canis lupus (Mammalia: Carnivora) in Turkey*
Figure 5. A- Carnassial loss (Skull No: 3) and B- premolar loss (Skull No: 27).
Baseline Performance and MAS reaction to network Anomalies in Demo 1
<p>Collected Latency data and packet captures for the demo described by the paper 10.5281/zenodo.12820942 "Augmented Reality App with AI-based Pervasive Latency Monitoring of RAN and Programmable Metro Packet-Optical Networks" presented during ICTON24 conference.</p>
Forecasted crop yield anomalies for NUTS3 level regions in the Pannonian Basin
<p>The datasets contain crop yield anomaly forecasts on NUTS3 level for the Pannonian Basin 2002-2016. The files contain the crop yield anomaly forecasts calculated one and two months before the harvest. Further information about the methodology can be found here:</p> <p>https://www.sciencedirect.com/science/article/pii/S0168192323002873</p>
Comprehensive Dataset for Detecting Road Anomalies in Diverse Real-World Situations
<p>In Smart Cities, technologies are playing an important role in efficiently managing the rapid growth of the world's industrialization today. The deployment of surveillance cameras has proliferated to improve public safety and security. Many Closed-Circuit Television (CCTV) cameras have been installed to monitor and safeguard public spaces efficiently within the cities. Despite advancements in technology, video and image processing still largely rely on manual observation. This manual analysis is time-consuming, prone to missing critical details, and costly in terms of labor and resources. Nevertheless, monitoring large video feeds for long periods indicates fatigue, demise of focus, and errors, particularly when video surveillance is a necessity. <br>Road anomaly detection is one of the prominent computer vision issues that researchers have investigated to guarantee public safety. Road anomaly identification is increasingly difficult and complex due to the variety and complexity of abnormalities. <br>Deep learning algorithms must be efficient but also need a large dataset to train to recognize road anomalies in different environments. We proposed a custom real-world data set containing road anomaly images and videos that are made available to the public and private surveillance systems. Primary data were collected from diverse sites in Pakistan, and the data were gathered by recording videos and capturing images by using mobile and surveillance cameras The dataset encompasses five major categories of road anomaly effects.: vehicle accidents, vehicle fire, fighting, snatching(gunpoint), and potholes that classification modeling while promoting improvement in both scientific research and realistic application. The dataset also encompasses annotations with You Only Look Once (YOLO) based bounding boxes and class label files in text format for every image. <br>The researchers can utilize data to train and validate their anomaly detection algorithms and models, thus increasing public security and safety. This dataset focuses on natural environment scenes with a detailed examination of safe transportation and impacts on broader environmental knowledge. Data can give to the liable and ethical arrangement of Artificial Intelligence technologies in surveillance security system</p>
Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet
<p>Supplementary data used in the article <em>Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet</em> submitted to The Cryosphere.</p> <p>Please cite the corresponding paper if you use this data. </p> <p>We supply</p> <ol> <li>the regridded CMIP6 precipitation (pr) and temperature (tas) model output for Greenland.</li> <li>local precipitation-near surface temperature sensitivities for each model in the folder <em>local_slopes_precipitation</em></li> <li>The monthly anomalies for near-surface temperature and precipitation as well as the local change in precipitation (compare to the climatology, in %) with respect to the climatology (year 1980-200) in the folder <em>2100_anomalies</em></li> <li>The CMIP6 monthly climatology from the years 1980-2000 (precipitation & near-surface temperature) </li> <li>The monthly precipitation and near-surface temperature anomalies from 2015-2100 in the folder <em>2100_85years</em></li> </ol> <p> </p> <p>License of CMIP6 model output: </p> <p>CMIP6 model data produced is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
EagleEye: A general purpose density anomaly detection method
<p>The Air2m_northern_DJF.npy and Air2m_northern_JJA.npy datasets is derived from the <em>NCEP-NCAR Reanalysis 1 data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a></em>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.</p> <p>The LHC Olympics R&D dataset used in the article can be downloaded from https://zenodo.org/records/4536377 .</p> <pre> </pre> <p> </p>
Applied Geophysics - Magnetic Anomaly Data Mosaic of the Socorro-Guaxupé Geological Area (Brazil)
<p>The Socorro-Guaxupé area is a complex structural environment formed during Proterozoic times, associated with the formation of the Gondwana Supercontinent. The notable magnetic signatures of these geological areas were selected for an Applied Geophysical project for undergraduates in the Geology Course at the University of São Paulo in 2024.</p> <p>This grid mosaic encompasses airborne magnetic data from areas 15, 14, 7, and 2 of the CODEMIG repository (<a href="http://www.codemig.com.br/" target="_new" rel="noopener">http://www.codemig.com.br/</a>) as well as data from areas 1105, 1117, 1039, 4099, 4112, and 4210 from the Geological Survey of Brazil (SGB-CPRM). The surveys were gridded with cell sizes of 1/4 of the flight line spacing, using the minimum curvature method. Additionally, they were continued to a nominal altitude of 700 m for decreasing noise signal, due to the variable spatial resolution and acquisition techniques. Grid stitching was performed using pairs of grids, with the stitching method implemented in Oasis Montaj software. The locations of all surveys are presented in the figure "socorro-guaxupe_surveys.tif."</p> <p>We would like to thank the team at the Laboratory for Research in Applied Geophysics (LPGA) and the professors from the Rio de Janeiro State University (UERJ) and GeoAtlantico Institute for their technical support in assembling this data.</p> <p>This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 (L. Szameitat 88887.798323/2022-00)</p> <p> </p>
The groundwater-level anomalies observed by 446 monitoring wells in the North China Plain during 2003-2017
<p>The groundwater level anomalies observed by 446 monitoring wells in the North China Plain (NCP) during 2003-2017. The monthly GWL data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The wells were relatively evenly distributed in NCP region . The GWL anomalies were calculated by subtracting the mean GWL values during 2003-2017 from the monthly time series values of each well. Unit: meter</p>
High-dimensional Anomaly Detection with Radiative Return in e+e- Collisions
<p>Numpy files of Pythia + Delphes simulated e+e- collisions used as DNN/PFN training inputs.</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.