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Dataset results
625 results for “Anomaly”
Risk and anomaly sensor for the steel production [CSS5] - Integrated
<p>The CAPRI risk and anomalies sensor for the steel production aims to provide an estimate of the processing risk for intermediate products at different stages of the processing chain. This risk estimation will be the basis for a decision support system, which will provide recommendations regarding the further processing of a semi-product. For instance, if an item will likely fail to meet the quality specification for its original customer order, the support system could recommend changing the target order the product will be assigned to, or it could recommend to immediately recycle the item or to do some reprocessing. The earlier we identify a problematic item, the less energy and time needs be wasted in its further processing, therefore the solution can lead to substantial savings both in cost and CO2 emissions.</p> <p>This video describes the integration of the risk and anomalies sensor into CAPRI's cognitive automation platform (CAP).</p>
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>
Griffiths Phase, Re-Entrant Spin-Glass Behaviour and Schottky Anomaly in Anti-Site Disordered Double Perovskite Pr2MnNiO6
<p>In the present study, the effect of anti-site disorder is explored on the magnetic properties of Pr2MnNiO6. Due to anti-site disorder, a reduced TC preceded by a Griffith phase has been observed. At low temperatures, we also report the development of the unconventional spin glass phase in co-existence with the cluster-like ferromagnetic order. The signature of the re-entrant spin glass phase is revealed by the frequency-dependent ac-susceptibility measurements. The spin glass behavior is also supported by the slow decay of thermo-remanent magnetization. A broad Schottky anomaly has been observed in the specific heat data near 10 K, along with a linear spin-glass term that was suppressed in the magnetic field of 5T. The analysis of the specific heat data indicates the presence of true singlet state of ground state of Pr3+ in Pr2MnNiO6</p>
Anomaly Engine Development and Testing Datasets
<p>The following datasets have been used for development an testing of Anomaly Engine Webapp. They includes data samples for all the 4 scenarios:<br> 1 - Simple Self Financing<br> 2 - Self Financing by bank account<br> 3 - Indirect SF (by recharge)<br> 4 - Indirect Account Self Financing</p> <p>Each file row represents a graph relationship between a source node (<code>source</code>) and a destination node (<code>target</code>).</p> <p>Files must have the following schema:</p> <ul> <li><code>source_type</code>: Source node label;</li> <li><code>source_attributes</code>: Source node attributes. Can be null;</li> <li><code>target_type</code>: Destination node label;</li> <li><code>target_attributes</code>: Destination node attributes. Can be null;</li> <li><code>relation_type</code>: Relationship label;</li> <li><code>relation_attributes</code>: Relationship attributes. Can be null.</li> </ul> <p>Attributes must be expressed as key-value pairs separated by a semicolon <code>;</code>. For example</p> <pre><code>key1=value1;key2=value2</code></pre> <p> </p> <p>The datasets are also available in <a href="https://gitlab.infinitech-h2020.eu/pilot16/aml-graph-payments-anomaly-detection/-/tree/master/docker/docker_resources/docker_webapp/src/webapp_dash/assets">Infinitech Marketplace</a> </p>
Hurricane Disturbance Vegetation Anomaly (HDVA) [Data set for Turner et al.]
<p>The Hurricane Disturbance Vegetation Anomaly (HDVA) is a rapid assessment approach to understand the severity of ecological damage from a high intensity storm event on an otherwise healthy, mature mangrove forest. Data archived here focuses on Cuba, where Hurricane Irma (Category 5) hit the northern coast in September 2017 and caused wide-spread damage to mangroves and coastal forests. Local scientists were not able to assess the full extent or severity of damages through field work due to limited infrastructure and resources, so they turned to remote sensing analysis. We developed a multitemporal, multiresolution approach to assess the damage 1) extent and 2) relative severity using changes from the typical green-leaf phenology represented by the Enhanced Vegetation Index with MODIS and Sentinel-2 data. All data was processed in Google Earth Engine API to access and utilize large amounts of historical data, as well as compare sensors’ spatial resolution impacts on results. This data set includes the HDVA products and categorization of data by quartile of damage (catastrophic, severe, moderate, mild, no loss). </p>
MARVEL - Malta Audio Visual Anomaly Dataset (MAVAD)
<p>The raw audio-video data was collected from two locations on the island of Malta one in Zejtun, a town close to the industrial region on the eastern coast, and another in Mgarr, a rural town on the western coast. Three AV cameras were deployed, two in Zejtun (Zejtun Scrapyard and Zejtun Field) and one in Mgarr, resulting in three datasets with the same titles.</p> <p> </p>
Dataset Artifact for Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems
<p>The dataset contains a small set of application runs from Eclipse supercomputer. The applications run with and without synthetic HPC performance anomalies. More detailed information regarding synthetic anomalies can be found at: https://github.com/peaclab/HPAS.</p> <p>We have chosen four applications, namely LAMMPS, sw4, sw4Lite, and ExaMiniMD, to encompass both real and proxy applications. We have executed each application five times on four compute nodes without introducing any anomalies. To showcase our experiment, we have specifically selected the "memleak" anomaly as it is one of the most commonly occurring types. Additionally, we have also executed each application five times with the chosen anomaly. The dataset we have collected consists of a total of 160 samples, with 80 samples labeled as anomalous and 80 samples labeled as healthy. For the details of applications please refer to the paper.</p> <p>The applications were run on Eclipse, which is situated at Sandia National Laboratories. Eclipse comprises 1488 compute nodes, each equipped with 128GB of memory and two sockets. Each socket contains 18 E5-2695 v4 CPU cores with 2-way hyperthreading, providing substantial computational power for scientific and engineering applications.</p>
Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (anomalies) 0-5 cm
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/388acf49-4cd6-4b32-9abb-e0f343c48cfd. Produced on QA4SM (https://qa4sm.eu)
Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (anomalies) 5-10 cm
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/5219f9fd-d309-4405-befb-cf829f695bee. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global (anomalies)
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/ba00b64b-3b61-4426-9b07-29df7e8e3620. Produced on QA4SM (https://qa4sm.eu)
Synthetic noisy datasets for submarine cable magnetic anomaly locaition
<p>Synthetic noisy datasets for submarine cable magnetic anomaly locaition, including a training, a validation and two test data sets.</p>
Primary data for: "Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long-term rodent datasets"
<p>Datasets</p>
The global marine free air gravity anomaly model SDUST2022GRA
<p>SDUST2022GRA.nc is the global marine free air gravity anomaly model covering 80°S~82°N and 0~360°E on 1′×1′ grids. SDUST2022GRA is recovered from multi-radar and ICESat-2 laser altimeter data to investigate the contribution of ICESat-2.</p>
Taraxacum anomalie (BR0000024931410)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Taraxacum anomalie (BR0000024931403)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Detecting anomalies in melt-extruded 3D printed parts using in situ data
Open the record for dataset details and reuse information.
Drivers and projections of global surface temperature anomalies at the local scale
Open the record for dataset details and reuse information.
MarkusThill/MGAB: The Mackey-Glass Anomaly Benchmark
<p>This repository contains the Mackey-Glass anomaly benchmark (MGAB), which is composed of synthetic Mackey-Glass time series with non-trivial anomalies. Mackey-Glass time series are known to exhibit chaotic behavior under certain conditions. MGAB contains 10 MG time series of length 100k. Into each time series 10 anomalies are inserted with a procedure as described below. In contrast to other synthetic benchmarks, it is very hard for the human eye to distinguish the introduced anomalies from the normal (chaotic) behavior.</p>
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>
Standard Bouguer anomaly model achieved by multi-source Bouguer gravity anomaly Bayesian data fusion algorithm in Sichuan-Yunnan region
<p>* Method: Based on the equivalent source inversion and Bayesian uncertainty quantization theory, a new multi-source gravity data fusion algorithm is developed, which effectively solves the multi-source data fusion problem with different noise and datum.</p> <p>* Standard Bouguer anomaly is Fused from WGM2012 Bouguer gravity anomaly model and 394 gravity profile data measured in Sichuan-Yunnan region. Fusion anomaly results can eliminate datum draft between multi-source gravity and reduce incoherent noise.</p> <p>* Spatial resolution of the standard Bouguer anomaly is about 20 kilometers.</p> <p>* Correcting deviations means the difference between the fused standard Bouguer anomaly model and the WGM2012 Earth gravity model.</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.