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625 results for “Anomaly”
Maps of anomalies in heavy rainfall across Central Europe from the Little Ice Age to near present
<p>This dataset includes the data displayed in</p> <p>Förster, K., Thiele, L.-B. (2020): Variations in sub-daily precipitation at centennial scale. <em>npj Clim Atmos Sci</em> <strong>3, </strong>13. https://doi.org/10.1038/s41612-020-0117-1</p> <p>For each year, a csv file is provided containing anomalies as xyz point data (latitude, longitude, anomaly), referring to the center of each grid cell (~30 km).</p> <p> </p> <p> </p>
Dataset: timeseries of temperatures and anomalies for the city of Paris (France) for Climate 101 Galaxy training
<p>Dataset is originally downloaded from <a href="https://knmi-ecad-assets-prd.s3.amazonaws.com/ensembles/data/Grid_0.1deg_reg_ensemble/tg_ens_mean_0.1deg_reg_v20.0e.nc">https://knmi-ecad-assets-prd.s3.amazonaws.com/ensembles/data/Grid_0.1deg_reg_ensemble/tg_ens_mean_0.1deg_reg_v20.0e.nc</a> </p> <p>Then 3 single locations have been extracted: </p> <ul> <li>Paris (France): latitude=48.85341,longitude=2.3488</li> <li>Freiburg (Germany): latitude=47.996894,longitude=7.841431</li> <li>Oslo (Norway): latitude=59.911491,longitude=10.75793</li> </ul> <p>Climatologies and anomalies have been computed using <a href="https://code.mpimet.mpg.de/">cdo</a></p> <p>This dataset is meant to be used for teaching purposes only.</p>
Mixed Layer Depth Anomaly
<p>time is in days since 0</p>
Data from: Why concatenation fails near the anomaly zone
Genome-scale sequencing has been of great benefit in recovering species trees, but has not provided final answers. Despite the rapid accumulation of molecular sequences, resolving short and deep branches of the tree of life has remained a challenge, and has prompted the development of new strategies that can make the best use of available data. One such strategy – the concatenation of gene alignments – can be successful when coupled with many tree estimation methods, but has also been shown to fail when there are high levels of incomplete lineage sorting. Here, we focus on the failure of likelihood-based methods in retrieving a rooted, asymmetric four-taxon species tree from concatenated data when the species tree is in or near the anomaly zone – a region of parameter space where the most common gene tree does not match the species tree because of incomplete lineage sorting. First, we use coalescent theory to prove that most informative sites will support the species tree in the anomaly zone, and that as a consequence maximum-parsimony succeeds in recovering the species tree from concatenated data. We further show that maximum-likelihood tree estimation from concatenated data fails both inside and outside the anomaly zone, and that this failure cannot be easily predicted from the topology of the most common gene tree. We show that likelihood-based methods often fail in a region partially overlapping the anomaly zone, likely because of the lower relative cost of substitutions on discordant gene tree branches that are absent from the species tree. Our results confirm and extend previous reports on the performance of these methods applied to concatenated data from a rooted, asymmetric four-taxon species tree, and highlight avenues for future work improving the performance of methods aimed at recovering species tree.
Data from: Positive selection of deleterious alleles through interaction with a sex-ratio suppressor gene in African buffalo: a plausible new mechanism for a high frequency anomaly
Although generally rare, deleterious alleles can become common through genetic drift, hitchhiking or reductions in selective constraints. Here we present a possible new mechanism that explains the attainment of high frequencies of deleterious alleles in the African buffalo (Syncerus caffer) population of Kruger National Park, through positive selection of these alleles that is ultimately driven by a sex-ratio suppressor. We have previously shown that one in four Kruger buffalo has a Y-chromosome profile that, despite being associated with low body condition, appears to impart a relative reproductive advantage, and which is stably maintained through a sex-ratio suppressor. Apparently, this sex-ratio suppressor prevents fertility reduction that generally accompanies sex-ratio distortion. We hypothesize that this body-condition-associated reproductive advantage increases the fitness of alleles that negatively affect male body condition, causing genome-wide positive selection of these alleles. To investigate this we genotyped 459 buffalo using 17 autosomal microsatellites. By correlating heterozygosity with body condition (heterozygosity-fitness correlations), we found that most microsatellites were associated with one of two gene types: one with elevated frequencies of deleterious alleles that have a negative effect on body condition, irrespective of sex; the other with elevated frequencies of sexually antagonistic alleles that are negative for male body condition but positive for female body condition. Positive selection and a direct association with a Y-chromosomal sex-ratio suppressor are indicated, respectively, by allele clines and by relatively high numbers of homozygous deleterious alleles among sex-ratio suppressor carriers. This study, which employs novel statistical techniques to analyse heterozygosity-fitness correlations, is the first to demonstrate the abundance of sexually-antagonistic genes in a natural mammal population. It also has important implications for our understanding not only of the evolutionary and ecological dynamics of sex-ratio distorters and suppressors, but also of the functioning of deleterious and sexually-antagonistic alleles, and their impact on population viability.
Data from: Parallelism isn't perfect: could disease and flooding drive a life history anomaly in Trinidadian guppies?
Nonparallel evolution, where independent populations occupy similar environments but show phenotypic differences, can uncover previously ignored selective factors. We investigated a nonparallelism in the life-history strategy of a Trinidadian guppy population, a system famous for parallel adaptation to differences in predation risk. We tested the hypothesis that high mortality drives an observed fast life-history pattern (i.e., earlier maturation and more frequent reproductive events) that is atypical for a low-predation environment. Using mark-recapture techniques, we compared neighboring low-predation populations, finding significantly higher mortality rates in the population with atypical life-history traits. Mortality was elevated during the wet season, when flooding was common. Moreover, individuals from the anomalous population were more likely to transition from healthy to infected disease states. Our results stand out against previous patterns observed in this system, indicating that higher mortality caused by disease and flooding may have selected for a faster life history. Thus, we highlight that even in systems famous for parallel adaptation, variation in selective pressures can result in nonparallel phenotypic evolution.
Performance Anomaly Detection in Microservice Architectures under Continuous Change
<p>Supplementary material for the master's thesis<br> <strong>"Performance Anomaly Detection in Microservice Architectures under Continuous Change"</strong><br> of<br> <strong>Thomas F. Düllmann</strong></p> <p><br> The folder for the supplemental material is structured as follows:.</p> <ul> <li><strong>abstract-de.txt</strong> (abstract in german)</li> <li><strong>abstract-en.txt</strong> (abstract in english)</li> <li><strong>01-MicroserviceMetamodel</strong> (Eclipse project containing the Ecore meta model and the Xtend generation template)</li> <li><strong>02-AnomalyDetectionImplementation</strong> (Eclipse project containing the implementation of the customized RanCorr approach and the EAR approach) <ul> <li><strong>EARExperimentSetup</strong> (the Evaluation Setup that is run based on the input data from the experiment and the EAR implementation)</li> <li><strong>Kieker</strong> (Kieker and the customized RanCorr approach)</li> </ul> </li> <li><strong>03-ExperimentTools</strong> (supplementary microservices and scripts that were used for the experiment setup. The generated services need to be placed next to these files and have to have the folder prefix "gen-") <ul> <li><strong>jmeter</strong> (the microservice that is used for generating load)</li> <li><strong>jmsserver</strong> (the microservice that runs ActiveMQ to bundle the monitoring logs from the services)</li> <li><strong>monitoringserver</strong> (the microservice that collects the monitoring data from the jmsserver microservice and stores them)</li> <li><strong>registry</strong> (the microservice that is responsible for managing the delays that should be injected)</li> <li><strong>copyResults.sh</strong> (simple bash script that masks the scp command to copy the monitoring data from the monitoringserver to the local file system) </li> <li><strong>deployPackage.sh</strong> (bash script that uploads the docker images to defined remote systems via ssh and initiates the start of the microservices on a Kubernetes cluster using the said docker images)</li> <li><strong>dockerinit.sh</strong> (bash script that goes into the microservice folders to compile and package them and create the corresponding Docker images (useful if running minikube for example))</li> <li><strong>kubeinit.sh</strong> (bash script that starts the microservices on a Kubernetes cluster that is associated with the kubectl Kubernetes tool)</li> <li><strong>kubeclean.sh</strong> (bash script that removes the microservices from a Kubernetes cluster that is associated with the kubectl Kubernetes tool) </li> </ul> </li> <li><strong>04-EvaluationData</strong> <ul> <li><strong>RawData </strong> (the raw data that was extracted from the experiment environment) <ul> <li>kieker-monitoring-data (the Kieker monitoring data obtained from the experiment setup)</li> <li><strong>anomalies.log</strong> (the log data that shows the injected real anomalies)</li> <li><strong>events.log</strong> (log file that contains the timesstamps, the scope and the type of event injections)</li> <li><strong>registry.log</strong> (registry microservice log showing the injections of real and change anomalies)</li> </ul> </li> <li><strong>Results</strong> (results for the evaluation with different thresholds) <ul> <li><strong>*folders*</strong> (contain the results of the anomaly detection using the threshold represented by the folder name)</li> <li><strong>results.csv</strong> (the calulated results in terms of TP/FN/FP/FN for every approach with every threshold)</li> <li><strong>results-calculated.csv</strong> (further metrics that were calculated based on the TP/FN/FP/FN values)</li> </ul> </li> </ul> </li> <li><strong>05-EvaluationServices</strong> (the folders containing the microservices that were used for the evaluation)</li> </ul> <p> </p>
ESA Anomaly Dataset
<p>ESA Anomaly Dataset is the first large-scale, real-life satellite telemetry dataset with curated anomaly annotations originated from three ESA missions. We hope that this unique dataset will allow researchers and scientists from academia, research institutes, national and international space agencies, and industry to benchmark models and approaches on a common baseline as well as research and develop novel, computational-efficient approaches for anomaly detection in satellite telemetry data.</p> <p>The dataset results from the work of an 18-month project carried by an industry Consortium composed of Airbus Defence and Space, KP Labs and the European Space Agency’s European Space Operations Centre. The project, funded by the European Space Agency (ESA), is part of the Artificial Intelligence for Automation (A²I) Roadmap (De Canio et al., 2023), a large endeavour started in 2021 to automate space operations by leveraging artificial intelligence.</p> <p>Further details can be found on the <a href="https://arxiv.org/abs/2406.17826">arXiv</a> and <a href="https://github.com/kplabs-pl/ESA-ADB">Github</a>.</p> <p><em>References</em><br>De Canio, G. et al. (2023) Development of an actionable AI roadmap for automating mission operations. In, 2023 SpaceOps Conference. American Institute of Aeronautics and Astronautics, Dubai, United Arab Emirates.</p>
Data for: Electrical Conductivity of Superionic Hydrous SiO2 and the Origin of Lower-mantle High Conductivity Anomalies Beneath Subduction Zones
<p><strong>Dataset S1.</strong> Experimental conditions and the measured resistance and conductivity of hydrous Al-bearing SiO2. </p>
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</p>
Weekly Carbon Monoxide Anomalies over Maritime Southeast Asia and Weekly Climate Indices
This repository contains weekly atmospheric carbon monoxide (CO) anomalies over the Maritime Southeast Asia (MSEA) region from 2001 to 2019, as well as weekly climate index data. Total column CO from the MOPITT satellite instrument were converted to column average volume mixing ratios (VMR) and were averaged within the MSEA region on a weekly time scale. A climatological seasonal cycle for the weekly time series was created using all 19 years of MOPITT data and was subtracted from the weekly VMRs to create the anomalies. Five climate indices are also provided on a weekly timescale (Nino3.4, AAO, DMI, TSA, OLR proxy for MJO). For details on the MOPITT CO retrieval parameters and the geometry of the MSEA region, see the provided README file. The geometry of the MSEA region and the spatial range of influence of the five climate indices are also plotted on the provided map. These data are associated with the JGR-Atmos. manuscript "Predicting Fire Season Intensity in Maritime Southeast Asia with Interpretable Models" by Daniels et al., (submitted October 2021).
Equatorial waves for: A prediction attempt using equatorial waves for tropical sea surface temperature anomaly by Atlantic zonal mode
<p>The dataset is the wave-induced geopotential output from linear ocean models and potential energy flux by a group-velocity-based wave energy flux scheme in the period (1992–2016), which is involved in building a lightweight model, as well as showing a simple instance of utilizing the wave energy transfer for the prediction of Atlantic Niño/Niñas.</p>
Data for: Global frequency analyses of canine progressive rod-cone degeneration–progressive retinal atrophy and collie eye anomaly using commercial genetic testing data
<p>Hundreds of genetic variants associated with canine traits and disorders have been identified, with commercial tests offered. However, the geographic distributions and changes in allele and genotype frequencies over prolonged, continuous periods of time are lacking. This study utilized a large set of genotypes from dogs tested for the progressive rod-cone degeneration–progressive retinal atrophy (prcd-PRA) G>A missense PRCD variant (n = 86,667) and the collie eye anomaly (CEA)-associated NHEJ1 deletion (n = 33,834) provided by the commercial genetic testing company (Optigen/Wisdom Panel, Mars Petcare Science & Diagnostics). These data were analyzed using the chi-square goodness-of-fit test, time-trend graphical analysis, and regression modeling in order to evaluate how test results changed over time. The results span fifteen years, representing 82 countries and 67 breeds/breed mixes. Both diseases exhibited significant differences in genotype frequencies (p = 2.7 × 10−152 for prcd-PRA and 0.023 for CEA) with opposing graphical trends. Regression modeling showed time progression to significantly affect the odds of a dog being homozygous or heterozygous for either disease, as do variables including breed and breed popularity. This study shows that genetic testing informed breeding decisions to produce fewer affected dogs. However, the presence of dogs homozygous for the disease variant, especially for prcd-PRA, was still observed fourteen years after test availability, potentially due to crosses of unknown carriers. This suggests that genetic testing of dog populations should continue.</p>
Climate (thermal anomalies) and dengue incidence in Brazil aggregated by microregions (MRG)
<p>Climate (thermal anomalies) and dengue incidence in Brazil aggregated by microregions (MRG). The frequency of temperature anomalies wee calculated between 2007 and 2020. Dengue disease notification frequency were aggregated by MRG and divided by population to create incidence rates between 2007 and 2020.</p>
Free-air gravity anomaly data at the continental margin and oceanic basin north of the Daimao Seamount of the South China Sea
<p>This archive includes the free-air gravity anomaly data (in mGal) from the continental margin to the oceanic basin north of the Daimao Seamount in the South China Sea. The data was collected with the help of the Guangzhou Marine Geological Survey. </p>
The elasticity of seifertite at lowermost mantle conditions: implication for velocities anomaly at the Earth's core-mantle boundary
<p>the open data of the study "<span>The elasticity of seifertite at lowermost mantle conditions: implication for</span><span> </span><span>velocities anomaly at the Earth's core-mantle boundary"</span></p>
Segregation of a thermochemical anomaly and coalescence with a large low-velocity province
<p># This is a supplement to the manuscript "Segregation of a thermochemical anomaly and coalescence with a large low-velocity province" currently accepted of Nature Geoscience. </p> <p>The objective of this supplement is to offer the user access to our model outputs, which include reproducible map data files and 3-D images. Additionally, we provide the seismological data utilized in the manuscript.</p> <p>1. The "map_files" folder contains the map data obtained at a depth of 2800 km from our model, as listed in Extended Data Table 1.</p> <p>2. The "paraview_files" directory includes a state file named "Reference_Model_Visualisation.pvsm" that enables the reproduction of Figure 2 as presented in the main text.</p> <p>3. The "tomography" folder contains the map and slice data files of the tomography utilized in the manuscript.</p> <p>4. "quantitative_comparison" folder contains quantitative comparisons of the model results with the S-wave and P-wave votemap. (0: True Negatives, 1: True Positives, 2: False Negatives, 3: False Positives)</p> <p>5. The file "Modeled_Kamchatka_LIPs.xyz" contains the Kamchatka Large Igneous Provinces (LIPs) created by our models, as depicted in Extended Data Figure 7.</p>
CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023
<p><span>Contains processed data to create all figures and tables from the publication Diamond et al. (2024) </span><span>CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023; Geophysical Research Letters </span><span>[Full reference available on publication]</span></p> <p><span> </span></p> <p><span>Directory structure and associated contents listed below. Folders contain all data required to make corresponding figure, apart from where indicated. Full description of analysis in Diamond et al. (2024). Unless otherwise indicated, 'SIE' refers to August Antarctic SIE.</span></p> <p><span> </span></p> <p><span>F1/</span></p> <p><span>f1a_obs_ts.csv: Timeseries of 1979-2023 SIE </span></p> <p><span>f1b_all_instances.csv: All instances (labelled by model, simulation, and simulation year) where an anomaly of magnitude SIE_2023 was detected. SIE timeseries from years -20 to +20 about each anomaly.</span></p> <p><span>f1b_quartiles.csv: Upper and lower quartiles, mean, and median, computed over all instances.</span></p> <p><span> </span></p> <p><span>F2/</span></p> <p><span>F2_a.csv: 5--95% range of SIE variability dataset, for all model simulations, and observations.</span></p> <p><span>F2_b.csv: T_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F2_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span> </span></p> <p><span>F3/</span></p> <p><span>Data for all figures is already provided elsewhere:</span></p> <p><span>F3a: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>F3b: for multi-model ensemble T_aug23 with associated errors in SF6/SF6_all.csv</span></p> <p><span>F3c: for GEV_estimated T_aug23 for all simulations and models in SF6/SF6_all.csv; and<span> </span>5--95% ranges of SIE variability datasets in F2/F2_a.csv</span></p> <p><span> </span></p> <p><span>~</span></p> <p><span>SF1/</span></p> <p><span>SF1_all.csv: SIE timeseries for all model simulations, and associated moving averages.</span></p> <p><span>SF2/</span></p> <p><span>sf2_all.csv: SIE decadal means and ranges for all model simulations.</span></p> <p><span>sf2_quartiles.csv: taken over all simulations above - mean decadal mean in each bin; and lower quartile, median and upper quartile of the decadal ranges in each bin.</span></p> <p><span>SF3/</span></p> <p><span>SF3_obs_dataset.csv: SIE variability dataset for 1979-2023 observations.</span></p> <p><span>SF3_simulations.csv: SIE variability datasets for all model simulations</span></p> <p><span>SF4/</span></p> <p><span>F4_a.csv: p_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_b.csv: p_aug22, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_d.csv: for all model simulations: p-value that simulation SIE variability dataset is drawn from same probability distribution as observations. Computed using Kolmogorov-Smirnoff 2-sample test.</span></p> <p><span>SF5/</span></p> <p><span>Data for both SF5a and b are already provided in F1/f1b_all_instances.csv.</span></p> <p><span>SF6/</span></p> <p><span>SF6_all.csv: for all model simulations, and multi-model ensemble, p_aug23 and T_aug23, with lower and upper errors (computed from 5--95% confidence interval using bootstrapping)</span></p> <p><span>SF7/</span></p> <p><span>Data for SF7a is already provided elsewhere: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>SF7_all.csv: as for SF6_all.csv, but instead using subset of models.</span></p>
Dataset for the article Artificial intelligence for earthquake prediction: a preliminary system based on periodically trained neural networks using ionospheric anomalies
<p>Training and validation data sets along with the corresponding trained convolutional neural network in the article "Artificial intelligence for earthquake prediction: a preliminary system based on periodically trained neural networks using ionospheric anomalies" by Sergio Baselga published in <em>Appl. Sci.</em> <strong>2024</strong>, <em>14</em>(23), 10859; https://doi.org/10.3390/app142310859</p>
Table A.2: Bona fide binaries with astrometric anomalies
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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.