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625 results for “Anomaly”
Daily Anomalies of the Surface Atmospheric Fluxes of the Brazilian Northeast (DASAF-BNE)
<p>This dataset contains the daily anomalies of the main atmospheric fluxes throughout the Brazilian NE region. Geographically it is framed at 42.5°W - 29.75°E/20.5°S - 1.25°N, in the time range from 1979-01-01 12:00:00 to 2017-12-31 12:00:00. The DASAF-BNE dataset with a spatial resolution of 1 degree was the basis for the calculation of the daily anomalies, they were interpolated by the bilinear method to obtain a resolution of 0.25 degrees.</p>
Monthly Anomalies of Oceanographic Parameters in the Brazilian Northeast (MAOP-BNE)
<p>This dataset is formed by the anomalies of the main oceanic parameters: potential temperature, salinity, surface heat flux, mixed layer depth, sea surface height, density, wind forcings and marine currents, this is calculated from the monthly mean dataset SODA with an original resolution of 1/2 degree, which was interpolated with the bilinear method at 1/4 degree. The time period is from 1980-01-16 to 2017-12-12 and is geographically limited by 42°W - 30°W / 20°S - 0°.</p> <p>In this dataset, the coordinate system of the currents and the wind forcings are different from the coordinate system of the rest of the variables, for that reason they are in separate files.</p>
Bibliographic Data from the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review
<p>This database contains all the bibliographic information about the 8673 records found after applying the Search Strategy used for the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review. Such strategy consisted on using seven initial keywords and similar terms of interest (namely: bridge and bridges, etc.): </p> <ul> <li>Bridge.</li> <li>Digital twin.</li> <li>Bridge information modelling.</li> <li>Finite elements.</li> <li>Bridge health monitoring.</li> <li>Anomaly detection algorithm.</li> <li>Cultural heritage.</li> </ul> <p>Six initial queries were done combining the first keyword with the rest of them:</p> <ul> <li>bridge* AND "digital twin*"</li> <li>bridge* AND (BrIM OR "bridge information model*")</li> <li>bridge* AND (FEM OR FEA OR "finite element method*" OR "finite element analy*")</li> <li>bridge* AND ("bridge health monitoring" OR "structural health monitoring")</li> <li>bridge* AND (ADA OR "anomaly detection algorithm*")</li> <li>bridge* AND ("cultural heritage" OR "monument* bridge*" OR "old bridge*" OR "ancient bridge*" OR "historic* bridge*")</li> </ul> <p>As a first screening step, the combination of these 6 initial searches was done to obtain relevant works containing at least three of the main keywords of interest:</p> <ul> <li>#1 AND #2</li> <li>#1 AND #3</li> <li>#1 AND #4</li> <li>#1 AND #5</li> <li>#1 AND #6</li> <li>#2 AND #3</li> <li>#2 AND #4</li> <li>#2 AND #5</li> <li>#2 AND #6</li> <li>#3 AND #4</li> <li>#3 AND #5</li> <li>#3 AND #6</li> <li>#4 AND #5</li> <li>#4 AND #6</li> <li>#5 AND #6</li> </ul> <p>All records found in Scopus where downloaded both in .ris and .csv format and are included in this database. The search was conducted on 10/12/2022.</p> <p>Note: Searches 10, 14, 17 and 21 did not return any records.</p>
Flavor-violating Higgs decays and stellar cooling anomalies in axion models
<p>We study a class of DFSZ-like models for the QCD axion that can address observed anomalies in stellar cooling. Stringent constraints from SN1987A and neutron stars are avoided by suppressed couplings to nucleons, while axion couplings to electrons and photons are sizable. All axion couplings depend on few parameters that also control the extended Higgs sector, in particular lepton flavor-violating couplings of the Standard Model-like Higgs boson h. This allows us to correlate axion and Higgs phenomenology, and we find that BR(h → τe) can be as large as the current experimental bound of 0.22%, while BR(h → μμ) can be larger than in the Standard Model by up to 70%. Large parts of the parameter space will be tested by the next generation of axion helioscopes such as</p>
A global dataset of SST anomaly evolving processes retrieved from remote sensing products (GDSSTAEP V1.0)
<p> The GDSSTAEP includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively. </p> <p> </p>
Daily ocean bottom pressure anomalies 2007-2009 from global numerical models
<p>Data supplement to: Schindelegger, M., Harker, A. A., Ponte, R. M., Dobslaw, H., & Salstein, D. A. (2021). Convergence of daily GRACE solutions and models of submonthly ocean bottom pressure variability. <em>Journal of Geophysical Research: Oceans</em>, 126, e2020JC017031. <a href="https://doi.org/10.1029/2020JC017031">https://doi.org/10.1029/2020JC017031</a></p> <p><strong>Contents:</strong></p> <p>Daily ocean bottom pressure anomalies over 2007-2009 obtained from two global forward simulations:</p> <ol> <li><strong>DEBOT</strong> (<em>David Einspigel Barotropic Ocean Tide Model</em>): 1/3° horizontal grid spacing, single-layer model</li> <li><strong>MITgcm LLC270</strong> (<em>Massachusetts Institute of Technology general circulation model, Lat-Lon-Cap 270</em>): nominal 1/3° horizontal grid spacing, 50 vertical layers</li> </ol> <p>Common specifications:</p> <ul> <li>Yearly files (<em>yyyy</em>) for each model run (<em>DEBOT_OBP_n180_yyyy.nc</em>, <em>LLC270_OBP_n180_yyyy.nc</em>)</li> <li>Temporal mean 2007-2009 reduced</li> <li>Daily fields centered at 12 UTC</li> <li>Units: cm of equivalent water height</li> <li>Data given on regular 1° grid</li> <li>Synthesized from spherical harmonic expansion truncated at degree 180 (<em>n180</em>)</li> <li>Degree 1 terms: included</li> <li>Static atmospheric contribution to bottom pressure: included</li> <li>Atmospheric forcing: ERA-Interim (6-hourly)</li> </ul> <p> </p> <p>Contact: M. Schindelegger (schindelegger@igg.uni-bonn.de)</p>
Precipitation and Temperature Anomalies from MERRA-2 dataset
<p>Anomalies in Precipitation and Temperature for tiles around the globe.</p> <p>Data derived from the reanalysis MERRA-2 project [1]. The dataset covers the period from 1980 to 2018, between 80ºN and 80ºS. The spatial resolution is 1.0º x 1.0º resulting in 45,792 tiles and a time resolution of 7 days (by averaging the values over each week). Precipitation time-series were re-scaled by applying a logarithmic function to all values.</p> <p>To discount seasonality effects, we averaged temperature and precipitation values for each of the 365 calendar days. We considered the interval from 1 January 1980 through 28 February 2018 as the climatic period for which the long-term averages were computed. The anomalies are then obtained by subtracting for each day the respective average temperature or precipitation from the climatic period (for example, the 1 January 1998 anomaly is computed as the value for that day minus the average of all January 1 values between 1980 and 2018).</p> <p>To process the data, use the code available in:</p> <p><a href="https://github.com/filipinascimento/teleconnectionsgranger/">https://github.com/filipinascimento/teleconnectionsgranger/</a></p> <p><a href="http://arxiv.org/abs/2012.03848">http://arxiv.org/abs/2012.03848</a></p> <p>[1] A. Molod, L. Takacs, M. Suarez, and J. Bacmeister, “Development of the geos-5 atmospheric general circulation model: evolution from merra to merra2,” Geoscientific Model Development 8, 1339–1356 (2015).</p> <p> </p> <p> </p>
Dataset for the paper "Historical model biases in monthly high temperature anomalies indicate under-projection of future temperature extremes"
<div> <div>This repository holds data and scripts related to the revision of the paper entitled: <span>"Historical model biases in monthly high temperature anomalies indicate under-projection of future temperature extremes" </span>by Lei Duan, Lyssa M. Freese, Govindasamy Bala, and Ken Caldeira. <span>The paper is currently submitted for peer review. </span>Any questions regarding the data and paper could be sent to the corresponding author: Lei Duan (leiduan@carnegiescience.edu). </div> </div>
Geospatial Dataset of GNSS Anomalies and Political Violence Events
<p><strong>Geospatial Dataset of GNSS Anomalies and Political Violence Events</strong></p> <p><strong>Overview</strong></p> <p>The <strong>Geospatial Dataset of GNSS Anomalies and Political Violence Events </strong>is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.</p> <p><strong>Dataset Files</strong></p> <p>The dataset consists of three CSV files:</p> <ol> <li><strong>Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv</strong></li> <ul> <li><strong>Description:</strong> Contains all grids and dates that had aircraft traffic during 2023.</li> <li><strong>Number of Records:</strong> 6,777,228</li> <li><strong>Purpose:</strong> Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.</li> </ul> <li><strong>Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv</strong></li> <ul> <li><strong>Description:</strong> A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.</li> <li><strong>Number of Records:</strong> 718,237</li> <li><strong>Purpose:</strong> Focuses on areas and times with GNSS anomalies for targeted analysis.</li> </ul> <li><strong>Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv</strong></li> <ul> <li><strong>Description:</strong> Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.</li> <li><strong>Number of Records:</strong> 25,770</li> <li><strong>Purpose:</strong> Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.</li> </ul> </ol> <p><strong>Data Fields: </strong>Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv</p> <ol> <li><strong>grid_id</strong></li> <ul> <li><strong>Description:</strong> Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li><strong>Format:</strong> String combining latitude and longitude (e.g., -10.0_-36.0).</li> </ul> <li><strong>day</strong></li> <ul> <li><strong>Description:</strong> Date of the recorded data.</li> <li><strong>Format:</strong> YYYY-MM-DD (e.g., 2023-03-28).</li> </ul> <li><strong>geometry</strong></li> <ul> <li><strong>Description:</strong> Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li><strong>Format:</strong> POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).</li> </ul> <li><strong>flights</strong></li> <ul> <li><strong>Description:</strong> Number of aircraft flights that passed through the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 28).</li> </ul> <li><strong>GPS_jumps</strong></li> <ul> <li><strong>Description:</strong> Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 1).</li> </ul> <li><strong>GPS_gaps</strong></li> <ul> <li><strong>Description:</strong> Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 0).</li> </ul> <li><strong>gaps_density</strong></li> <ul> <li><strong>Description:</strong> Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.</li> <li><strong>Format:</strong> Decimal (e.g., 0).</li> </ul> <li><strong>jumps_density</strong></li> <ul> <li><strong>Description:</strong> Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.</li> <li><strong>Format:</strong> Decimal (e.g., 0.035714286).</li> </ul> <li><strong>event_id_cnty</strong></li> <ul> <li><strong>Description:</strong> ACLED event ID corresponding to political violence events in the grid on that day.</li> <li><strong>Format:</strong> String (e.g., BRA69267).</li> </ul> <li><strong>disorder_type</strong></li> <ul> <li><strong>Description:</strong> Type of disorder as classified by ACLED (e.g., "Political violence").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>event_type</strong></li> <ul> <li><strong>Description:</strong> General category of the event according to ACLED (e.g., "Violence against civilians").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>sub_event_type</strong></li> <ul> <li><strong>Description:</strong> Specific subtype of the event as per ACLED classification (e.g., "Attack").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>acled_count</strong></li> <ul> <li><strong>Description:</strong> Number of ACLED events in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 1).</li> </ul> <li><strong>acled_flag</strong></li> <ul> <li><strong>Description:</strong> Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).</li> <li><strong>Format:</strong> Integer (0 or 1).</li> </ul> </ol> <p><strong> </strong></p> <p><strong>Data Fields: </strong>Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv</p> <p>The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below:</p> <ol> <li><strong>grid_id</strong></li> <ul> <li><strong>Description</strong>: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.</li> <li><strong>Format</strong>: String combining latitude and longitude (e.g., -0.5_-79.0).</li> </ul> <li><strong>year_month</strong></li> <ul> <li><strong>Description</strong>: Month and year of the aggregated data.</li> <li><strong>Format</strong>: String in Mon-YY format (e.g., Jan-23).</li> </ul> <li><strong>geometry</strong></li> <ul> <li><strong>Description</strong>: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li><strong>Format</strong>: POLYGON((longitude latitude, ...))<br>(e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).</li> </ul> <li><strong>flights</strong></li> <ul> <li><strong>Description</strong>: Total number of aircraft flights that passed through the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 1230).</li> </ul> <li><strong>GPS_jumps</strong></li> <ul> <li><strong>Description</strong>: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 13).</li> </ul> <li><strong>GPS_gaps</strong></li> <ul> <li><strong>Description</strong>: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.</li> <li><strong>Format</strong>: Integer (e.g., 0).</li> </ul> <li><strong>event_id_cnty</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.</li> <li><strong>Format</strong>: String (e.g., ECU3151;ECU3158;ECU3150).</li> </ul> <li><strong>disorder_type</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.</li> <li><strong>Format</strong>: String.</li> </ul> <li><strong>event_type</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").</li> <li><strong>Format</strong>: String.</li> </ul> <li><strong>sub_event_type</strong></li> </ol> <ul> <li><strong>Description</strong>: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").</li> <li><strong>Format</strong>: String.</li> </ul> <ol> <li><strong>acled_count</strong></li> </ol> <ul> <li><strong>Description</strong>: Total number of ACLED conflict events in the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 2).</li> </ul> <ol> <li><strong>acled_flag</strong></li> </ol> <ul> <li><strong>Description</strong>: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.</li> <li><strong>Format</strong>: Integer (0 or 1).</li> </ul> <ol> <li><strong>gaps_density</strong></li> </ol> <ul> <li><strong>Description</strong>: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.</li> <li><strong>Format</strong>: Decimal (e.g., 0.0).</li> </ul> <ol> <li><strong>jumps_density</strong></li> </ol> <ul> <li><strong>Description</strong>: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.</li> <li><strong>Format</strong>: Decimal (e.g., 0.0106).</li> </ul> <p><strong> </strong></p> <p><strong>Data Sources</strong></p> <ul> <li><strong>GNSS Anomalies Data:</strong></li> <ul> <li>Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.</li> <li>GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).</li> </ul> <li><strong>Political Violence Events Data:</strong></li> <ul> <li>Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.</li> </ul> </ul> <p><strong>Temporal and Spatial Coverage</strong></p> <ul> <li><strong>Temporal Coverage:</strong></li> <ul> <li>From January 1, 2023, to December 31, 2023.</li> <li>Daily records provide temporal granularity for time-series analysis.</li> </ul> <li><strong>Spatial Coverage:</strong></li> <ul> <li>Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li>Each grid cell represents an area on Earth's surface, facilitating spatial analysis.</li> </ul> </ul> <p><strong>Usage and Applications</strong></p> <ul> <li><strong>Security Analysis:</strong></li> <ul> <li>Assess potential correlations between GNSS anomalies and political violence events.</li> <li>Identify regions with increased risk of GNSS spoofing or signal disruption.</li> </ul> <li><strong>Research and Development:</strong></li> <ul> <li>Develop models to predict socio-political events based on GNSS anomalies.</li> <li>Study the impact of political instability on aviation safety.</li> </ul> <li><strong>Policy and Decision Making:</strong></li> <ul> <li>Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.</li> <li>Support conflict analysis and monitoring efforts.</li> </ul> </ul> <p> </p> <p> </p>
Dataset for Investigating Anomalies in Compute Clusters
<p><strong>Abstract</strong></p><p>The dataset was collected for 332 compute nodes throughout May 19 - 23, 2023. May 19 - 22 characterizes normal compute cluster behavior, while May 23 includes an anomalous event. The dataset includes eight CPU, 11 disk, 47 memory, and 22 Slurm metrics. It represents five distinct hardware configurations and contains over one million records, totaling more than 180GB of raw data.</p><p><strong>Background</strong></p><p>Motivated by the goal to develop a digital twin of a compute cluster, the dataset was collected using a Prometheus server (1) scraping the Thomas Jefferson National Accelerator Facility (JLab) batch cluster used to run an assortment of physics analysis and simulation jobs, where analysis workloads leverage data generated from the laboratory's electron accelerator, and simulation workloads generate large amounts of flat data that is then carved to verify amplitudes. Metrics were scraped from the cluster throughout May 19 - 23, 2023. Data from May 19 to May 22 primarily reflected normal system behavior, while May 23, 2023, recorded a notable anomaly. This anomaly was severe enough to necessitate intervention by JLab IT Operations staff.</p><p>The metrics were collected from CPU, disk, memory, and Slurm. Metrics related to CPU, disk, and memory provide insights into the status of individual compute nodes. Furthermore, Slurm metrics collected from the network have the capability to detect anomalies that may propagate to compute nodes executing the same job.</p><p><strong>Usage Notes</strong></p><p>While the data from May 19 - 22 characterizes normal compute cluster behavior, and May 23 includes anomalous observations, the dataset cannot be considered labeled data. The set of nodes and the exact start and end time affected nodes demonstrate abnormal effects are unclear. Thus, the dataset could be used to develop unsupervised machine-learning algorithms to detect anomalous events in a batch cluster.</p><p><a href="https://doi.org/10.48550/arXiv.2311.16129">https://doi.org/10.48550/arXiv.2311.16129</a></p>
The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection
<p><strong>See the official website: <a href="https://autovi.utc.fr">https://autovi.utc.fr</a></strong></p> <p>Modern industrial production lines must be set up with robust defect inspection modules that are able to withstand high product variability. This means that in a context of industrial production, new defects that are not yet known may appear, and must therefore be identified.</p> <p>On industrial production lines, the typology of potential defects is vast (texture, part failure, logical defects, etc.). Inspection systems must therefore be able to detect non-listed defects, i.e. not-yet-observed defects upon the development of the inspection system. To solve this problem, research and development of unsupervised AI algorithms on real-world data is required.</p> <p>Renault Group and the Université de technologie de Compiègne (Roberval and Heudiasyc Laboratories) have jointly developed the <em>Automotive Visual Inspection Dataset (AutoVI)</em>, the purpose of which is to be used as a scientific benchmark to compare and develop advanced unsupervised anomaly detection algorithms under real production conditions. The images were acquired on Renault Group's automotive production lines, in a genuine industrial production line environment, with variations in brightness and lighting on constantly moving components. This dataset is representative of actual data acquisition conditions on automotive production lines.</p> <p>The dataset contains 3950 images, split into 1530 training images and 2420 testing images.</p> <p>The evaluation code can be found at <a href="https://github.com/phcarval/autovi_evaluation_code">https://github.com/phcarval/autovi_evaluation_code</a>.</p> <p><strong>Disclaimer</strong><br>All defects shown were intentionally created on Renault Group's production lines for the purpose of producing this dataset. The images were examined and labeled by Renault Group experts, and all defects were corrected after shooting.</p> <p><strong>License</strong><br>Copyright © 2023-2024 Renault Group</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of the license, visit <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>.</p> <p>For using the data in a way that falls under the commercial use clause of the license, please contact us.</p> <p><strong>Attribution</strong><br>Please use the following for citing the dataset in scientific work:</p> <p>Carvalho, P., Lafou, M., Durupt, A., Leblanc, A., & Grandvalet, Y. (2024). The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection [Dataset]. <a href="https://doi.org/10.5281/zenodo.10459003">https://doi.org/10.5281/zenodo.10459003</a></p> <p><strong>Contact</strong><br>If you have any questions or remarks about this dataset, please contact us at philippe.carvalho@utc.fr, meriem.lafou@renault.com, alexandre.durupt@utc.fr, antoine.leblanc@renault.com, yves.grandvalet@utc.fr.</p> <p><strong>Changelog</strong></p> <ul> <li><em>v1.0.0</em> <ul> <li>Cropped engine_wiring, pipe_clip and pipe_staple images</li> <li>Reduced tank_screw, underbody_pipes and underbody_screw image sizes</li> </ul> </li> <li><em>v0.1.1</em> <ul> <li>Added ground truth segmentation maps</li> <li>Fixed categorization of some images</li> <li>Added new defect categories</li> <li>Removed tube_fastening and kitting_cart</li> <li>Removed duplicates in pipe_clip</li> </ul> </li> </ul>
Risk and anomaly sensor for the steel production [CSS5]
<p> </p> <p> </p> <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>
GRAiCE: Terrestrial water storage anomalies reconstructions
<p>Terrestrial Water Storage (TWS) is the total amount of freshwater stored on and below the Earth’s land surface, including surface water, groundwater, soil moisture, snow, and ice. As a result, TWS is a crucial variable of the global hydrologic cycle, representing an essential indicator of water availability.</p> <p>Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on (GRACE-FO) have been measuring temporal and spatial variations of TWS, namely the Terrrestrial Water Storage Anomalies (TWSA), enabling the monitoring of global hydrological changes over the last two decades. However, the lack of observations prior to 2002 along with the temporal gaps in GRACE/GRACE-FO time series limit our understanding of long-term variations of global freshwater availability.</p> <p>In this study, we use Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks and two sets of predictors to develop four global monthly reconstructions of TWSA from 1984 to 2021 at 0.5º spatial resolution (GR<em>Ai</em>CE). The first set of predictors is given by a combination of five fundamental meteorological forcings and data on vegetation dynamics, whereas the second set of predictors includes the five meteorological forcings only. Specifically, the meteorological predictors are monthly averaged data of total precipitation, snow depth water equivalent, surface net solar radiation, surface air temperature, and surface air relative humidity. We derive data on vegetation dynamics from a long-term reconstruction of solar-induced fluorescence (SIF), which represents a proxy for photosynthesis. Each model is trained with monthly TWSA data from the GRACE JPL mascon dataset. The GR<em>Ai</em>CE dataset accurately reproduces GRACE/GRACE-FO observations at the global scale and across different climatic regions. Moreover, we found that our models predict observed TWSA better than a previous reference reconstruction and produce reliable estimates of the water budget at the river basin scale. Beyond generating long-term continuous TWSA time series, our models allow us to detect and examine TWS changes due to climate variability/change.</p> <p>This repository contains the GR<em>Ai</em>CE dataset and includes four files in netCDF format. The dataset provides monthly TWSA estimates from 1984 to 2021 at a 0.5º spatial resolution. TWSA values are expressed in terms of cm of equivalent water thickness. GRAiCE_LSTM.nc and GRAiCE_BiLSTM.nc files contain TWSA reconstructions obtained from LSTM and BiLSTM models fed with all predictors (i.e., including SIF data), respectively. GRAiCE_LSTMnoSIF.nc and GRAiCE_BiLSTMnoSIF.nc files contain TWSA reconstructions obtained from LSTM and BiLSTM models fed with meteorological forcings only (i.e., without SIF data).</p>
EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) compressed for NumPy
<p>A compressed NumPy version of the <a href="https://www.ngdc.noaa.gov/geomag/emag2.html">EMAG2 (v3)</a> global Earth Magnetic anomaly grid compiled from satellite, ship, and airborne magnetic measurements. The original CSV data was imported, transformed, and saved to a compressed NumPy archive as follows:</p> <pre><code class="language-python">import numpy as np mag_data = np.loadtxt('EMAG2_V3_20170530.csv', delimiter=',', usecols=(2,3,4,5,7)) lon_mask = mag_data[:,0] > 180.0 mag_data[lon_mask,0] -= 360.0 np.savez_compressed('EMAG2_V3_20170530.npz', data=mag_data.astype(np.float32))</code></pre> <p>The NumPy archive (contained in this repository) can be efficiently loaded in Python workflows. It contains the following columns:</p> <ol> <li>Longitude - geographic longitudinal coordinates in decimal degrees (WGS84)</li> <li>Latitude - geographic latitudinal coordinates in decimal degrees (WGS84)</li> <li>SeaLevel - magnetic anomaly value at sea level (nT)</li> <li>UpCont - magnetic anomaly value at continuous 4km altitude (nT)</li> <li>Error - Error estimate (nT)</li> </ol> <p>Code 888 is assigned in certain cells on grid edges where the data source is ambiguous and assigned an error of -888 nT.<br> Code 999 is assigned in cells where no data is reported with the anomaly value assigned 99999 nT and an error of -999 nT.</p> <p><strong>Reference</strong></p> <p>Brian Meyer, Richard Saltus, Arnaud Chulliat (2017): EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) Version 3. National Centers for Environmental Information, NOAA. Model. doi:10.7289/V5H70CVX</p>
STOP-IT REAL-TIME ANOMALY DETECTOR (RTAD)
<p>ICS were designed with automation reliability in mind and most communication technologies were proprietary with no compatibility with TCP/IP Stack. Nowadays most devices have connectivity features inheriting attacks that do not require physical access to plant or systems and organizations are dedicating resources to protect their assets converging physical, logical and IT resources. Task 5.5 objective is to detect known and unknown threats to ICS systems affecting integrated sensors or actuators and SCADA systems by monitoring and learning from its normal behaviour and giving the ICS operators the ability to detect advanced attacks and anomalies before they can cause damage or spread inside the network. Machine Learning techniques and custom attacks were developed for those purposes in order to identify attack patterns and unwanted behaviour before they can interact with sensors/actuators or spoof SCADA reporting messages for removing visibility of the threat to the operator.</p>
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
<h2><strong>CESNET-TimeSeries24: The dataset for network traffic forecasting and anomaly detection</strong></h2> <p>The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods.<br><br>Last but not least, the CESNET-TimeSeries24 dataset provides traffic time series on institutional and IP subnet levels to cover all possible anomaly detection or forecasting scopes. Overall, the time series dataset was created from the 66 billion IP flows that contain 4 trillion packets that carry approximately 3.7 petabytes of data. The CESNET-TimeSeries24 dataset is a complex real-world dataset that will finally bring insights into the evaluation of forecasting models in real-world environments.<br><br></p> <p>Please cite the usage of our dataset as:</p> <blockquote> <p>Koumar, J., Hynek, K., Čejka, T. <em>et al.</em> CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting. <em>Sci Data</em> <strong>12</strong>, 338 (2025). https://doi.org/10.1038/s41597-025-04603-x<br><br>@Article{cesnettimeseries24,<br> author={Koumar, Josef and Hynek, Karel and {\v{C}}ejka, Tom{\'a}{\v{s}} and {\v{S}}i{\v{s}}ka, Pavel},<br> title={CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting},<br> journal={Scientific Data},<br> year={2025},<br> month={Feb},<br> day={26},<br> volume={12},<br> number={1},<br> pages={338},<br> issn={2052-4463},<br> doi={10.1038/s41597-025-04603-x},<br> url={https://doi.org/10.1038/s41597-025-04603-x}<br>}<br><br></p> </blockquote> <p> </p> <h3>Time series</h3> <p>We create evenly spaced time series for each IP address by aggregating IP flow records into time series datapoints. The created datapoints represent the behavior of IP addresses within a defined time window of 10 minutes. The vector of time-series metrics v_{ip, i} describes the IP address ip in the i-th time window. Thus, IP flows for vector v_{ip, i} are captured in time windows starting at t_i and ending at t_{i+1}. The time series are built from these datapoints. </p> <p>Datapoints created by the aggregation of IP flows contain the following time-series metrics:</p> <ul> <li><strong><em>Simple volumetric metrics:</em></strong> the number of IP flows, the number of packets, and the transmitted data size (i.e. number of bytes)</li> <li><strong><em>Unique volumetric metrics:</em></strong> the number of unique destination IP addresses, the number of unique destination Autonomous System Numbers (ASNs), and the number of unique destination transport layer ports. The aggregation of \textit{Unique volumetric metrics} is memory intensive since all unique values must be stored in an array. We used a server with 41 GB of RAM, which was enough for 10-minute aggregation on the ISP network. </li> <li><strong><em>Ratios metrics:</em></strong> the ratio of UDP/TCP packets, the ratio of UDP/TCP transmitted data size, the direction ratio of packets, and the direction ratio of transmitted data size</li> <li><em><strong>Average metrics:</strong></em> the average flow duration, and the average Time To Live (TTL)</li> </ul> <p> </p> <p><strong>Multiple time aggregation: </strong> The original datapoints in the dataset are aggregated by 10 minutes of network traffic. The size of the aggregation interval influences anomaly detection procedures, mainly the training speed of the detection model. However, the 10-minute intervals can be too short for longitudinal anomaly detection methods. Therefore, we added two more aggregation intervals to the datasets--1 hour and 1 day.</p> <p><strong>Time series of institutions:</strong> We identify 283 institutions inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution's data. </p> <p><strong>Time series of institutional subnets:</strong> We identify 548 institution subnets inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution subnet's data. </p> <p> </p> <h3>Data Records</h3> <p>The file hierarchy is described below:</p> <blockquote> <p>cesnet-timeseries24/</p> <p> |- institution_subnets/</p> <p> | |- agg_10_minutes/<id_institution>.csv</p> <p> | |- agg_1_hour/<id_institution>.csv</p> <p> | |- agg_1_day/<id_institution>.csv</p> <p> | |- identifiers.csv</p> <p> |- institutions/</p> <p> | |- agg_10_minutes/<id_institution_subnet>.csv</p> <p> | |- agg_1_hour/<id_institution_subnet>.csv</p> <p> | |- agg_1_day/<id_institution_subnet>.csv</p> <p> | |- identifiers.csv</p> <p> |- ip_addresses_full/</p> <p> | |- agg_10_minutes/<id_ip_folder>/<id_ip>.csv</p> <p> | |- agg_1_hour/<id_ip_folder>/<id_ip>.csv</p> <p> | |- agg_1_day/<id_ip_folder>/<id_ip>.csv</p> <p> | |- identifiers.csv</p> <p> |- ip_addresses_sample/</p> <p> | |- agg_10_minutes/<id_ip>.csv</p> <p> | |- agg_1_hour/<id_ip>.csv</p> <p> | |- agg_1_day/<id_ip>.csv</p> <p> | |- identifiers.csv</p> <p> |- times/</p> <p> | |- times_10_minutes.csv</p> <p> | |- times_1_hour.csv</p> <p> | |- times_1_day.csv</p> <p> |- ids_relationship.csv<br> |- weekends_and_holidays.csv</p> </blockquote> <p>The following list describes time series data fields in CSV files:</p> <ul> <li><strong>id_time: </strong>Unique identifier for each aggregation interval within the time series, used to segment the dataset into specific time periods for analysis.</li> <li><strong>n_flows: </strong>Total number of flows observed in the aggregation interval, indicating the volume of distinct sessions or connections for the IP address.</li> <li><strong>n_packets: </strong>Total number of packets transmitted during the aggregation interval, reflecting the packet-level traffic volume for the IP address.</li> <li><strong>n_bytes: </strong>Total number of bytes transmitted during the aggregation interval, representing the data volume for the IP address.</li> <li><strong>n_dest_ip: </strong>Number of unique destination IP addresses contacted by the IP address during the aggregation interval, showing the diversity of endpoints reached.</li> <li><strong>n_dest_asn: </strong>Number of unique destination Autonomous System Numbers (ASNs) contacted by the IP address during the aggregation interval, indicating the diversity of networks reached.</li> <li><strong>n_dest_port: </strong>Number of unique destination transport layer ports contacted by the IP address during the aggregation interval, representing the variety of services accessed.</li> <li><strong>tcp_udp_ratio_packets: </strong>Ratio of packets sent using TCP versus UDP by the IP address during the aggregation interval, providing insight into the transport protocol usage pattern. This metric belongs to the interval <0, 1> where 1 is when all packets are sent over TCP, and 0 is when all packets are sent over UDP.</li> <li><strong>tcp_udp_ratio_bytes:</strong> Ratio of bytes sent using TCP versus UDP by the IP address during the aggregation interval, highlighting the data volume distribution between protocols. This metric belongs to the interval <0, 1> with same rule as <em>tcp_udp_ratio_packets</em>.</li> <li><strong>dir_ratio_packets: </strong>Ratio of packet directions (inbound versus outbound) for the IP address during the aggregation interval, indicating the balance of traffic flow directions. This metric belongs to the interval <0, 1>, where 1 is when all packets are sent in the outgoing direction from the monitored IP address, and 0 is when all packets are sent in the incoming direction to the monitored IP address.</li> <li><strong>dir_ratio_bytes: </strong>Ratio of byte directions (inbound versus outbound) for the IP address during the aggregation interval, showing the data volume distribution in traffic flows. This metric belongs to the interval <0, 1> with the same rule as <em>dir_ratio_packets</em>.</li> <li><strong>avg_duration: </strong>Average duration of IP flows for the IP address during the aggregation interval, measuring the typical session length.</li> <li><strong>avg_ttl: </strong>Average Time To Live (TTL) of IP flows for the IP address during the aggregation interval, providing insight into the lifespan of packets.</li> </ul> <p>Moreover, the time series created by re-aggregation contains following time series metrics instead of <strong>n_dest_ip</strong>, <strong>n_dest_asn</strong>, and <strong>n_dest_port</strong>:</p> <ul> <li><strong>sum_n_dest_ip: </strong>Sum of numbers of unique destination IP addresses.</li> <li><strong>avg_n_dest_ip: </strong>The average number of unique destination IP addresses.</li> <li><strong>std_n_dest_ip: </strong>Standard deviation of numbers of unique destination IP addresses.</li> <li><strong>sum_n_dest_asn: </strong>Sum of numbers of unique destination ASNs.</li> <li><strong>avg_n_dest_asn: </strong>The average number of unique destination ASNs.</li> <li><strong>std_n_dest_asn: </strong>Standard deviation of numbers of unique destination ASNs)</li> <li><strong>sum_n_dest_port: </strong>Sum of numbers of unique destination transport layer ports.</li> <li><strong>avg_n_dest_port: </strong> The average number of unique destination transport layer ports.</li> <li><strong>std_n_dest_port: </strong>Standard deviation of numbers of unique destination transport layer ports.</li> </ul> <p> </p> <p>Moreover, files <em>identifiers.csv</em> in each dataset type contain IDs of time series that are present in the dataset. Furthermore, the <em>ids_relationship.csv</em> file contains a relationship between IP addresses, Institutions, and institution subnets. The <em>weekends_and_holidays.csv</em> contains information about the non-working days in the Czech Republic.</p>
A Nu Supersymmetric Anomaly-free Atlas: anomaly-free, flavour-dependent U(1) charge assignments for the Minimally Supersymmetric Standard Model plus three Standard Model-singlet superfields
<p>We present lists of anomaly-free charge assignments up to a maximum magnitude charge Qmax=10 for the chiral fermionic content of the MSSM plus 3 right-handed neutrinos. </p> <p>Due to the large number of solutions, we compress the list into the file MSSMnuRcharges_Qmax10.gz. Please note that the unzipped file is approximately 130GB in size. We additionally include a smaller file, MSSMnuRcharges_Qmax4, containing the subset of anomaly-free charge assignments up to a maximum magnitude charge Qmax=4.</p> <p>The files searchU1MSSM.cpp and searchU1MSSM.h contain C++ files (in the 2014 standard) to produce the solutions. runsearch.sh is a bash script that compiles the programs and then runs it for a sample set of inputs.</p> <p>We provide Mathematica notebooks Analytic_solution_generator.nb and Analytic_Checks.nb which respectively provide the parametrisation of the analytic solution and checks thereof.</p> <p>The files beginning 'filter' contain example programs that read in each line in the solution list, apply a filter and print only the solutions satisfying the conditions of that filter. runfilter.sh is a bash script that compiles the filters and then runs a single filter as an example.</p> <p>These data and programs are based on this paper: https://arxiv.org/abs/2107.07926.</p>
Tri-hourly dataset of wind and wave anomalies of the GFS and WAVEWATCH III models in the entire tropic region (TROPWA).
<p>This dataset contains the anomalies of the total height and peak period of the waves and of the zonal and meridional components of the wind at 10 m above the sea surface obtained from the outputs of the GFS and WAVEWATCH III coupled global models in the entire tropical region (180°W to 178.75°E longitude/30°S to 30°N latitude), with a spatial resolution of 1.25°x1°. It is made up of two files in NetCDF format, where the data for wind anomalies (speed module and its zonal and meridional components) and wave anomalies (total wave height and peak period) are contained separately. This dataset was created in order to study all types of tropical storms, cold fronts and other physical processes that influence significant changes in wave parameters, as well as for the design and construction of coastal engineering works. The authors thanks to Tropical Atlantic Interdisciplinary Laboratory on physical, biogeochemical, ecological and human dynamics (IJL TAPIOCA).</p>
Tropical Atmospheric Anomalies on the Surface (TROPAAS)
<p>This dataset contains the monthly, daily and 6-hour anomalies of the wind speed at 10 m, air temperature at 10 m, incident long-wave radiation, incident solar radiation, atmospheric pressure at sea level, the specific humidity at 10 m and precipitation in the entire tropical region (180°W – 178.125°E / 31.4281°S - 31.4281°N), calculated from the CORE v2 dataset (Coordinated Ocean-ice Reference Experiments) experiment with the correction of CIAF v2 (Corrected Inter-Annual Forcing). Covers the period from 1948 to 2009.</p> <p> </p> <p>There are 3 compressed files, each of which contains 6 3D grids in NetCDF files format with the following name description:</p> <p> </p> <p>anom_<frequency>_<variablename>_TROPAAS.nc</p> <p> </p> <p><frequency>:</p> <p>mm – Monthly means.</p> <p>dm – Daily means.</p> <p>6hrs – Each 6-hours.</p> <p> </p> <p><variablename>:</p> <p>precip – Precipitation.</p> <p>q - Specific humidity at 10 m.</p> <p>rad - Incident long-wave radiation and incident solar radiation.</p> <p>Slp - Pressure at sea level.</p> <p>T - Temperature at 10 m.</p> <p>winds - Zonal and meridional components and the module of the wind speed at 10 m.</p> <p> </p> <p>All the grids are made up of 192 x 34 nodes, with a spatial resolution of 1.875 x 1.904733 degrees.</p> <p> </p>
Global Ocean Heat Content Anomalies based on Argo data
<p><strong>NOTE for users: please use the latest version of the product at https://zenodo.org/doi/10.5281/zenodo.10182972. </strong>Ocean Heat Content Anomalies (OHCA) are calculated (during 2005-2022) subtracting the mean over the period 2005-2021 from the monthly time series. Yearly OHCA time series are then calculated. OHC fields are mapped using locally stationary Gaussian processes with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In the present version, mapping is done in latitude and longitude with monthly subsets of data (a future version will add time to the mapping). Mapping is done separately for different vertical sections. Different vertical sections are combined to estimate: 1. Global OHC timeseries (e.g., for level 0-2000m: GCOS_0000_2000_OHCA_J_m2_oc, for OHC in J/m2; GCOS_0000_2000_OHCA_ZJ, for OHC in ZJ; the attribute “GCOS_area” is included for both variable types in the netcdf file and it tells the corresponding surface area); 2. Volume averaged temperature anomaly (global) timeseries (e.g., for level 0-2000m: GCOS_0000_2000_vol_ave_temp_anom, in degC; the attribute “GCOS_volume'' is included for this variable type in the netcdf file and it tells the corresponding volume). Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included.</p>
ScienceDex guides
Understand access before you commit
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.