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625 results for “Anomaly”
Control covariance between temperature and radiative heating anomaly composites
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Mediterranean Sea Climatic Indices - Differences of two 30-years averages of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_Climashift_30yrs.tar.gz" contains : a) an ascii file named "inventory_Climashift_30yrs.txt" which lists all the available indices, and b) a directory named "Climashift_30yrs" with the indices under the following structure:</p> <ul> <li>Climashift_30yrs/Anomalies/Annual/Variable_decade2_decade1_allmonths_z1_z2.nc</li> <li>Climashift_30yrs/Anomalies/Seasonal/ Variable_decade2_decade1_season_z1_z2.nc</li> <li>Climashift_30yrs/Vertical_Averages/Annual/Variable_decade2_decade1_allmonths_z1z2.nc</li> <li>Climashift_30yrs/Vertical_Averages/Seasonal/Variable_decade2_decade1_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li> Tanomclimashift: Temperature anomaly difference between decade2 and decade1</li> <li> Sanomclimashift: Temperature anomaly difference between decade2 and decade1</li> <li> Tvavgclimashift: Vertically averaged temperature anomaly difference between decade2 and decade1</li> <li> Svavgclimashift: Vertically averaged salinity anomaly difference between decade2 and decade1</li> <li> OHCclimashift: Ocean Heat Content anomaly difference between decade2 and decade1</li> <li> OSCclimashift: Ocean Salt Content anomaly difference between decade2 and decade1</li> </ul> <p> <br> Other naming:</p> <ul> <li>decade1: stands for 19501979</li> <li>decade2: stands for 19802015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1z2: vertical layer between z1 and z2 depths</li> <li>z1, z2: standard depth levels</li> </ul> <p>Example:</p> <ul> <li>Sanomclimashift_19802015_19501979_0112_5_4000.nc, is the annual salinity anomaly difference between 1980-2015 and 1950-1970 from 5 to 4000 m.</li> </ul> <p><br> </p>
Mediterranean Sea Climatic Indices - T/S Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_Anomalies.tar.gz" contains : a) an ascii file named "inventory_Anomalies.txt" which lists all the available indices, and b) a directory named "Anomalies" with the indices under the following structure:</p> <ul> <li>Anomalies/Annual_Decadal/Variable_decade_allmonths_z1_z2.nc</li> <li>Anomalies/Seasonal_Decadal/Variable_decade_season_z1_z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tanom: Temperature anomaly</li> <li>Sanom: Salinity anomaly</li> </ul> <p>Other naming:</p> <ul> <li>decade: 19501959 to 20062015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1, z2: standard depth levels</li> </ul> <p>Example:</p> <ul> <li>Sanom_19501959_0112_5_4000.nc, is the Salinity anomaly for the decade 19501959 from 5 to 4000 m</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Mediterranean Sea Climatic Indices - Linear trends of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_LinearTrends.tar.gz" contains : a) an ascii file named "inventory_LinearTrends.txt" which lists all the available indices, and b) a directory named "LinearTrends" with the indices under the following structure:</p> <ul> <li>LinearTrends/Annual/Variable_period_allmonths_z1z2.nc</li> <li>LinearTrends/Seasonal/ Variable_period_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tlineartrend: Vertically averaged temperature anomaly linear trend</li> <li>Slineartrend: Vertically averaged salinity anomaly linear trend</li> <li>OHClineartrends: Ocean Heat Content anomaly linear trend</li> <li>OSClineartrends: Ocean Salt Content anomaly linear trend</li> </ul> <p> </p> <p>Other naming:</p> <ul> <li>period: stands for 19502015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1z2: vertical layer between z1 and z2 depths</li> </ul> <p>Example:</p> <ul> <li>Tlineartrend_19502015_0112_6004000.nc, is the annual vertically temperature anomaly linear trend, for the period 19502015, between 600 and 4000 m</li> </ul>
Mediterranean Sea Climatic Indices - Time Series of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_TimesSeries.tar.gz" contains a directory named "TimeSeries" with the following 5 indices:</p> <ul> <li>TimeSeries_Annual.nc: annual time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0103.nc: winter time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0406.nc: spring time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0709.nc: summer time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_1012.nc: autumn time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> </ul>
Supporting Data : Signal outages of GPS from an anomaly crest location
<p>This zipped file contains satellite specific raw phase data from GPS L1 C/A, L2C and L5 signals for vernal equinox of 2014. It also contains the sample datasheet for case study and overall statistical data.</p>
Data Tables and figures for "Quantification of 3D thermal anomalies from surface observations of an orogenic geothermal system (Grimsel Pass, Swiss Alps)"
<p>Tables and figures containing the data used in the manuscript called</p> <p><strong>"Quantification of 3D thermal anomalies from surface observations of an orogenic geothermal system (Grimsel Pass, Swiss Alps)", </strong></p> <p>which was submitted to JGR:Solid Earth</p>
An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation
<p><strong>Abstract: </strong></p> <p>Swarms of drones offer an increased sensing aperture, and having them mimic behaviors of natural swarms enhances sampling by adapting the aperture to local conditions. We demonstrate that such an approach makes detecting and tracking heavily occluded targets practically feasible. While object classification applied to conventional aerial images generalizes poorly the randomness of occlusion and is therefore inefficient even under lightly occluded conditions, anomaly detection applied to synthetic aperture integral images is robust for dense vegetation, such as forests, and is independent of pre-trained classes. Our autonomous swarm searches the environment for occurrences of the unknown or unexpected, tracking them while continuously adapting its sampling pattern to optimize for local viewing conditions. We achieved an average positional accuracy of 0.39 m with an average precision of 93.2% and an average recall of 95.9%. Here, adapted particle swarm optimization considers detection confidences and predicted target appearance. We show that sensor noise can effectively be included in the synthetic aperture image integration process, removing the need for a computationally costly optimization of high-dimensional parameter spaces. Finally, we present a complete hard- and software framework that supports low-latency transmission and fast processing of extensive video and telemetry data.</p>
Late Triassic carbon isotope anomalies in the Canadian Cordillera: Paleoenvironmental disturbances associated with the Norian/Rhaetian boundary and end-Triassic mass extinction event
<p>Appendices for "Late Triassic carbon isotope anomalies in the Canadian Cordillera: Paleoenvironmental disturbances associated with the Norian/Rhaetian boundary and end-Triassic mass extinction event".</p>
Replication Package: Anomaly detection via runtime monitoring data for structural equation modeling
<p>This replication package contains the following information:</p> <ul> <li><strong>Data extraction from literature & interviews: </strong><em>Generation Structural & Measurement Model via literature and interviews.xlsx</em> - here you can find the mapping of the extracted phrases to inductively summarise information regarding the structural and measurement models.</li> <li><strong>Dataset</strong> of runtime monitoring data extracted from TrainTicket via EvoMaster: <br> <ul> <li><em>TrainTicket faults classification.xlxs:</em> Describes the datasets and their faults, in which microservice the fault is injected for better explainability of the obtained results</li> <li><em>IndicatorDescriptionbasedonAnomalyDetectionToolsInterviews.xlsx:</em> description and mapping of selected indicators to the defined parameters from <a href="https://arxiv.org/abs/2408.07816" target="_blank" rel="noopener">previous work </a></li> <li>Unfortunately, the size of the datasets generated via EvoMaster and their injected faults are too big to upload here, thus, they will be available here: <a href="https://uibkacat-my.sharepoint.com/:f:/g/personal/monika_steidl_uibk_ac_at/EjLMt8SYWwtJtp2YuSaqavcBKJoCQ3b5H_l_OY0ifbVRCA?e=fatKyD" target="_blank" rel="noopener">Datasets with injected anomalies</a><br> <ul> <li>the error description can be found <a href="https://github.com/FudanSELab/train-ticket/wiki/Fault-Description" target="_blank" rel="noopener">here</a></li> <li>the datasets are named ts-error-<em>indicatorOfError</em>-reset.zip because the databases are getting reset so that no anomalies are introduced with wrong database entries</li> </ul> </li> </ul> </li> <li><strong>Code</strong> for handling and transforming data to extract indicators describing the whole system's and microservices' behavior from the collected runtime monitoring data collected from TrainTicket:<br> <ul> <li><a href="https://github.com/moniSt13/ConTest-Parsing" target="_blank" rel="noopener">link to the Github repository</a></li> </ul> </li> <li><strong>reports</strong> regarding the established PLS-SEM model using previously handled and transformed runtime monitoring data. Please be aware that opening the reports can leas to out of memory due to their size: <ul> <li><em>Assessment of Measurement Model: MeasurementModel_TrainTicket_erorcleaned.zip & MeasurementModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em> </li> <li><em>Assessment of Structural Model: StructuralModel_TrainTicket_errorcleaned.zip & StructuralModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em></li> </ul> </li> </ul> <p><br><br>---------------------------------------</p> <p><em>Future work </em>not elaborated in the associated paper due to space restrictions:</p> <ul> <li><strong>reports regarding F5 error</strong>: PLS-SEM model results without interpretation and further mediating effects between microservices included: F5_error.zip</li> </ul> <p> </p> <p> </p> <p> </p>
Anonymised Phone Call Dataset for Anomaly Detection
<p>The dataset provides anonymized information related to phone calls, including the following details:</p> <p>1. Origin Numbers (A-Numbers)<br>2. Destination Numbers (B-Numbers)<br>3. Timestamp of the call<br>4. Call Result, indicating whether the call was blacklisted (coded as 001) or not (coded as 000)</p> <p>The dataset is divided into two subsets with the following characteristics:</p> <p>Dataset 1<br>- Collection Period: 24th July 2018 to 21st October 2018<br>- Duration: 89 days<br>- Total Records: 83,366,367 examples<br>- Unique A-Numbers: 9,006,011<br>- Unique B-Numbers: 2,387,932</p> <p>Dataset 2<br>- Collection Period: 1st June 2019 to 30th June 2019<br>- Duration: 29 days<br>- Total Records: 32,879,670 examples<br>- Unique A-Numbers: 3,217,069<br>- Unique B-Numbers: 1,380,235</p>
Dataset for "Lithospheric structure above the Northern Appalachian Anomaly: Initial results from the NEST array"
<p>The preprocessed seismic traces used in the receiver function analysis and resulting negative velocity gradient (NVG) depths. This dataset was used in, "Lithospheric structure above the Northern Appalachian Anomaly: Initial results from the NEST array" by Kimberly Espinal, Maureen Long, Paul Karabinos, and James R. Bourke. The manuscript will soon be available.</p> <p>The receiver functions were processed using a version of the software by Jeffrey Park and Vadim Levin (https://seiscode.iris.washington.edu/projects/rfsyn). </p>
Enhanced Anomalies Detection
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Radiative acceleration calculation methods and abundance anomalies in Am stars
<p>This archive contains stellar structures used to create the figures of surface abundance evolution with time of the paper entitled "Radiative acceleration calculation methods and abundance anomalies in Am stars" to be published in Astronomy & Astrophysics.</p> <p>The first directory level refers to the dataset used to compute the Rosseland mean opacities (OP = Opacity Project for instance), the second to the stellar mass (in solar mass unit), and the third to the g_rad calculation method and the prescriptions used for macroscopic transport processes (e.g. SVP2004_RMT for g_rad computed with the 2004 version of the SVP approximation and the RMT turbulence model).</p> <p>In each of these directories, the stellar structure file name contains the age (e.g. "structure.00215.78.gz" for an age of 215.78 Myr). </p> <p>The first line of each stellar structure file shows the meaning of each field. The columns are separated with tabulations to allow easy an importation in spreadsheets. All quantities are expressed in cgs units, the abundances in mass fractions.</p>
Data for paper entitled "Origin of the high conductivity anomalies in the mid-lower crust of the Tibetan Plateau: Dehydration melting of garnet amphibolites"
<p>The data base includes the original data of the submitted paper to JGR:SE as listed below:</p> <p>(1) R1040</p> <p>(2) R1055</p> <p>(3) R1056</p> <p>(4) R1094</p> <p>(5) R1105</p> <p>(6) R1106</p> <p>(7) Data for figure 7</p>
Dataset of "Impactor material records the ancient lunar magnetic field in antipodal anomalies"
<p>This dataset contains input files for iSALE-3D for the paper "Impactor material records the ancient lunar magnetic field in antipodal anomalies" by S. Wakita et al.<br> <br> Please note that usage of the iSALE-3D code is restricted to those who have contributed to the development of iSALE-2D, and iSALE-2D is distributed on a case-by-case basis to academic users in the impact community. It requires a registration from the iSALE webpage (http://www.isale-code.de), and usage of iSALE-2D and computational requirements are also shown there. Please also note that pySALEPlot in the current stable release of iSALE-2D (Dellen) would not work for the data from iSALE-3D.</p>
Anomaly Detection Algorithm Performance
<p>Anomaly detection algorithms performance metrics: AUC and Average precision; two sets of 298 + 13 algorithms; 9315 datasets.</p>
FIGURE 1 in Unnoticed anomaly in the holotype of Grallaria rufocinerea (Myrmotheridae) deprives romeroana Hernández-Camacho & Rodríguez, 1979 of diagnosability
FIGURE 1. Approximate geographic distribution of Grallaria rufocinerea. Stars represent the type localities of rufocinerea (Sclater & Salvin 1879) and romeroana (Hernández-Camacho & Rodríguez-M. 1979). All adult individuals of this species along its range look as the individual in the inset photograph, from the northern part of its range, in Río Blanco, Caldas (gray dot). Note the solid rufous brown throat that makes a complete hood, and the faint scaling below. Photo by Daniel Uribe Birding Tours Colombia.
FIGURE 2 in Unnoticed anomaly in the holotype of Grallaria rufocinerea (Myrmotheridae) deprives romeroana Hernández-Camacho & Rodríguez, 1979 of diagnosability
FIGURE 2. Four specimens of Grallaria rufocinerea, all from the Central Cordillera of Colombia, and arranged north (top) to south (bottom). A: Holotype of G. rufocinerea (Sclater & Salvin 1879) from Santa Elena, Antioquia (NHMUK 1889.9.20.618). B: An adult male from Páramo de Sonsón, Antioquia (USNM 436486). C: Holotype of G. rufocinerea romeroana (HernándezCamacho & Rodríguez-M. 1979) from Río Bedón, PuracéNational Park, Huila (IAvH-A 525). D: An adult female from Santa Helena at La Cruz, Nariño (MHN-UC AV004534). Note the disarranged throat plumage of the rufocinerea type (A) that makes it look as mottled gray. For this same reason, the erected feathers in the type of romeroana (C) and in the female from Nariño (D) make the upper throat look grayish too. The soft scaling pattern on the belly is not exclusive to romeroana, as it occurs both Antioquia rufocinerea specimens.
FIGURE 3 in Unnoticed anomaly in the holotype of Grallaria rufocinerea (Myrmotheridae) deprives romeroana Hernández-Camacho & Rodríguez, 1979 of diagnosability
FIGURE 3. The plate of Grallaria rufocinerea based on the type specimen (NHM 1889.9.20.618); the illustration, taken from Sclater (1890), is by Joseph Smit. Note that the center of throat is shown as gray from the base of the bill to the breast, in contrast to the characteristic solid rufous brown throat that forms the rufous brown hood in this species. This is likely an artifact of specimen preparation (see text). Extracted from the Biodiversity Heritage Library (https://www.biodiversitylibrary.org/item/34360).
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International Brain Laboratory public data
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OpenNeuro
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