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625 results for “Anomaly”
Fatiando a Terra Data: Britain - Airborne total-field magnetic anomaly
<p>This is a digitized version of an airborne magnetic survey of Britain. Data are sampled where flight lines crossed contours on the archive maps. Contains only the total field magnetic anomaly, not the magnetic field intensity measurements or corrections.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's mean for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Datum was changed to WGS8; Year was separated from the survey name; Some fields were dropped; Exported to compressed CSV format.</p> <p><strong>Source: </strong><a href="https://www.bgs.ac.uk/datasets/gb-aeromagnetic-survey/">British Geological Survey</a></p> <p><strong>Source license: </strong><a href="https://www.bgs.ac.uk/bgs-intellectual-property-rights/open-government-licence/">Open Government Licence</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/britain-magnetic">https://github.com/fatiando-data/britain-magnetic</a></p> <p>Contains British Geological Survey materials © UKRI 2021.</p>
Fatiando a Terra Data: Osborne Mine, Australia - Airborne total-field magnetic anomaly
<p>This is a section of a survey acquired in 1990 by the Queensland Government, Australia. The data are good quality with approximately 80 m terrain clearance and 200 m line spacing. The anomalies are very visible and present interesting processing and modelling challenges, as well as plenty of literature about their geology.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Change the horizontal datum from GDA94 to WGS84. Convert terrain clearance to flight height using an SRTM grid. Keep only the coordinates, AWAGS leveled magnetic anomaly, and flight line ID. Cut to a smaller region containing only the 2 anomalies of interest.</p> <p><strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. <a href="http://pid.geoscience.gov.au/dataset/ga/142419">http://pid.geoscience.gov.au/dataset/ga/142419</a></p> <p><strong>Source license: </strong><a href="http://pid.geoscience.gov.au/dataset/ga/142419">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/osborne-magnetic">https://github.com/fatiando-data/osborne-magnetic</a></p>
Dataset for: An experimental comparison of anomaly detection methods for collaborative robot manipulators
<p>The dataset contains data recordings from a UR5e robot during normal and anomalous operation and is recorded to support the authors Master thesis project and the associated Paper: <em>"An Experimental Comparison of Anomaly Detection Methods for Collaborative Robot Manipulators" </em>(inProceeding).</p> <p>An in-depth description of the dataset can be found in the pdf uploaded with the dataset and an example of a data loader is also provided.</p>
Dataset for Anomaly Detection in a Production Wireless Mesh Community Network
<p>CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24). </p> <p>Live mesh network monitoring link: <a href="http://dsg.ac.upc.edu/qmpsu">http://dsg.ac.upc.edu/qmpsu</a></p> <p>The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00". The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture.</p> <p>A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid.</p> <p><br> Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:<br> feature.rate are columns computed from feature as<br> feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])<br> feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted<br> where n is the number of samples</p> <p><strong>features</strong><br> - processes number of processes<br> - loadavg.m1 1 minute load average<br> - softirq.rate servicing softirqs<br> - iowait.rate waiting for I/O to complete<br> - intr.rate <br> - system.rate processes executing in kernel mode<br> - idle.rate twiddling thumbs<br> - user.rate normal processes executing in user mode<br> - irq.rate servicing interrupts<br> - ctxt.rate total number of context switches across all CPUs<br> - nice.rate niced processes executing in user mode<br> - nr_slab_unreclaimable The part of the Slab that can't be reclaimed under memory pressure<br> - nr_anon_pages anonymous memory pages<br> - swap_cache Memory that once was swapped out, is swapped back in but still also is in the swapfile<br> - page_tables Memory used to map between virtual and physical memory addresses<br> - swap <br> - eth.txe.rate tx errors over all ethernet interfaces<br> - eth.rxe.rate rx errors over all ethernet interfaces<br> - eth.txb.rate tx bytes over all ethernet interfaces<br> - eth.rxb.rate rx bytes over all ethernet interfaces<br> - eth.txp.rate tx packets over all ethernet interfaces<br> - eth.rxp.rate rx packets over all ethernet interfaces<br> - wifi.txe.rate tx errors over all wireless interfaces<br> - wifi.rxe.rate rx errors over all wireless interfaces<br> - wifi.txb.rate tx bytes over all wireless interfaces<br> - wifi.rxb.rate rx bytes over all wireless interfaces<br> - wifi.txp.rate tx packets over all wireless interfaces<br> - wifi.rxp.rate rx packets over all wireless interfaces<br> - txb.rate tx bytes over all ethernet and wifi interfaces<br> - txp.rate tx packets over all ethernet and wifi interfaces<br> - rxb.rate rx bytes over all ethernet and wifi interfaces<br> - rxp.rate rx packets over all ethernet and wifi interfaces<br> - sum.xb.rate tx+rx bytes over all ethernet and wifi interfaces<br> - sum.xp.rate tx+rx packets over all ethernet and wifi interfaces<br> - diff.xb.rate tx-rx bytes over all ethernet and wifi interfaces<br> - diff.xp.rate tx-rx packets over all ethernet and wifi interfaces</p>
Foot anomalies in four post-medieval, Dutch populations (17th-19th century)
<p>This dataset was compiled for the master's thesis "Familiar Feet. Kinship analysis using foot anomalies in the cemetery of Middenbeemster (Netherlands, 17th to 19th century)" at Leiden University (Netherlands) by the author. The thesis was submitted on June 15th, 2021 (<a href="https://hdl.handle.net/1887/3204807">https://hdl.handle.net/1887/3204807</a>). The data was collected in the period from January to May 2021 at the Laboratory of Human Osteoarchaeology at Leiden University.</p> <p>It includes the data of 380 adult individuals from four different post-medieval, Dutch populations: the rural population of Middenbeemster (MB) (Hakvoort 2013) and the urban populations of Eindhoven (Catharinakerk) (EH) (Arts 2013), Zwolle (Broerenkerk) (ZW) (Clevis and Constandse-Westermann 1992) and Arnhem (Eusebiuskerk) (AR) (Baetsen et al. 2018). For every individual, an estimate of sex and age-at-death was collected. If a DNA analysis was performed, then this result was used instead of an estimation. The sex estimations of the individuals were based on pelvic and cranial morphology (Bainbridge & Genoves Tarazaga, 1956; Bass, 1987; Buikstra & Ubelaker, 1997; Maat & Mastwijk, 2009; McCormick et al., 1991; Phenice, 1969; Stewart, 1979; Steyn & Işcan, 1999; Workshop for European Anthropologists (WEA), 1980) and were supported by archival data. For the Eindhoven collection, the DNA-determined sex estimations took precedence over the estimations by traditional bioarchaeological techniques (Baetsen & Weterings-Korthorst, 2013). The used abbreviations are M = male, PM = probable male, I = indeterminate, PF = probable female, F = female. <br> Age-at-death estimations were made according to traditional bioarchaeological techniques (Brooks & Suchey, 1990; Buckberry & Chamberlain, 2002; İşcan et al., 1984, 1985; Lovejoy et al., 1985; Maat, 2001; Meindle & Lovejoy, 1985; Todd, 1920). The used abbreviations are EYA = early young adult, LYA = late young adult, MA = middle adult, OA = old adult.</p> <p>The foot anomalies included are accessory navicular (AccessNav), brachydactyly D (BrachyD), brachydactyly of the first metatarsal (BrachyMT1), brachydactyly of the fourth metatarsal (BrachyMT4), brachydactyly of the first proximal phalanx (BrachyPP1), calcaneocuboid coalition (CalcCubCoal), calcaneonavicular coalition (CalcNavCoal), coalition between the intermediate and lateral cuneiform (CF2CF3Coal), coalition between the lateral cuneiform and the third metatarsal (CF3MT3Coal), talocalcaneal coalition (TaloCalcCoal), talonavicular coalition (TaloNavCoal) and os intermetatarseum with the medial cuneiform (CF1Intermet), the first metatarsal (MT1Intermet) and/or the second metatarsal (MT2Intermet) involved. The scoring itself was performed without prior knowledge of skeletal data like sex and age to avoid observer bias. The trait could be marked absent (0), present (1), indiscernible if the bone was damaged (6), or missing if the bone was not present (9) for each separate foot (left (L) or right (R) side). Tarsal coalitions were scored if at least one of the two bones could be marked “absent/present”. In the remarks, some of the found lesions are described in more detail.</p> <p>For some of the individuals, there is archival data available. This is indicated with 'Yes' or 'No' in the 'Archive data?' column. For the Middenbeemster population, the location of the individual graves was determined from the grave polygons in the excavation data in QGIS by calculating the centroids of these polygons and resulted in a single pair of (x, y) coordinates.</p> <p>This dataset was used to identify probable genetic relatives within the Middenbeemster skeeltal collection through developmental foot anomalies and to analyse the spatial structure of the Middenbeemster cemetery in the context of intracemetery kinship relations. The Middenbeemster trait frequencies for these anomalies were compared to those of a reference sample of the post-medieval Dutch population (consisting of individuals from the Dutch post-medieval collections of Arnhem, Eindhoven, Zwolle). A hypothetical kinship group could be identified when the trait frequencies of the Middenbeemster sample were considerably higher than those in the reference sample. Other sources had only limited validation value in relation to the hypothesis. Visual examination and spatial statistics of the distribution of the hypothetical kinship group revealed a possible patrilineally structured cemetery, although this is based on a small sample. By putting the observed trait frequencies in a broader context, the data suggested a rather high inter-relatedness of the Middenbeemster community. It also exposed the need for a better understanding of the used traits and perhaps a different approach to kinship analysis (due to necessarily large time investment in contrast to limited results). In conclusion, this study gave an insight into the social structure of post-medieval Dutch communities. Future improvements to kinship analysis may not only be beneficial for bioarchaeology, but also for other fields such as forensic anthropology.<br> For more information on this research, see the thesis (<a href="https://hdl.handle.net/1887/3204807">https://hdl.handle.net/1887/3204807</a>) or the article published in <em>International Journal of Osteoarchaeology</em> (DOI: 10.1002/oa.3100).</p> <p> </p> <p>References:</p> <p>Arts, Nico. 2013. Een knekelveld maakt geschiedenis. Het archeologisch onderzoek van het koor en het grafveld van de middeleeuwse Catharinakerk in Eindhoven, circa 1200-1850. Rapport 22. Utrecht: Uitgeverij Matrijs.</p> <p>Baetsen, Steffen, Willem Baetsen, Martijn Defilet, and Gerben Zielman. 2018. ‘Sint-Jansbeek brengt Oude Kerkhof boven water. Graven bij de Arnhemse Eusebiuskerk’. Archeologie in Nederland 3: 34–43.</p> <p>Baetsen, Steffen, and L. Weterings-Korthorst. 2013. ‘De menselijke overblijfselen’. In Een knekelveld maakt geschiedenis. Het archeologisch onderzoek van het koor en het grafveld van de middeleeuwse Catharinakerk in Eindhoven, circa 1200-1850, edited by Nico Arts and E. Altena, 288. Utrecht: Uitgeverij Matrijs.</p> <p>Bainbridge, D., and S. Genoves Tarazaga. 1956. ‘A Study of Sex Differences in the Scapula’. Journal of the Royal Anthropological Institute of Great Britain and Northern Ireland 86 (2): 109–34. https://doi.org/10.2307/2843994.</p> <p>Bass, W. M. 1987. Human Osteology: A laboratory and Field Manual. Missouri Archaeological Society. Special Publication 2. Columbia (Mo): Missouri Archaeological Society.</p> <p>Brooks, S., and J. M. Suchey. 1990. ‘Skeletal Age Determination Based on the Os Pubis: A Comparison of the Acsadi-Nemeskeri and Suchey-Brooks Methods’. Human Evolution 5 (3): 227–38.</p> <p>Buckberry, J.L., and A.T. Chamberlain. 2002. ‘Age Estimation from the Auricular Surface of the Ilium: A Revised Method’. American Journal of Physical Anthropology 119 (3): 231–39. https://doi.org/10.1002/ajpa.10130.</p> <p>Buikstra, Jane E., and Douglas H. Ubelaker. 1997. Standards for Data Collection from Human Skeletal Remains. 3rd ed. Arkansas Archaeological Survey Research Series 44. Fayetteville, Arkansas: Arkansas Archaeological Survey.</p> <p>Clevis, Hemmy, and T. S. Constandse-Westermann. 1992. De doden vertellen: opgraving in de Broerekerk te Zwolle 1987-1988. Kampen: Stichting Archeologie IJssel/Vechtstreek III.</p> <p>Hakvoort, A. 2013. ‘De begravingen bij de Keyserkerk te Middenbeemster’. Hollandia Reeks 464. Hollandia Reeks. Zaandijk: Hollandia Archeologen.</p> <p>İşcan, M. Y., S. R. Loth, and R. K. Wright. 1985. ‘Age Estimation from the Rib by Phase Analysis: White Females’. Journal of Forensic Sciences 30 (3): 853–63.</p> <p>İşcan, M. Yaşar, Susan R. Loth, and Ronald K. Wright. 1984. ‘Metamorphosis at the Sternal Rib End: A New Method to Estimate Age at Death in White Males’. American Journal of Physical Anthropology 65 (2): 147–56. https://doi.org/10.1002/ajpa.1330650206.</p> <p>Lovejoy, C. Owen, Richard S. Meindl, Thomas R. Pryzbeck, and Robert P. Mensforth. 1985. ‘Chronological Metamorphosis of the Auricular Surface of the Ilium: A New Method for the Determination of Adult Skeletal Age at Death’. American Journal of Physical Anthropology 68 (1): 15–28. https://doi.org/10.1002/ajpa.1330680103.</p> <p>Maat, G. J. R., and R. W. Mastwijk. 2009. Manual for the Physical Anthropological Report. 6th ed. Barge’s Anthropologica 6. Leiden: Barge’s Anthropologica.</p> <p>Maat, George J. R. 2001. ‘Diet and Age-At-Death. Determination from Molar Attrition: A Review Related to the Low Countries’. The Journal of Forensic Odonto-Stomatology 19: 18–21.</p> <p>McCormick, W. F., J. H. Stewart, and H. Greene. 1991. ‘Sexing of Human Clavicles Using Length and Circumference Measurements’. The American Journal of Forensic Medicine and Pathology 12 (2): 175–81. https://doi.org/10.1097/00000433-199106000-00017.</p> <p>Meindl, Richard S., and C. Owen Lovejoy. 1985. ‘Ectocranial Suture Closure: A Revised Method for the Determination of Skeletal Age at Death Based on the Lateral-Anterior Sutures’. American Journal of Physical Anthropology 68 (1): 57–66. https://doi.org/10.1002/ajpa.1330680106.</p> <p>Phenice, T. W. 1969. ‘A Newly Developed Visual Method of Sexing the Os Pubis’. American Journal of Physical Anthropology 30 (2): 297–301. https://doi.org/10.1002/ajpa.1330300214.</p> <p>Stewart, T. D. 1979. Essentials of Forensic Anthropology. Springfield (Ill.): C. C. Thomas.</p> <p>Steyn, M., and M. Y. Işcan. 1999. ‘Osteometric Variation in the Humerus: Sexual Dimorphism in South Africans’. Forensic Science International 106 (2): 77–85. https://doi.org/10.1016/s0379-0738(99)00141-3.</p> <p>Todd, T. 1920. ‘Age Changes in the Pubic Bones, I: The White Male Pubis’. American Journal of Physical Anthropology 3: 285–334.</p> <p>Workshop for European Anthropologists (WEA). 1980. ‘Recommendations for Age and Sex Diagnoses of the Skeleton’. Journal of Human Evolution 9: 517–49. https://doi.org/10.1016/j.jchb.2005.07.002.</p> <p> </p>
Spontaneous parthenogenesis in the parasitoid wasp Cotesia typhae: low frequency anomaly or evolving process?
<p>Raw data linked to the manuscript, including phenotyping and genotyping results for all Cotesia typhae females analyzed in this study.</p>
R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge
<p>This is the first R&D dataset for the LHC Olympics 2020 Anomaly Detection Challenge. It consists of 1M QCD dijet events and 100k W'->XY events, with X->qq and Y->qq. The W', X, and Y masses are 3.5 TeV, 500 GeV and 100 GeV respectively. The events are produced using Pythia8 and Delphes 3.4.1, with no pileup or MPI included. They are selected using a single fat-jet (R=1) trigger with pT threshold of 1.2 TeV. </p> <p>The events are randomly shuffled together, but for the purposes of testing and development, we provide the user with a signal/background truth bit for each event. Obviously, the truth bit will not be included in the actual challenge.</p> <p>These events are stored as pandas dataframes saved to compressed h5 format. For each event, all Delphes reconstructed particles in the event are assumed to be massless and are recorded in detector coordinates (pT, eta, phi). More detailed information such as particle charge is not included. Events are zero padded to constant size arrays of 700 particles, with the truth bit appended at the end. The array format is therefore (Nevents=1.1M, 2101).</p> <p>For more information, including an example Jupyter notebook illustrating how to read and process the events, see the official LHC Olympics 2020 webpage.</p> <p><a href="https://lhco2020.github.io/homepage/">https://lhco2020.github.io/homepage/</a></p> <p><strong>UPDATE May 18 2020</strong></p> <p>We have uploaded a second signal dataset for R&D, consisting of 100k W'->XY with X,Y->qqq (i.e. 3-prong substructure). Everything else about this signal dataset (particle masses, trigger, Pythia configuration, detector simulation) is the same as the previous one described above. </p> <p><strong>UPDATE November 23 2020</strong></p> <p>We now include high-level feature files for the background and 2-prong signal (events_anomalydetection_v2.features.h5) and for the 3-prong signal (events_anomalydetection_Z_XY_qqq.features.h5). To produce the features, we have clustered every event into R=1 jets using the anti-kT algorithm. The features (calculated using fastjet plugins) are the 3-momenta, invariant masses, and n-jettiness variables tau1, tau2 and tau3 for the highest pT jet (j1) and the second highest pT jet (j2):</p> <p>'pxj1', 'pyj1', 'pzj1', 'mj1', 'tau1j1', 'tau2j1', 'tau3j1', 'pxj2', 'pyj2', 'pzj2', 'mj2', 'tau1j2', 'tau2j2', 'tau3j2'</p> <p>The rows (events) in each feature file should be ordered exactly the same as in their corresponding raw event file. For convenience, we have also included the label (1 for signal and 0 for background) as an additional column in the first feature file (events_anomalydetection_v2.features.h5).</p> <p><strong>UPDATE February 11 2021</strong></p> <p>We have included the Delphes detector card and the Pythia8 command files used to produce the R&D datasets.</p> <p><strong>UPDATE April 17 2022</strong></p> <p>It was brought to our attention that somehow the raw events file events_anomalydetection.h5 was never updated to v2, which had a lower generator-level pT threshold (PhaseSpace:pTHatMin = 500) for QCD events to minimize artificial trigger sculpting. This v2 is the version that the features file (events_anomalydetection_v2.features.h5) corresponds to, as well as the Pythia cmnd file (pythia_RnD_qcd.cmnd). Now the raw events file has been brought up to date as well. </p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
DongTing: A Large-scale Dataset for Anomaly Detection of the Linux Kernel
<p>DongTing is the first large-scale dataset dedicated to Linux kernel anomaly detection. The dataset covers Linux kernels released in the last five years and includes a total of 18,966 well-labeled normal and attack sequences. The entire dataset is 85 GB in size (after decompression). The attack data covers 26 major kernel releases and contains a total of 12,116 system call sequences collected from running 17,855 bug-triggering programs. The normal data comes from 6,850 normal programs in four kernel regression test suites. We maintain the dataset and source code in Zenodo and Github, respectively, and back up the dataset and code in Baidu netdisk.</p> <h3><strong>Dataset</strong></h3> <p>The dataset is stored at <a href="http://doi.org/10.5281/zenodo.6627050">http://doi.org/10.5281/zenodo.6627050</a></p> <ul> <li>The data includes `abnormal_data`, `normal_data`, `models`, `npz` and baseline data, with a total volume of nearly 87 GB (including 85 GB for abnormal data and normal data, it's after decompression files size).</li> <li>The `Abnormal_data` directory contains 12,116 files containing system call sequence for 26 kernel releases, and the `Normal_data` directory contains 6,850 files containing system call sequences collected from four regression test suites. All of which are raw sequences.</li> <li>CNN/RNN, LSTM, and Wavenet (three sets of hyperparameters per model) machine learning models are selected, the ECOD model (without hyperparameters) was also chosen for the evaluation of DT. DT_abnormal, DT_normal, ADFA-LD, and PLAID are used for training respectively. The results of DT training models are stored in the directory `Models-DongTing`, and the results of ADFA-LD and PLAID training models are stored in the directory `Models-Comparison`.</li> <li>The directory `npz `stores the encoded dataset of DongTing, ADFA-LD, and PLAID (sequence length varies from 8 to 4495), according to syscall_64.tbl in Linux kernel 5.17, including the training set, validation set, and test set.</li> <li>The file `Baseline.xlsx` contains all the information about DongTing dataset, which can be used in training machine learning models. For example, the whole dataset is randomly divided into three sets with the ratio of 80%:10%:10% (training: validation: test). The implementation of dataset division can be found in the source code.</li> </ul> <h3><strong>Source Code</strong></h3> <p><br>The source code for dataset development is stored at <a href="https://github.com/HNUSystemsLab/DongTing">https://github.com/HNUSystemsLab/DongTing</a> and the following is a brief introduction.</p> <ul> <li>The source code contains three folders, i.e., `Source Code Files`, `Documents` and `DB`, where `Documents `stores the detailed documents related to development, `DB` stores samples data, and `Source Code Files` stores the source code related to the development of our dataset.</li> <li>The detailed description about the source code can be found in `Documents/Documentation.pdf`. The document consists of four parts: environment requirements, database, program structure and working steps, model training and evaluation (including training and evaluation). It details the preparation of the environment, data import method, functional description of each file in the source code directory, how model training and evaluation work and other related contents.</li> </ul> <p>We additionally maintain the dataset and source code on Baidu.com <a href="https://pan.baidu.com/s/1vu1WGZpf2DqMIoyGayNu3w?pwd=dtds">https://pan.baidu.com/s/1vu1WGZpf2DqMIoyGayNu3w?pwd=dtds</a> to facilitate the access from China.</p> <p> </p> <h3>Tips: </h3> <p>If you find DongTing useful for your research, please cite the article as "DongTing: A large-scale dataset for anomaly detection of the Linux kernel".</p> <blockquote> <p><br>@article{DUAN2023111745,<br>title = {DongTing: A large-scale dataset for anomaly detection of the Linux kernel},<br>journal = {Journal of Systems and Software},<br>volume = {203},<br>pages = {111745},<br>year = {2023},<br>issn = {0164-1212},<br>doi = {https://doi.org/10.1016/j.jss.2023.111745},<br>url = {https://www.sciencedirect.com/science/article/pii/S0164121223001401},<br>author = {Guoyun Duan and Yuanzhi Fu and Minjie Cai and Hao Chen and Jianhua Sun}<br>}<br><br></p> </blockquote>
Fatiando a Terra Data: Lightning Creek Sill Complex, Australia - Airborne total-field magnetic anomaly grid
<p>Regular grid of total-field magnetic anomaly data from the Lightning Creek Sill Complex, featuring a text-book dipolar anomaly. This is a gridded version of the Lightning Creek anomaly from <a href="https://github.com/fatiando-data/osborne-magnetic">our Osborne Mine dataset</a>.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Slice to area of interest. Project data to UTM. Interpolate to a regular 50 m grid at 500 m height. Add CF-compliant metadata to the grid. Export to compressed netCDF 4.</p> <p><strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. <a href="http://pid.geoscience.gov.au/dataset/ga/142419">http://pid.geoscience.gov.au/dataset/ga/142419</a></p> <p><strong>Source license: </strong><a href="http://pid.geoscience.gov.au/dataset/ga/142419">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/lightning-creek-magnetic-grid">https://github.com/fatiando-data/lightning-creek-magnetic-grid</a></p>
Investigation of the Post-Sunset Extra electron density Peak Poleward of the Equatorial Ionization Anomaly Southern Crest
<p>These files are waccmx data to draw the plot in the paper.</p> <p>dwind stands for neutral wind transport</p> <p>dfield stands for E cross B transport</p> <p>ambi stands for transport due to ambipolar diffusion</p> <p>OP40W stands for O+ at 40W</p> <p>VN40W stands for meridional wind at 40W</p> <p>WI40W stands for vertical component of E cross B at 40W</p> <p> </p> <p>OPCHEMP and OPCHEML stand for the production and loss of the O+</p> <p>All these fils are at 40W, and their dimensions are 192*60*288 , 192 stands for number of latitude, 60 stands for number of altitudes and 288 stands for number of times</p> <p> </p> <p>HMF2 and NMF2 stand for the hmf2 and nmf2 global</p>
Detecting anomalies in melt-extruded 3D printed parts using in situ data
<p>The data in this repository was gathered from a study to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing, using a set of sensors to non-destructively identfy printed parts that contain defects. The data underwent variance analysis to determine an "acceptable" range of filament diameters and non-destructivley identify spatial regions of printed cylinders in multi-part builds that contain defects.</p> <p>The data consists of two folders and a log meant to track procedural adherence for each cylinder printed, the introduced defects, or lack thereof, and the pressurization of the part. The "Final Build Logs" spreadsheet contains information regarding the two locations of the deformations along the 56 meters of filament needed to have no more than three anomalous cylinders out of the six printed cylinders, the date of the applied deformations to the filament, the initials of the researcher applying the deformations, the date that the build was printed along with the initials of the researcher who printed it, the part number, researcher initials, and date of the pressurization test for each cylinder within the build, and a comment describing any deviations from the procedure that play into the random error of the statistical analysis for each cylinder. </p> <p>The "Pressure Test Data" folder contains a folder for each build. Within these folders are .tdms files containing metadata on the measurement system in the header and tab-delimeted values for the columns. The columns of interest to the study are X_Value, representing time elapsed, and pressure, which we evaluated on the values' exponential decay rate. The files also contain supplemental information such as a column for temperature (celsius), and the flow rate (SLPM Normalized). The "Build Data" folder contains in situ data from the sensor-equipped printer in a .csv file, the STL file for the build, the gcode file from the applied slicer settings, the AMRP file stores printer settings, and a .pdf file for the setup specifications.</p>
Subducting seafloor anomalies promote porphyry copper formation
<p>Source data used to interrogate relationships between subducting seafloor anomalies and porphyry copper deposits presented in:</p> <p>Mather, B., Müller, R.D., Alfonso, C.P., Wright, N.M., Seton, M. (In prep.) Subducting seafloor anomalies promote porphyry copper formation.</p> <p>Python notebooks and scripts can be found in the <a href="https://github.com/brmather/SeafloorAnomalies">GitHub repository</a>.</p>
Regional velocity anomalies and slip distribution of the 2023 Kahramanmaraş, Türkiye Earthquakes
<p>dvp.dat contains columns of longitude, latitude, depth (km), and dVp (%) relative to the 1-D reference model used in https://www.mdpi.com/2076-3263/11/2/91.</p> <p>dvs.dat contains columns of longitude, latitude, depth (km) and dVs (%) relative to the 1-D reference model used in https://www.mdpi.com/2076-3263/11/2/91.</p> <p>slip_model.csv contains columns of subfault node ID, UTM Zone 37 easting (m), UTM Zone 37 northing (m), depth (m), strike (o), dip (o), strike slip (m),dip slip (m) and associated primary fault (i.e. <span>Southern East Anatolian fault (SEAF) and Savrun-Çardak-Sürgü fault (SCSF)). A</span><span> positive strike slip refers to a right-lateral slip and a positive dip slip refers to a normal slip.</span></p>
Mean age of air anomaly derived from ACE-FTS N2O
<p>The deseasonalized mean age of air (AoA) anomaly is derived using N2O observations from ACE-FTS and simulations of N2O and AoA from CLaMS. Results are provided as a monthly zonal mean in 10 degree latitude bins and on 19 pressure levels ranging from 100 hPa to 3.162 hPa. Four different versions of the AoA are provided, each using CLaMS simulations that were forced with a different reanalysis (ERA5, MERRA-2, JRA-55, ERA-Interim). AoA based on ERA5 is available from 2004/2 to 2021/12. AoA based on the other reanalyses is available from 2004/2 to 2017/12.</p>
Figure 5 in Morphological anomalies in polychaetes: Perinereis species (Polychaeta: Annelida) examples from the Brazilian coast
Figure 5. Number and type of morphological anomalies found in the species P. anderssoni and P. ponteni.
Figure 2 in Morphological anomalies in polychaetes: Perinereis species (Polychaeta: Annelida) examples from the Brazilian coast
Figure 2. Morphological anomalies in P. anderssoni: A. single antenna; B. basally fused antennae; C. completely fused antennae; D. seven tentacular cirri; E. two parapodia on the same side of chaetiger; F. five eyes; G. nine tentacular cirri; H. six tentacular cirri.
Figure 3 in Morphological anomalies in polychaetes: Perinereis species (Polychaeta: Annelida) examples from the Brazilian coast
Figure 3. Morphological anomalies in P. ponteni: A. nine tentacular cirri; B. seven tentacular cirri.
Figure 4 in Interannual Variability of Water Exchange Anomalies Between the Northern, Middle and Southern Caspian Based on Satellite Altimetry Data
Figure 4. Temporal variability of anomalies of surface geostrophic velocities (m/s) directed normal to 133 (a) and 209 (b) tracks. Positive values correspond to the southeast direction of currents, negative values correspond to the northwest direction.
Figure 1 in Interannual Variability of Water Exchange Anomalies Between the Northern, Middle and Southern Caspian Based on Satellite Altimetry Data
Figure 1. The Caspian Sea. Main parts of the Caspian Sea: (1) – the Northern Caspian (2) - the Middle Caspian; (3) – the Southern Caspian; (4) – the Kara-Bogaz-Gol Bay. Isobaths are shown in meters. The coastline corresponds to year 1934, when the sea level was -26.46 m relative to the World Ocean level (Lebedev, 2018).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.