Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
72
datasets available to search
ShareScore release 0.9.0
Dataset results
72 results for “anomaly dataset”
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>
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>
M100 dataset: time-aggregated data for anomaly detection
<p>This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, <a href="https://www.hpc.cineca.it/hardware/marconi100">https://www.hpc.cineca.it/hardware/marconi100</a>). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast. <br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.</p> <p>This particular dataset is made for anomaly detection purposes, it contains the same data as the main dataset but aggregated over time, with one Parquet file for each node. The data is distributed in tarballs, each one including all the files relative to the nodes contained in a given rack. For each file, the rows represent periods of 15 minutes, with the columns being aggregated values (average, standard deviation, min, max) over all the IPMI metrics that are available for the node; an additional column contains anomaly labels from Nagios.</p> <p>More details can be found in the companion repository: <a href="https://gitlab.com/ecs-lab/exadata">https://gitlab.com/ecs-lab/exadata</a>, including the spatial distribution of the nodes in the room.</p>
Anomaly Engine Development and Testing Datasets
<p>The following datasets have been used for development an testing of Anomaly Engine Webapp. They includes data samples for all the 4 scenarios:<br> 1 - Simple Self Financing<br> 2 - Self Financing by bank account<br> 3 - Indirect SF (by recharge)<br> 4 - Indirect Account Self Financing</p> <p>Each file row represents a graph relationship between a source node (<code>source</code>) and a destination node (<code>target</code>).</p> <p>Files must have the following schema:</p> <ul> <li><code>source_type</code>: Source node label;</li> <li><code>source_attributes</code>: Source node attributes. Can be null;</li> <li><code>target_type</code>: Destination node label;</li> <li><code>target_attributes</code>: Destination node attributes. Can be null;</li> <li><code>relation_type</code>: Relationship label;</li> <li><code>relation_attributes</code>: Relationship attributes. Can be null.</li> </ul> <p>Attributes must be expressed as key-value pairs separated by a semicolon <code>;</code>. For example</p> <pre><code>key1=value1;key2=value2</code></pre> <p> </p> <p>The datasets are also available in <a href="https://gitlab.infinitech-h2020.eu/pilot16/aml-graph-payments-anomaly-detection/-/tree/master/docker/docker_resources/docker_webapp/src/webapp_dash/assets">Infinitech Marketplace</a> </p>
MARVEL - Malta Audio Visual Anomaly Dataset (MAVAD)
<p>The raw audio-video data was collected from two locations on the island of Malta one in Zejtun, a town close to the industrial region on the eastern coast, and another in Mgarr, a rural town on the western coast. Three AV cameras were deployed, two in Zejtun (Zejtun Scrapyard and Zejtun Field) and one in Mgarr, resulting in three datasets with the same titles.</p> <p> </p>
Dataset Artifact for Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems
<p>The dataset contains a small set of application runs from Eclipse supercomputer. The applications run with and without synthetic HPC performance anomalies. More detailed information regarding synthetic anomalies can be found at: https://github.com/peaclab/HPAS.</p> <p>We have chosen four applications, namely LAMMPS, sw4, sw4Lite, and ExaMiniMD, to encompass both real and proxy applications. We have executed each application five times on four compute nodes without introducing any anomalies. To showcase our experiment, we have specifically selected the "memleak" anomaly as it is one of the most commonly occurring types. Additionally, we have also executed each application five times with the chosen anomaly. The dataset we have collected consists of a total of 160 samples, with 80 samples labeled as anomalous and 80 samples labeled as healthy. For the details of applications please refer to the paper.</p> <p>The applications were run on Eclipse, which is situated at Sandia National Laboratories. Eclipse comprises 1488 compute nodes, each equipped with 128GB of memory and two sockets. Each socket contains 18 E5-2695 v4 CPU cores with 2-way hyperthreading, providing substantial computational power for scientific and engineering applications.</p>
Synthetic noisy datasets for submarine cable magnetic anomaly locaition
<p>Synthetic noisy datasets for submarine cable magnetic anomaly locaition, including a training, a validation and two test data sets.</p>
Primary data for: "Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long-term rodent datasets"
<p>Datasets</p>
Dataset for Anomaly Detection Using Inter-Arrival Curves for Real-time Systems
<p>The dataset shows the input files and detailed results for the experiments discussed in the paper. A README file provide more details on the data.</p>
EIRSAT-1 Test Campaign and Flight Dataset for Anomaly Detection
<p><span>We have curated a unique dataset derived from EIRSAT-1, Ireland's inaugural domestically produced satellite, as a testing and validation resource for these ML models and the future development cycle of AI-enabled small satellites. This dataset consists of a training set developed during ground testing and containing artificial anomalies induced to train satellite operators, a validation dataset containing real anomalies encountered during the qualification campaign, and an early flight test dataset collected since the satellite was launched on December 1<sup>st</sup>, 2023. This paper presents an in-depth analysis of the efficacy of these ML techniques when applied to the EIRSAT-1 dataset, offering insights into their potential to revolutionize the domain of satellite operations through enhanced autonomy and responsiveness. This study not only showcases the capabilities of these ML techniques in an operational environment but also sets the stage for future research and development in autonomous satellite systems.</span></p>
ComplexVAD Video Anomaly Detection Dataset
<p><strong>Introduction</strong></p> <p>The ComplexVAD dataset consists of 104 training and 113 testing video sequences taken from a static camera looking at a scene of a two-lane street with sidewalks on either side of the street and another sidewalk going across the street at a crosswalk. The videos were collected over a period of a few months on the campus of the University of South Florida using a camcorder with 1920 x 1080 pixel resolution. Videos were collected at various times during the day and on each day of the week. Videos vary in duration with most being about 12 minutes long. The total duration of all training and testing videos is a little over 34 hours. The scene includes cars, buses and golf carts driving in two directions on the street, pedestrians walking and jogging on the sidewalks and crossing the street, people on scooters, skateboards and bicycles on the street and sidewalks, and cars moving in the parking lot in the background. Branches of a tree also move at the top of many frames.</p> <p>The 113 testing videos have a total of 118 anomalous events consisting of 40 different anomaly types.</p> <p>Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. A single frame can have more than one anomaly labeled.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~39GB</li> <li>The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity), a ground truth annotation file for each Test sequence, and a README.md file describing the data organization and ground truth annotation format.</li> <li>The zip files contain a Train directory, a Test directory, an annotations directory, and a README.md file.</li> </ul> <p><strong>License</strong></p> <p>The ComplexVAD dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Dataset related to 'Full-waveform inversion reveals diverse origins of lower mantle positive wave speed anomalies'
<p>This repository contains the global distribution of sources and receivers, tomographic models (netCDF4 format), stacked waveforms from the wavefield modelling (.h5 format), 2D grids of the time-depth correlations (.csv format), and Python scripts required for the full analysis and figures presented in the manuscript.</p>
Long-Tailed Anomaly Detection (LTAD) Dataset
<p><strong>Introduction</strong></p> <p>Anomaly detection (AD) aims to identify defective images and localize their defects (if any). Ideally, AD models should be able to: detect defects over many image classes; not rely on hard-coded class names that can be uninformative or inconsistent across datasets; learn without anomaly supervision; and be robust to the long-tailed distributions of real-world applications. To address these challenges, we formulate the problem of long-tailed AD by introducing several datasets with different levels of class imbalance for performance evaluation.</p> <p>To encourage more follow up works on long-tailed AD, we are publicly releasing the dataset split used in our paper (“Long-Tailed Anomaly Detection with Learnable Class Names” by Chih-Hui Ho, Kuan-Chuan Peng, and Nuno Vasconcelos, CVPR 2024).</p> <p>Files in the unzipped folder:</p> <p>1. ./README.md: This Markdown file</p> <p>2. ./dataset_split: Folder contains long-tail splits from three datasets. See below for details.</p> <p><strong> </strong></p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~16MB</li> <li>Three datasets are used in this project, including [MVTec](https://www.mvtec.com/company/research/datasets/mvtec-ad), [VisA](https://github.com/amazon-science/spot-diff) and [DAGM](https://www.kaggle.com/datasets/mhskjelvareid/dagm-2007-competition-dataset-optical-inspection). Please download the datasets from their original repositories.</li> <li>The dataset split provided in this folder is organized as follows:<br>```<br>dataset_split<br>|---dagm_lt<br>|---mvtec_lt<br>|---visa_lt<br>|-----|-- exp<br>|-----|-----|----- 100<br>|-----|-----|----- |-----test.json<br>|-----|-----|----- |-----train.json<br>|-----|-----|----- 200<br>|-----|-- step<br>|-----|-- ...<br>```</li> <li>Each long-tailed dataset split contains a subfolder ``imbalance_type/imbalance_factor", where imbalance type can be [exponential (exp), step, reverse exponential (exp_reverse), reverse step (step_reverse)]. The definition of imbalance type and imbalance factor can be found in our paper. Each subfolder contains two json files, one for training and the other for testing.</li> <li>Each entry in the json file contains the meta information of an image and is similar to<br>```<br>{"filename": "candle/test/bad/000.JPG", "label": 1, "label_name": "defective", "clsname": "candle", "maskname": "candle/ground_truth/bad/000.png"}<br>```<br>- filename: location of the input image in the dataset<br>- label: indicates whether the input image is normal (labeled as 0) or defective (labeled as 1)<br>- label name: can be "good" or "defective"<br>- clsname: class name of the input image<br>- maskname (optional): location of the binary image that indicates the defect region. This is only available for test.json, because there is no defect image during training.</li> </ul> <p><strong>Citation</strong></p> <p>If you use the LTAD dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{Ho_2024_CVPR, author = {Ho, Chih-Hui and Peng, Kuan-Chuan and Vasconcelos, Nuno}, title = {Long-Tailed Anomaly Detection with Learnable Class Names}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024} } </code></pre> <p><strong>License</strong></p> <p>The LTAD dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>. For the images in the MVTec, VisA, and DAGM datasets, please refer to their websites for their copyright and license terms.</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023-2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Street Scene Video Anomaly Detection Dataset
<p><strong><span>Introduction</span></strong></p> <p><span>The Street Scene dataset consists of 46 training video sequences and 35 testing video sequences taken from a static USB camera looking down on a scene of a two-lane street with bike lanes and pedestrian sidewalks.<span> </span>Videos were collected from the camera at various times during two consecutive summers.<span> </span>All of the videos were taken during the daytime.<span> </span>The dataset is challenging because of the variety of activities taking place such as cars driving, turning, stopping and parking; pedestrians walking, jogging and pushing strollers; and bikers riding in bike lanes. In addition, the videos contain changing shadows, and moving background such as a flag and trees blowing in the wind.</span></p> <p><span>There are a total of 202,545 color video frames (56,135 for training and 146,410 for testing) each of size 1280 x 720 pixels. The frames were extracted from the original videos at 15 frames per second.</span></p> <p><span>The 35 testing sequences have a total of 205 anomalous events consisting of 17 different anomaly types. A complete list of anomaly types and the number of each in the test set can be found in our paper.</span></p> <p><span>Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. Track lengths vary from tens of frames to 5200 which is the length of the longest testing sequence. A single frame can have more than one anomaly labeled.</span></p> <p><span>NOTE: This version of the dataset differs slightly with the original made available in 2020.<span> </span>Some anomalies were found in a few of the normal training sequences.<span> </span>These training frames were deleted from the dataset.<span> </span>Specifically, the following frames were removed:</span></p> <p><span>Train026: frames 1-184 (car taking a u-turn)</span></p> <p><span>Train027: frames 1-229 (jay walkers)</span></p> <p><span>Train031: frames 1-299 (jay walkers, illegally parked car)</span></p> <p><strong><span>At a Glance</span></strong></p> <ul> <li><span>The size of the unzipped dataset is ~46GB</span></li> <li><span>The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity) along with ground truth annotations, and a README.md file describing the data organization and ground truth annotation format.</span></li> <li><span>The zip file contains a Train directory, a Test directory and a README.md file.</span></li> </ul> <p><strong><span>Other Resources</span></strong></p> <p><span>None</span></p> <p><strong><span>Citation</span></strong></p> <p><span>If you use the Street Scene dataset in your research, please cite our contribution:</span></p> <pre><code>@inproceedings{ramachandra2020street, title={Street Scene: A new dataset and evaluation protocol for video anomaly detection}, author={Ramachandra, Bharathkumar and Jones, Michael}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={2569--2578}, year={2020} } </code></pre> <p><strong><span>License</span></strong></p> <p><span>The Street Scene dataset is released under </span><a href="https://creativecommons.org/licenses/by-sa/4.0/"><span>CC-BY-SA-4.0 license</span></a><span>.</span></p> <p><span>All data:</span></p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0 </code></pre>
Dataset used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study."
<p>This dataset repository includes eight raster layers (Reference System EPSG:3035 - ETRS89-extended / LAEA Europe), used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study", and obtained by the adaptation of analyses carried out by previous studies (Morabito et al., 2021; Guerri et al., 2021; 2022).</p> <p>Further information regarding the source, study period, and horizontal resolution is available in the attached text file. </p> <p> </p> <p><strong><em>References</em></strong></p> <p>Guerri, G., Crisci, A., Congedo, L., Munafò, M., Morabito, M., <strong>2022</strong>. A functional seasonal thermal hot-spot classification: Focus on industrial sites. Science of The Total Environment 806, 151383.<a href="http://https://doi.org/10.1016/j.scitotenv.2021.151383"> https://doi.org/10.1016/j.scitotenv.2021.151383</a>.</p> <p>Guerri, G., Crisci, A., Messeri, A., Congedo, L., Munafò, M., Morabito, M., <strong>2021</strong>. Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers. Remote Sensing 13, 538. <a href="https://doi.org/10.3390/rs13030538">https://doi.org/10.3390/rs13030538</a>.</p> <p>Morabito, M., Crisci, A., Guerri, G., Messeri, A., Congedo, L., Munafò, M., <strong>2021</strong>. Surface Urban Heat Islands in Italian Metropolitan Cities: Tree Cover and Impervious Surface Influences. Science of The Total Environment 751, 142334. <a href="https://doi.org/10.1016/j.scitotenv.2020.142334">https://doi.org/10.1016/j.scitotenv.2020.142334</a>.</p>
Datasets for Simulation-based Anomaly Detection for Multileptons at the LHC
<p>The simulated background and signal data used for a signal model agnostic machine learning search. This search examined the decay of the Higgs boson to leptons working off of LHC data from the Atlas experiment. Details are provided in the paper entitled "Simulation-based Anomaly Detection for Multileptons at the LHC". </p>
Dataset for Sound-based Anomalies Detection in Agricultural Robotics Application
<p>This data set contains data related to a Mowing Intelligent Tool (MowIT).</p> <p>Two different microphones were used to collect the sound samples, recording the audio with just one single channel, with a sampling rate of 44100 Hz and 16 bits resolution.</p> <p>The data provided by an inertial measurement unit (IMU) was also recorded since that was already integrated into the MowIT.</p> <p>Two different data collections were performed in different open-air environments with grass to cut.</p> <p>In each collection, eight different sample sets were made, five with the machine cutting using a trimmer line and the other three using the blades. Various combinations were used in each set, and tools were or were not placed on each of the three cutting axes of the MowIT. For each group, the acquisitions were designated from 0 to 7.</p> <p>Each folder of the first collection is a combination containing two audio files, one for each microphone used, the IMU data and a photograph of the lower part of the MowIT to understand the configuration used.</p> <p>In the second collection, to improve the variety of data, three distinct sub-sets were performed for combination: the first with the MowIT turned on but not cutting grass and the next two cutting grass. </p> <p>In samples 4 and 7, there is one audio where the MowIT cuts but stops due to motor stress. In sample 6, the initial recording was not made without cutting grass, and only the two recordings were made cutting grass.</p> <p> </p> <p> </p> <p> </p>
Dataset for model input of WRF model for the paper:Modulation of Extratropical Cyclones by Previous Cyclones via the Sea Surface Temperature Anomaly over the Sea of Japan in Winter
<p>This is the dataset and code for generating the lower boundary condition which used in our study submitted to the JGR-Atmospheres. The meteorological data for the initial condition are available on NCEP-FNL ftp database.</p>
Dataset for Non-resonant Anomaly Detection with Background Extrapolation
<p>These are the datasets used in the journal version of the Non-resonant Anomaly Detection with Background Extrapolation paper. The datasets are simulated using MadGraph5 aMC@NLO, Pythia 8.310, and Delphes. There are 0.2M signal events of semi-visible jets in five sets of parameters (invisible-ratio, Z' mass) = { (1/3, 4 TeV), (1/3, 2 TeV), (1/3, 3 TeV), (0, 4 TeV), (2/3, 4 TeV) }, 18.6M background events of SM QCD jets (including background, ideal AD background, and simulated background) for training, and 21.4M background events for testing. The detailed breakdown of number of events after selections in different regions is listed in Table1 of the paper. The input parameter cards used for generating background and signal events are also included.</p>
Dataset: 2023 Aircraft traffic and GPS anomalies aggregated per hexbins
<p>We divided the globe into hexbins, each with an average area of 385 square kilometers. Once the hexbin grid was established, data from the GPS gaps, GPS deviations, and Traffic Density datasets were used to populate these hexbins with relevant information. On average, each hexbin has around 23,478 flights passing through it.</p> <ul> <li><strong>Total Records</strong>: 14,117 - total number of hexbin on a map, where number of flights > 0</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>id:</strong></li> <li><strong>WKT</strong>: Well-Known Text representation of a POINT (senter of each hexbin) in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>flights</strong>: number of flights traveled trough that hexbin in 2023</li> <li><strong>gaps</strong>: number of GPS gap incidents registered in that hexbin in 2023</li> <li><strong>deviations</strong>: number of GPS deviation incidents started in that hexbin in 2023</li> </ul> </ul>
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.