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250 results for “Synthetic Dataset”
TUT Sound Events 2018 - Circular array, Reverberant and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Circular array, Reverberant and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated, reverberant, and circular-array format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters). The sound events are spatially placed within a room using the image source method. The room size chosen was 10x8x4 meter with reverberation time per octave band of [1.0, 0.8, 0.7, 0.6, 0.5, 0.4] s and 125 Hz–4 kHz band center frequencies.</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of at least 1 meter away from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TUT Sound Events 2018 - Ambisonic, Anechoic and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Anechoic, and Synthetic Impulse Response Dataset </strong></p> <p>This dataset consists of simulated anechoic first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of [1 10] meters from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TUT Sound Events 2018 - Circular array, Anechoic and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Circular array, Anechoic and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated anechoic circular-array format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of [1 10] meters from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TUT Sound Events 2018 - Ambisonic, Reverberant and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Reverberant and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated reverberant first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters). The sound events are spatially placed within a room using the image source method. The room size chosen was 10x8x4 meter with reverberation time per octave band of [1.0, 0.8, 0.7, 0.6, 0.5, 0.4] s and 125 Hz–4 kHz band center frequencies.</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of at least 1 meter away from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p> <p> </p>
TAU Moving Sound Events 2019 - Ambisonic, Anechoic, Synthetic IR and Moving Source Dataset
<p><strong>Tampere University (TAU) Moving Sound Events 2019 - Ambisonic, Anechoic and Synthetic Impulse Response (IR) and Moving Source Dataset</strong></p> <p>This dataset consists of simulated anechoic first order Ambisonic (FOA) format recordings with moving point sources each in 2D spherical space represented with azimuth and elevation angles. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), starting spatial location and directional spatial location in azimuth and elevation angles (in degrees), angular velocity of motion, and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the DCASE 2016 task 2 dataset. This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. Every event is assigned a spatial trajectory on an arc with a constant distance from the microphone (in the range 1-10 m) and moving with a constant angular velocity for its duration. Due to the choice of the ambisonic spatial recording format, the steering vectors for a plane wave source or point source in the far field are frequency-independent. Hence, there is no need for a time-variant convolution or impulse response interpolation scheme as the source is moving; the spatial encoding of the monophonic signal was done sample-by-sample using instantaneous ambisonic encoding vectors for the respective DOA of the moving source. The synthesized trajectories in the dataset vary in both azimuth and elevation and are simulated to have a constant angular velocity in the range [-90, 90]/s with 10-degree/s steps.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Localization, Detection and Tracking of Multiple Moving Sound Sources with Convolutional Recurrent Neural Networks'</a> work.</p>
Synthetic dataset used in "The maximum weighted submatrix coverage problem: A CP approach"
<p>Synthetic dataset used in "The maximum weighted submatrix coverage problem: A CP approach".</p> <p>Includes both the generated datasets as a zip archive and the python script used to generate them.</p> <p>Each instance is composed of two files in the form</p> <ul> <li>XxY_K_O_0xN_AxB_Smatrix.tsv being the matrix to use. Each row on a separate line, with tab-separated cells.</li> <li>XxY_K_O_0xN_AxB_Ssolution.txt giving the implanted solution. One submatrix per line. Then two JSON arrays follow, separated by a tabulation. The first is the list of rows selected in the submatrix, the second the columns.</li> </ul> <p>With:</p> <ul> <li>X and Y the size of the matrix</li> <li>K the number of submatrices in the implanted solution</li> <li>O the (minimum) overlap percentage of each submatrix</li> <li>N the sigma used for the background noise</li> <li>A and B the size of the implanted submatrices (subject to noise)</li> </ul> <p> </p>
Synthetic Datasets for "Binary Classification Optimisation with AI-Generated Data"
<p>Images of melanomas and Basal Cell Carcinoma generated with a stylegan2. Dataset corresponding to the article "Binary Classification Optimisation with AI-Generated Data"</p>
Synthetic Aircraft Landing Trajectory Dataset for Oslo Airport
<p>This dataset is <span>a collection of synthetically generated landing trajectories for Oslo airport.</span></p>
Synthetic pupulation & convictions dataset
Open the record for dataset details and reuse information.
Infected Leaves Synthetic Dataset - Ash Tree Dieback
<p>A dataset of synthetic ash tree leaves with accompanying labels for leaves of infection level: Healthy, low level and mid level. The images and labels are already split into train and test sets. The synthetic leaves are placed on a set of real background images of ash tree leaves and branches to approximate an appropriate background. </p>
Datasets for modelling synthetic tectonic tremors and VLFEs
<p>This dataset was used to model synthetic seismic waveforms radiated by clusters of quartz veins in subduction shear zone.</p> <p>Eight folders are included in this dataset.</p> <ol> <li>Source_1000m: Time serieses of the number of cracks ruptured at each time step, which were calculated with probabilistic cell automaton model of Ide and Yabe (2019, https://doi.org/10.1007/s00024-018-1976-9). The system size was set to 1000 cells X 1000 cells. The rupture propagation probability pb was changed from 0.01 to 0.24 with 0.01 increments. Time step was set to 0.01 seconds. "npy" format of numpy.</li> <li> Source_100m: Same as "Source_1000m", but the sysmtem size was set to 100 cells X 100 cells.</li> <li>Single_Shear_0.250: Synthetic seismic waveforms from a single shear crack in subduction shear zone. The duration of the rupture was assumed to be 0.25 seconds. SAC format.</li> <li>Single_Shear_0.500: Same as "Single_Shear_0.250", but the duration of the rupture was assumed to be 0.500 seconds.</li> <li>Single_Shear_0.125: Same as "Single_Shear_0.250", but the duration of the rupture was assumed to be 0.125 seconds.</li> <li>Single_Extension_0.250: Synthetic seismic waveforms from a single extension crack in subduction shear zone. The duration of the rupture was assumed to be 0.25 seconds. SAC format.</li> <li>Single_Extension_0.500: Same as "Single_Extension_0.500", but the duration of the rupture was assumed to be 0.500 seconds.</li> <li>Single_Extension_0.125: Same as "Single_Extension_0.125", but the duration of the rupture was assumed to be 0.125 seconds.</li> </ol>
Synthetic Multimodal Dataset using MuJoCo: UR5 Robot Motion
<p>Using the Mujoco environment, we simulated robot trajectory and transitions from one formation<br> to another. Mujoco is a 3D simulator, while Gym serves as an interface to the UR5 robot.<br> The robot has measurement units that allow the acquisition of the angles, positions,<br> and quaternions of the joints and the position of the end-effector. The robot is located on<br> a table with 4 cameras all from the same radius to the center of the robot just<br> rotated by 90° for each of them. Using the described environment, we collected 1999 samples <br> at a rate of 10 samples per second.<br> <br> ################################################################<br> ################################################################<br> <br> camera views:</p> <p> id: 0<br> name: 'camera_0'<br> xmat: array([ 0.70710678, 0.42537261, -0.56485232, -0.70710678, 0.42537261,<br> -0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([-2., -2., 3.])</p> <p> id: 1<br> name: 'camera_1'<br> xmat: array([-0.70710678, 0.42537261, -0.56485232, -0.70710678, -0.42537261,<br> 0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([-2., 2., 3.])</p> <p> id: 2<br> name: 'camera_2'<br> xmat: array([ 0.70710678, -0.42537261, 0.56485232, 0.70710678, 0.42537261,<br> -0.56485232, -0. , 0.79882181, 0.60156772])<br> xpos: array([ 2., -2., 3.])</p> <p> id: 3<br> name: 'camera_3'<br> xmat: array([-0.70710678, -0.42537261, 0.56485232, 0.70710678, -0.42537261,<br> 0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([2., 2., 3.])</p> <p> <br> <br> The camera data is stored as .png files with a size of 256x256 </p> <p><br> <br> ################################################################<br> ################################################################<br> <br> The files angles.pt, angular_velocity.pt, angular_acceleration.pt contains information about<br> the motor data of the joints. The angles, velocity, acceleration is the information about the <br> Motor in each joint in following order:<br> ['base_to_lik', 'base_to_rik', 'elbow_joint', 'shoulder_lift_joint', 'shoulder_pan_joint', 'wrist_1_joint', 'wrist_2_joint', 'wrist_3_joint']<br> <br> <br> ################################################################<br> ################################################################<br> <br> The files pose.pt and quaternion.pt contains information about<br> the body data of the robot. The pose of each element is in following order:<br> ['base', 'base_link', 'box_2_link', 'box_link', 'drop_box', 'ee_link', 'forearm_link', 'left_inner_finger', 'left_inner_knuckle', 'right_inner_finger', 'right_inner_knuckle', 'robotiq_85_base_link', 'shoulder_link', 'upper_arm_link', 'world', 'wrist_1_link', 'wrist_2_link', 'wrist_3_link']<br> <br> <br> ################################################################<br> ################################################################<br> <br> The file action.pt contains information about the used action in the corresponding time-step. <br> The action of each element is in following order:<br> ['forearm_T', 'gripper_motor', 'shoulder_lift_T', 'shoulder_pan_T', 'wrist_1_T', 'wrist_2_T', 'wrist_3_T']<br> <br> </p>
Synthetic Multimodal Dataset using ABB Studio: Single Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Löppenberg et al. (2024), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p>Frame '02655_frame' is corrupted. That means the corresponding values of the other modality needs to be removed as well.</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Löppenberg, M., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2024). Dynamic robot routing optimization: State-space decomposition for operations research-informed reinforcement learning. Robotics and Computer-Integrated Manufacturing, 90, 102812.</p>
Synthetic Multimodal Dataset using ABB Studio: Dual Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Diprasetya et al. (2023), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters for each robot:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Diprasetya, M. R., Yuwono, S., Löppenberg, M., & Schwung, A. (2023, July). Integration of ABB Robot Manipulators and Robot Operating System for Industrial Automation. In 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) (pp. 1-7). IEEE.</p>
Large-scale Instance-diverse Synthetic COVID-19 CT Dataset
<p><strong>Dataset Description</strong></p> <p>This dataset consists of 367000 synthetic COVID-19 CT images generated from a new GAN algorithm known as the stacked residual dropout GAN (sRD-GAN) [1]. The 367 input images are acquired from the iCTCF dataset [2] and are stored in 512 × 512 × 3 in .jpg format. The input images [2] and their corresponding synthetic images are included in this dataset. </p> <p> </p> <p><strong>Stacked Residual Dropout GAN (sRD-GAN)</strong></p> <p>The sRD-GAN utilizes a regularization-based strategy in an Image-to-Image (I2I) translation setting to facilitate instance-level diversity. In this study, we show that the stacked dropout regularization in the generator model can induce significant latent-space stochasticity which generates perceptually significant structural dissimilarity in the output space. </p> <p>Paper: <a href="https://www.mdpi.com/2306-5354/9/11/698">Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout</a></p> <p>DOI: <a href="https://doi.org/10.3390/bioengineering9110698">10.3390/bioengineering9110698</a></p> <p> </p> <p><strong>Advantages</strong></p> <p>1) High-resolution, diverse patterns of synthetic ground-glass-opacities presented in chest CT images with different anatomy. </p> <p>2) Generalize across GAN-based models since the stacked residual dropout mechanism is not task- or dataset-specific. </p> <p>3) Does not require any auxiliary condition to generate diverse outputs. </p> <p>4) Does not require any non-trivial modification on the model's architectures. </p> <p> </p> <p><strong>Disadvantages</strong></p> <p>1) Diversity is not presented in large perceptual differences, and it focuses only on fine-grained details. </p> <p>2) Since sRD-GAN is trained in an unsupervised image-to-image setting, and the synthesis process does not require any auxiliary condition, thus, the magnitude of the style attributes (GGO features) cannot be manipulated. </p> <p> </p> <p><strong>Acknowledgments: </strong></p> <p>If you use this dataset in your research, please credit the author: </p> <p>[1] Lee, K.W.; Chin, R.K.Y. Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout. <em>Bioengineering</em> <strong>2022</strong>, <em>9</em>, 698. https://doi.org/10.3390/bioengineering9110698</p> <p> </p> <p><strong>References: </strong></p> <p>[2] Ning, W.; Lei, S.; Yang, J.; Cao, Y.; Jiang, P.; Yang, Q.; Zhang, J.; Wang, X.; Chen, F.; Geng, Z.; et al. Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via Deep Learning. Nat. Biomed. Eng. <strong>2020</strong>, 4, 1197–1207.</p>
Dataset of CCFs, dispersion data, synthetic ambient noise data, and rupture simulation data in the Anninghe study
<p>This is the main dataset supporting the Anninghe paper.</p> <p>sta.loc represents the station list file.<br> 3DVelocityModel folder contains the final velocity model file Anninghe_Vs3D.dat.<br> CCFs_Original folder contains the original CCFs obtained from the Anninghe array.<br> CCFs_Separated folder contains the CCFs after model separation. In CCFs_Separated folder, the merge folder is the final average CCFs used for later tomography.<br> DisperData folder contains the seimiautopicked dispersion data from the separated CCFs.<br> RuptureSimulation folder contains the rupture simulation data in the paper.<br> SynNoiseData folder contains the CCFs calculated from synthetic ambient noise data.<br> </p>
aKmerBroom synthetic datasets
<p>Synthetic datasets used to evaluate aKmerBroom performance</p>
Synthetic Datasets from the 2023 ECXAI Workshop Paper on Principled Benchmarking for Rule Set Learning Algorithms
<p>Synthetic datasets generated as part of the demonstration given in the paper <em>Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms</em> presented at the <em>Evolutionary Computing and Explainable Artificial Intelligence</em> (ECXAI) workshop taking place as part of the 2023 GECCO conference.</p>
(raw dataset) "Spatial biology of Ising-like synthetic genetic networks"
<p>This is the raw data for the manuscript Simpson et al "Spatial biology of Ising-like synthetic genetic<br> networks"</p>
Statistical error estimation from residual statistics of multiple collocated datasets: Data from synthetic experiments
<p>This archive contains the data used in the paper "How far can the statistical error estimation problem be closed by collocated data?" by A.Vogel and R.Menard (preprint available at: https://doi.org/10.5194/egusphere-2022-996) accepted for publication in Nonlinear Processes in Geophysics (NPG). </p> <p>The data refers to the synthetic experiments in Sect.5 of the paper which demonstrate the general ability to estimate statistical error covariances and cross-statistics from residual covariances, as well as the effects of inaccurate assumptions with respect to different setups.</p> <p>Further information on the data can be found in the README.txt file.</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.