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Dataset results
55 results for “Dataset augmentation”
Augmented Brain Tumor Multimodal Dataset Based on Reconstruction Consistency Loss
<p>We solved the problem that the brain MRI data only has the T2 modality and the other three modalities are missing. The data synthesis of the missing modal I data was completed, and the missing modal data of Flair, T1, and T1ce were completed, and 10,685 pieces of data were generated for each group of modalities.</p>
Everyday Augmented Conversation Videos Dataset
<p>This dataset builds on the <a href="https://zenodo.org/deposit/6901792">Everyday Conversation Videos Dataset</a> and includes augmented variants of those videos. The 160 videos in this dataset all depict about a minute long everyday conversations. For these, four different actors (A1-A4) acted out a set of eight scenarios (S1-S8) Here, the videos have been cropped, trimmed, and standardized to 720p at 30fps. We then added Snapchat filters to the videos by routing them through Snap Camera. Each video is then available in one of five filter conditions, where:</p> <ul> <li>F0: No filter added</li> <li>F01: Beautification filter</li> <li>F02: Subtle beautification filter</li> <li>F03: Face and body tattoo filter</li> <li>F1-F8: Scenario-specific filters</li> </ul>
A Transformer-based Approach for Augmenting Software Engineering Chatbots Datasets
<p>The results, datasets, and scripts used in "A Transformer-based Approach for Augmenting Software Engineering Chatbots Datasets" paper.</p>
Dataset: Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies
<p>Dataset for the following paper: <a href="https://arxiv.org/abs/2405.04401">"Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies", Julien Baglio, arXiv:2405.04401</a></p> <p>It contains:</p> <ul> <li>one folder named "data_for_all_plots" containing the raw data for the s, t, and y distributions for all the figures of the paper as well as a Jupyter notebook to generate the figures.</li> <li>one file named "variance_calculations_qGAN.txt" containing the data to calculate the errors for the KL divergences.</li> </ul>
Dataset of measured binaural room impulse responses for use in an position-dynamic auditory augmented reality application
<p>The dataset includes measured binaural room impluse responses (BRIRs) using a head and torso simulator (KEMAR). The measurements are realized for six loudspeaker positions. Five loudspeakers are placed in a standardized five-channel surround setup with the left and right speaker at +/-30° from the center speaker. The left-surround and right-surround speaker are placed at +/-120°. The distance to the midpoint of this setup is 3.5 m. The sixth loudspeaker is placed 1.35 m from the midpoint and 150° to the right side of the setup. Loudspeakers of type Geithain R906 are used.</p> <p>The BRIRs are measured at nine positions within the loudspeaker setup. For each position the artificial head is turned 360° in the horizontal plane with a step size of 5°. Next to the midpoint position of the setup, frontal, backward, and lateral positions are measured. The covered plane size is 4m x 4m. The figure 'scheme.png' gives an overview about the names and setup (included in the download of the dataset).</p> <p>A TV studio at the Technische Universität Ilmenau is used as room for the measurements. The figure 'studio.jpg' (included in the download of the dataset) shows the room in a prior configuration as a TV studio (the left wall as scenery setting is removed). The setup is build up in the left side of the room (gray floor area). The room has a total size of 19.5m x 11.5m x 5.5m. The reverberation time (RT60 from 80 Hz to 18 kHz) is approx. 0.7 s. The C50 and C80 are 15dB and 17dB.</p> <p>The BRIR dataset is used for the synthesis of new BRIRs at diffferent positions in the room using methods described and evaluated in [1] and [2]. Please feel free to use the measured BRIRs for your research and your project! </p> <p>---<br> [1] Brandenburg, K., Cano, E., Klein, F., Köllmer, T., Lukashevich, H., Neidhardt, A., Sloma, U., and Werner, S., “Plausible Augmentation of Auditory Scenes Using Dynamic Binaural Synthesis for Personalized Auditory Realities”, to be published in Proc. of: Conference of the Audio Engineering Society (AES) Audio for Virtual and Augmented Reality, USA, 2018.</p> <p><br> [2] Werner, S., Neidhardt, A., Klein, F., and Brandenburg, K., “Comparison of Different Methods to Create an Interactive Augmented Auditory Reality Scenario Using Sparse Binaural Room Impulse Response Measurements”, in Proc. of DAGA 2018, Garching, Germany, 2018.</p> <p> </p>
Dataset used in "What drives the Bitcoin price? A Factor Augmented Error Correction Mechanism investigation"
<p><strong>What drives the </strong><strong>Bitcoin price</strong><strong>? A Factor Augmented Error Correction Mechanism investigation</strong></p> <p>This article aims to determine what drives the price of Bitcoin. To achieve this aim, a large set of data is analysed using VEC models augmented by factors representing unobservable economic forces. They have been obtained by means of principal component analysis. This method enables us to contribute to the existing literature on Bitcoin in two ways. First, we employ the dimension reduction technique to combine variables from several papers. Second, we estimate several unobservable economic concepts instead of utilizing proxy variables as is usually done. We find that the main factor driving the Bitcoin price is its popularity. Hence, our result not only confirms some previous findings but reinforces them by providing a better definition of popularity. Finally, we conclude that the Bitcoin price is not affected by supply and demand factors in the way that is natural for conventional currencies. </p>
Actinidia chinensis cv. 'Hayward' Flower Dataset 2024 (augmented version)
<p>This dataset is an augmented version of an existing dataset (<a href="../doi/10.5281/zenodo.10790392">10.5281/zenodo.13692223</a>), which comprises images annotated according to Actinidia flower gender. The images were acquired in Quinta do Carrazedo (41.32170534304873, -8.661668130688255) with an iPhone Xr with a single camera and a resolution of 640x640 pixels. The dataset includes images of female and male flowers of <em>Actinidia chinensis</em> cv. 'Hayward'.</p> <p>The augmented images and their annotations are stored in an archive with the following structure:</p> <p><em>main_directory/</em></p> <ol> <li><em>annotations/</em> <ol> <li><em>pascalvoc/</em> <ul> <li>PascalVOC annotation files</li> <li>...</li> </ul> </li> <li><em>yolo/</em> <ul> <li>YOLO annotation files</li> <li>...</li> </ul> </li> </ol> </li> <li><em>images/</em> <ul> <li>Image files</li> <li>...</li> </ul> </li> </ol> <p><br>Each annotation file links to its image by the file name, so if an image is named <strong>"TL_Actinidia_Flower_2024_*_<em>type_of_augmentation</em>.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"TL_Actinidia_Flower_2024_*_<em>type_of_augmentation</em>.xml"</strong> (for PascalVOC format) and <strong>"TL_Actinidia_Flower_2024_*<em>type_of_augmentation</em>.txt"</strong> (for YOLO format) where in the place of the asterisks are numbers.</p> <p>Also, the subsets that were used to train, validate and test some deep learning models are available in three .TXT files (train.txt, val.txt and test.txt), where each file line corresponds to an image and annotation name.</p> <p> </p>
Dataset and models from: Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism
<p>Scripts, related code, files, and dataset for the paper: Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism.</p>
Dataset for damage-augmented digital twins towards the automated inspection of buildings
<p>This repository contains the dataset used for computing damage augmented digital twins for buildings via image-based approach. The method that uses this data set was presented in the paper "Damage-augmented digital twins towards the automated inspection of buildings" by Pantoja-Rosero et., al. (2023)" https://doi.org/10.1016/j.autcon.2023.104842</p> <p> </p> <p> </p> <p> </p>
Supplementary dataset for paper: "Approximate non-linear model predictive control with safety-augmented neural networks"
<p>Supplementary dataset for paper Henrik Hose and Johannes Koehler and Melanie N. Zeilinger and Sebastian Trimpe "Approximate non-linear model predictive control with safety-augmented neural networks".</p> <p>The code to use this dataset is publicly available at <a href="https://github.com/hshose/soeampc">https://github.com/hshose/soeampc</a></p> <p>The dataset contains training and testing data to train an NN controller for three standard benchmark systems, a stir tank reactor, a quadcopter, and a chain mass system.</p> <p>For each system, there are initial conditions as comma separated value in the `x0.txt` file, the MPC input trajectory in the `U.txt` file and the corresponding predicted state sequence in the `X.txt` file. MPC parameters are provided for each system. The dataset was computed using acados for SQP solving.</p> <p>The dataset also contains pretrained neural network approximations of the dataset.These are provided in the `pretrained_models.zip` file. The neural networks were trained with tensorflow.</p>
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning: W+jet training/test dataset
<p>W+jet events at generator and reconstruction level, used to train analysis-specific generative models.</p> <p>Events are represented as an array of relevant high-level features. Reco objects are matched to Gen objects and a minimal selection is applied to define the generator support in the N-dim space identified by the input features.</p> <p>About 2M events, used for training/validation/testing</p> <p>Details in https://arxiv.org/abs/2010.01835</p>
Dataset construction method of cross-lingual summarization based on filtering and text augmentation
<p>The NCLS dataset is provided by its authors (Zhu et al.): https://drive.google.com/file/d/1GZpKkHnTH_1Wxiti0BrrxPm18y9rTQRL/view. We work on the train set, validation set, and manually corrected test set.</p>
A Dataset and Machine Learning Approach to Classify and Augment Interface Elements of Household Appliances to Support People with Visual Impairment
<p>Here, we provide a dataset of images of interfaces from household appliances, where all interface elements are labled with one of five different types of interface elements. Further, we provide auxillary materials to use and extend the dataset.</p>
Data Augmentation Dataset
Open the record for dataset details and reuse information.
Everyday Augmented Conversation Videos Dataset (to delete)
<p>This dataset builds on the <a href="https://zenodo.org/deposit/6901792">Everyday Conversation Videos Dataset</a> and includes augmented variants of those videos. The 160 videos in this dataset all depict about a minute long everyday conversations. For these, four different actors (A1-A4) acted out a set of eight scenarios (S1-S8) Here, the videos have been cropped, trimmed, and standardized to 720p at 30fps. We then added Snapchat filters to the videos by routing them through Snap Camera. Each video is then available in one of five filter conditions, where:</p> <ul> <li>F0: No filter added</li> <li>F01: Beautification filter</li> <li>F02: Subtle beautification filter</li> <li>F03: Face and body tattoo filter</li> <li>F1-F8: Scenario-specific filters</li> </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.