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358 results for “dataset generation”
Dataset including records from the first two generation of selection for feed efficiency in three rabbits lines
<p>This dataset include records from the first two generation of selection of three rabbit lines. The objective was to improve feed efficiency accounting for social interactions within cage. In two lines the feed intake control is done using an electronic feeder, and in the third feed intake is controled at cage level. In the excel book provided, the date from the different lines are recorded in different pages.</p> <p>This dataset has been generated within Feed-a-Gene (H2020) and GENEF (Spanish INIA) projects. It is presented to fullfit the requeriments of Milestone 22 of Feed-a-Gene. This milestone basically aims to show that a selection experiment for feed efficiency on rabbits, accounting for social interactions, can be operationally conducted using an electronic feeder to record individual feed intake of animals raised in groups. This device has also been developed within the frame of these two projects.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset for IJCAI 2019 paper, Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks
<p>The dataset and pre-trained model for IJCAI 2019 paper "Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks"</p> <ul> <li>Pre-trained model(Pytorch): DSPNet_G_200_epochs.pth</li> <li>Dataset (generated from coco dataset): starry_night_coco.zip</li> <li>Github: https://github.com/jinningli/DSP-Net</li> </ul> <p>Please cite our paper if you are using this dataset:</p> <p><em>Jinning Li, and Yexiang Xue. Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks. In International Joint Conference on Artificial Intelligence (IJCAI) 2019</em></p> <p>Abstract:</p> <p><em>We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles. In scribble-to-painting transformation, a neural net has to infer additional details of the image, given relatively sparse information contained in the outlines of the scribble. Therefore, it is more challenging than classical image style transfer, in which the information content is reduced from photos to paintings. Inspired by the human cognitive process, we propose a multi-task generative adversarial network, which consists of two jointly trained neural nets -- one for generating artistic images and the other one for semantic segmentation. We demonstrate that joint training on these two tasks brings in additional benefit. Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. In addition, we publish a large dataset for scribble-to-painting transformation.</em></p>
Pulse Voltage Response Generation: TBSI-Lijing Battery Dataset
<p>In the retired batteries sustainable utilization scenario, preprocessing steps such as capacity grading and consistency matching is essential to determine how a battery should be reused or recycled. The conventional approach of measuring battery state of health (SOH), charge-discharge profiles, and other related properties through long-time charge-discharge cycle is both time-consuming and energy-intensive. Therefore, developing rapid, non-invasive, and sustainable preprocessing methods for randomly retired batteries is crucial. However, actual measured data in this field are very limited, both in terms of the quantity of retired batteries and the diversity of battery chemistries and materials. To address this gap, we open-source this Pulse Voltage Response Generation: TBSI-Lijing Battery Dataset to foster further academic research and industrial applications in the field of battery SOH fast estimation and consistency fast assessment. Xiamen Lijing New Energy Technology Co., Ltd., collected this dataset. The collaboration team at Tsinghua Berkeley Shenzhen Institute (TBSI) processed this dataset and utilized generative models for data augmentation, significantly enhancing the economic feasibility of large-scale battery repurposing.</p>
Dataset for "High power single crystal KTA optical parametric amplifier for efficient 1.4–3.5 µm mid-IR radiation generation"
<p>The dataset represents the experimental data for publication "High power single crystal KTA optical parametric amplifier for efficient 1.4–3.5 µm mid-IR radiation generation".</p>
Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072
<p>Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072.</p> <p>VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs </p> <p>EC_LCG_bits.txt: bits from the linear congruential generator on elliptic curves</p> <p>LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits</p> <p>VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs</p> <p> </p>
Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
<p>This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". </p> <p>Source Data Raw.zip has the entire data set used to generate the images.</p> <p>Source Data.zip contains the processed data from "Source Data Raw.zip". </p> <p>Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.</p> <p>For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.</p>
Designed and generated Robot and Furniture datasets
<p>This dataset is a public data set designed to promote the application of artificial intelligence technology in reverse engineering. The dataset contains mesh objects with different parameters that are automatically generated from the manually designed CAD model database, and the labels are the specific parameter sizes corresponding to these objects. Researchers can read these objects and generate different variants for different tasks.</p>
EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation
<p>EMOPIA (pronounced ‘yee-mò-pi-uh’) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in <strong>pop piano music</strong>, to facilitate research on various tasks related to music emotion. The dataset contains <strong>1,087</strong> music clips from 387 songs and <strong>clip-level</strong> emotion labels annotated by four dedicated annotators. </p> <p>For more detailed information about the dataset, please refer to our paper: <a href="https://arxiv.org/abs/2108.01374"><strong>EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation</strong></a>. </p> <p><strong>File Description</strong></p> <ul> <li><em><strong>midis/</strong></em>: midi clips transcribed using GiantMIDI. <ul> <li>Filename `Q1_xxxxxxx_2.mp3`: Q1 means this clip belongs to Q1 on the V-A space; xxxxxxx is the song ID on YouTube, and the `2` means this clip is the 2nd clip taken from the full song.</li> </ul> </li> <li><em><strong>metadata/</strong></em>: metadata from YouTube. (Got when crawling)</li> <li> <p><em><strong>songs_lists/</strong></em>: YouTube URLs of songs.</p> </li> <li> <p><em><strong>tagging_lists/</strong></em>: raw tagging result for each sample.</p> </li> <li> <p><em><strong>label.csv</strong></em>: metadata that records filename, 4Q label, and annotator.</p> </li> <li> <p><em><strong>metadata_by_song.csv</strong></em>: list all the clips by the song. Can be used to create the train/val/test splits to avoid the same song appear in both train and test.</p> </li> <li> <p><em><strong>scripts/prepare_split.ipynb:</strong></em> the script to create train/val/test splits and save them to csv files.</p> </li> </ul> <p>------</p> <p><strong>2.2 Update</strong></p> <ul> <li>Add tagging files in <em><strong>tagging_lists/</strong></em> that are missing in the previous version.</li> <li>Add <em><strong>timestamps.json</strong></em> for easier usage. It records all the timestamps in dict format. You can see <em><strong>scripts/load_timestamp.ipynb</strong></em> for the format example.</li> <li>Add <em><strong>scripts/timestamp2clip.py</strong></em>: After the raw audio are crawled and put in <em><strong>audios/raw</strong></em>, you can use this script to get audio clips. The script will read <em><strong>timestamps.json</strong></em> and use the timestamp to extract clips. The clips will be saved to <em><strong>audios/seg</strong> </em>folder.</li> <li>remove 7 midi files that were added by mistake, and also corrected the number in <em><strong>metadata_by_song.csv</strong></em>.</li> </ul> <p> </p> <p><strong>2.1 Update</strong></p> <p>Add one file and one folder:</p> <ul> <li><em><strong>key_mode_tempo.csv</strong></em>: key, mode, and tempo information extracted from files.</li> <li><strong><em>CP_events/</em></strong>: CP events used in our paper. Extracted using this <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/representations/uncond/cp/corpus2events.py">script</a>, and add the emotion event to the front.</li> </ul> <p>Modify one folder:</p> <ul> <li>The <strong><em>REMI_events/</em></strong> files in version 2.0 contain some information that is not related to the paper, so remove it.</li> </ul> <p> </p> <p><strong>2.0 Update</strong></p> <p>Add two new folders:</p> <ul> <li><strong><em>corpus/</em></strong>: processed data that following <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/Dataset.md">the preprocessing flow</a>. (Please notice that although we have <code>1078</code> clips in our dataset, we lost some clips during steps 1~4 of the flow, so the final number of clips in this <strong><code>corpus</code></strong> is <code>1052</code>, and that's the number we used for training the generative model.)</li> <li><strong><em>REMI_events/</em></strong>: REMI event for each midi file. They are generated using this <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/representations/uncond/remi/corpus2events.py">script</a>.</li> </ul> <p>-------- </p> <p> </p> <p> </p> <p> </p> <p><strong>Cite this dataset</strong></p> <pre><code>@inproceedings{{EMOPIA}, author = {Hung, Hsiao-Tzu and Ching, Joann and Doh, Seungheon and Kim, Nabin and Nam, Juhan and Yang, Yi-Hsuan}, title = {{MOPIA}: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation}, booktitle = {Proc. Int. Society for Music Information Retrieval Conf.}, year = {2021} }</code></pre>
Dataset for Generation of multiple true false questions
<p><strong>Generation of multiple true-false questions</strong></p> <p>This project provides a Natural Language Pipeline for processing German Textbook sections as an input generating Multiple True-False Questions using GPT2.</p> <p>Assessments are an important part of the learning cycle and enable the development and promotion of competencies. However, the manual creation of assessments is very time-consuming. Therefore, the number of tasks in learning systems is often limited. In this repository, we provide an algorithm that can automatically generate an arbitrary number of German True False statements from a textbook using the GPT-2 model. The algorithm was evaluated with a selection of textbook chapters from four academic disciplines (see `data` folder) and rated by individual domain experts. One-third of the generated MTF Questions are suitable for learning. The algorithm provides instructors with an easier way to create assessments on chapters of textbooks to test factual knowledge.</p> <p>As a type of Multiple-Choice question, Multiple True False (MTF) Questions are, among other question types, a simple and efficient way to objectively test factual knowledge. The learner is challenged to distinguish between true and false statements. MTF questions can be presented differently, e.g. by locating a true statement from a series of false statements, identifying false statements among a list of true statements, or separately evaluating each statement as either true or false. Learners must evaluate each statement individually because a question stem can contain both incorrect and correct statements. Thus, MTF Questions as a machine-gradable format have the potential to identify learners’ misconceptions and knowledge gaps.</p> <p>Example MTF question:</p> <blockquote> <p>Check the correct statements:</p> <p>[ ] All trees have green leafs.</p> <p>[ ] Trees grow towards the sky.</p> <p>[ ] Leafes can fall from a tree.</p> <p> </p> </blockquote> <p><strong>Features</strong></p> <p>- generation of false statements</p> <p>- automatic selection of true statements</p> <p>- selection of an arbitrary similarity for true and false statements as well as the number of false statements</p> <p>- generating false statements by adding or deleting negations as well as using a german gpt2</p> <p><strong>Setup</strong></p> <p><strong>Installation</strong></p> <p>1. Create a new environment: `conda create -n mtfenv python=3.9`</p> <p>2. Activate the environment: `conda activate mtfenv`</p> <p>3. Install dependencies using anaconda:</p> <p>```</p> <p>conda install -y -c conda-forge pdfplumber</p> <p>conda install -y -c conda-forge nltk</p> <p>conda install -y -c conda-forge pypdf2</p> <p>conda install -y -c conda-forge pylatexenc</p> <p>conda install -y -c conda-forge packaging</p> <p>conda install -y -c conda-forge transformers</p> <p>conda install -y -c conda-forge essential_generators</p> <p>conda install -y -c conda-forge xlsxwriter</p> <p>```</p> <p>3. Download spacy: `python3.9 -m spacy download de_core_news_lg`</p> <p><strong>Getting started</strong></p> <p>After installation, you can execute the bash script `bash run.sh` in the terminal to compile MTF questions for the provided textbook chapters.</p> <p>To create MTF questions for your own texts use the following command:</p> <p>`python3 main.py --answers 1 --similarity 0.66 --input ./<path>/<to>/<your>/<textbook>.txt`</p> <p>The parameter `answers` indicates how many false answers should be generated.</p> <p>By configuring the parameter `similarity` you can determine what portion of a sentence should remain the same. The remaining portion will be extracted and used to generate a false part of the sentence.</p> <p> </p> <p><strong>## History and roadmap </strong></p> <p>* Outlook third iteration: Automatic augmentation of text chapters with generated questions</p> <p>* Second iteration: Generation of multiple true-false questions with improved text summarizer and German GPT2 sentence generator</p> <p>* First iteration: Generation of multiple true false questions in the Bachelor thesis of Mirjam Wiemeler</p> <p> </p> <p><strong>Publications, citations, license</strong></p> <p><strong>Publications</strong></p> <ul> <li>Kasakowskij, R., Kasakowskij, T. & Seidel, N., (2022). Generation of Multiple True False Questions. In: Henning, P. A., Striewe, M. & Wölfel, M. (Hrsg.), 20. Fachtagung Bildungstechnologien (DELFI). Bonn: Gesellschaft für Informatik e.V.. (S. 147-152). DOI: [10.18420/delfi2022-026](https://dl.gi.de/handle/20.500.12116/38826)</li> </ul> <p> </p> <p><strong>Citation of the Dataset</strong></p> <ul> <li>Kasakowskij, R., Kasakowskij, T., & Seidel, N. (2022). <em>Dataset for Generation of multiple true false questions</em>. Zenodo. https://doi.org/10.5281/zenodo.7303300</li> </ul> <p>The source code and data are maintained at GitHub: https://github.com/D2L2/multiple-true-false-question-generation</p> <p><strong>Contact</strong></p> <ul> <li>Regina Kasakowskij (M.A.) - regina.kasakowskij@fernuni-hagen.de</li> <li>Dr. Niels Seidel - niels.seidel@fernuni-hagen.de</li> </ul> <p><strong>License</strong> Distributed under the MIT License. See [LICENSE.txt](https://gitlab.pi6.fernuni-hagen.de/la-diva/adaptive-assessment/generationofmultipletruefalsequestions/-/blob/master/LICENSE.txt) for more information.</p> <p><strong>Acknowledgments</strong> This research was supported by CATALPA - Center of Advanced Technology for Assisted Learning and Predictive Analytics of the FernUniversität in Hagen, Germany.</p> <p>This project was carried out as part of research in the CATALPA project [LA DIVA](https://www.fernuni-hagen.de/forschung/schwerpunkte/catalpa/forschung/projekte/la-diva.shtml)</p>
Huge variation in H2 generation during seawater alteration of ultramafic rocks: Dataset
<p>Dataset associated with the <em>Geochemistry, Geophysics, Geosystems</em> paper, "Huge variation in H2 generation during seawater alteration of ultramafic rocks"</p>
A Benchmark Dataset with Knowledge Graph Generation for Industry 4.0 Production Lines
<p>A benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how I4.0 industry can benefit from KGs and semantic datasets. This work is a result of collaborations with the production line managers, supervisors, and engineers of a football industry to acquire realistic production line data. Knowledge Graphs (KGs) or a Knowledge Graph (KG) emerged as a significant technology to store the semantics of the domain entities. The data is mapped and populated with RGOM classes and relations using an automated solution based on JenaAPI, producing an I4.0 KG. <br> <br> Usage:<br> <br> Recently, we use this dataset to analyze the performance of the five state-of-the-art KG embedding models, namely ComplEx, DistMult,TransE, ConvKB, and ConvE. We evaluated the models using two key metrics: Mean Reciprocal Rank (MRR), and Hits@N (Hits@10, Hits@3, and Hits@1). We observed that the TransE model outperforms other models, followed by ComplEx and DistMult, with ConvE demonstrating the lowest performance. Similarly, the dataset can be used alternatively in other potential scenarios.</p>
Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant
<p>The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA – Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers, or benchmarking mutation testing frameworks, and more applications are yet to be discovered. This dataset is a supplementary material for a paper entitled <a href="https://doi.org/10.3390/data8060109"><strong>Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant</strong></a> submitted to the <strong>MDPI Data</strong> journal.</p>
Datasets for manuscript - Dirichlet diffusion score model for biological sequence generation.
<p>This repository holds the trained Dirichlet Diffusion Score models for various datasets.</p> <p><strong>best_models.tar.gz</strong></p> <p>It also contains all input data required to train your own models with scripts provided via <a href="https://github.com/jzhoulab/ddsm">github repository</a>.</p> <p><strong>data.tar.gz</strong></p> <p>This archive contains the following folders: </p> <ul> <li><strong>satnet_sudoku </strong>contains dataset with sudoku examples which we used for evaluation of sudoku model.</li> <li><strong>promoter_design</strong> contains dataset used for training promoter design model as well as Sei model weights. Please, read provided readme file before using it for training scripts. </li> </ul>
C-Town dataset generated with DHALSIM
<p>Simulations of the C-Town water distribution system under normal operating conditions, and disruptive network anomalies, and cyber attacks. These simulations were run using the DHALSIM simulator. The dataset includes two types of physical data: ground truth and SCADA information. In addition, the dataset includes captures of all network packets seen by the PLCs and SCADA server during the simulation. DHALSIM is a co-simulation environment for Water Distribution Systems that combines EPANET and MiniCPS to generate more realistic simulations of water distribution systems.</p> <p> </p> <p>The dataset includes all configuration files required to replicate these results, using the DHALSIM simulator.</p> <p>DHALSIM Github link:https://github.com/afmurillo/DHALSIM/tree/master/dhalsim</p> <p>If you use this dataset, please cite the paper that was written alongside this dataset: https://ascelibrary.org/doi/abs/10.1061/JWRMD5.WRENG-5854 </p>
Carbon configurations dataset generated in "A systematic approach to generating accurate neural network potentials: the case of carbon"
<p>This dataset contains 60133 configurations of Carbon as generated in the paper "A systematic approach to generating accurate neural network potentials: the case of carbon". The configurations represent crystal structures containing only Carbon atoms, ranging from 16 to 200 atoms in the unit cell, with energy and forces calculated through density functional theory. Please refer to the original paper for more details about the generation procedure and simulation parameters.</p> <p>The dataset consists of a compressed archive containing a single .xyz file with all configurations, with lattice and energy information contained in the comment line, and one line per atom with positions and forces. Lengths are in Angstroms and energies in eV.</p>
Image dataset for GenerativeGI: Creating Generative Art with Genetic Improvement
<p>Full dataset of our results for our submission, "GenerativeGI: Creating Generative Art with Genetic Improvement," to the Special Issue on Genetic Improvement at the Automated Software Engineering journal. This zip archive contains the images and corresponding population data (including the genome that created each image) for all results reported in our submission.</p>
Dataset on PowerWorld Software Power Flow Calculations on an Underground Distribution Feeder for Inserting Renewable Distribution Generation from Biogas, Photovoltaic and Small Wind Sources
<p>This Dataset brings all the information, details and source files used for power flow studies of the USP-105 underground feeder of the distribution medium voltage network in the University of São Paulo campus, which has received several embedded DG sources, namely a biogas plant, photovoltaic units and a small wind turbine.</p> <p>The power flow simulations were realized using the PowerWorldTM Simulator, v.23</p> <p>The files types on the Dataset are: </p> <p>.pwb, .pwd and tsb: Powerworld software input files for the simulations</p> <p>.csv: where a semicolon symbol (;) is used as a column separator, while a dot symbol (.) represents the decimal separator. The first row of each CSV file corresponds to the header row to help identify data.</p>
Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions - Dataset and Results
<p>This dataset contains the replication package for the paper: Federico Formica, Tony Fan, and Claudio Menghi. 2023. "Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions".</p> <p>The dataset contains:</p> <ul> <li>The models used in the evaluation section.</li> <li>All the results obtained by running Athena-S on the models.</li> <li>The scripts that automatically analyze the results file and produce the tables and figures used in the paper.</li> </ul>
Dataset: Impact of lateral gap on flow distribution, backwater rise, and turbulence generated by a logjam
<p>This dataset includes flow measurements and wood accumulation characteristics of flume experiments conducted at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
NIFECG synthetic signals generated with fecgsym by PhysioNet: Dataset 1/2
<p>First part</p> <p>https://zenodo.org/records/8415709</p> <p>Second part</p> <p>https://zenodo.org/records/8429286</p> <p>Non-invasive fetal electrocardiogram (NIFECG) signals.</p> <p>Fetal's heart rate: 60 - 200 bpm</p> <p>Mother's heart rate: 65 - 120 bpm</p> <p>Sample frequency 1000 Hz</p> <p>8,008 signals in total</p> <p>Download the files and join them as follows:</p> <p>cat tmp2.tar_part* > nifecg_signals.tar</p> <p>Untar the file with the following command:</p> <p>tar xvf nifecg_signals.tar</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.