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358 results for “dataset generation”
Two datasets with user generated audio recordings
<p>We provide two open access datasets of <strong>user generated audio recordings </strong>captured with mobile devices such as smartphones and portable cameras . The provided audio files originate from two different public events, a <strong>musical concert</strong> and a <strong>football match</strong>. Also, for each event, we provide two different types of collections; the original <strong>unorganized</strong> collection of uncompressed audio files and an additional <strong>organized</strong> collection, where the different recordings corresponding to similar parts of the event are grouped into specific folders and time-aligned so they can be played back in unison. The interested researcher is invited to read the accompanying paper "<em>Two open access datasets of user generated audio recordings</em>" for finding out more details about these datasets and the way that they can be useful in the context of research related to the organization and reproduction of user generated content.</p>
An Open Access User Generated Video Dataset from 2016 Edinburgh Festival
<p>A user generated video dataset captured during the 2016 Edinburgh festival. The provided dataset was collected using a smart phone and is available with no post-processing. The videos mainly cover the Edinburgh streets and the festival atmosphere, and do not cover any performances. The dataset can be used for evaluation of various research tools, such as video quality assessment and enhancement.</p>
An urban traffic dataset composed of visible images and their semantic segmentation generated by the CARLA simulator
<p><strong>If you use this dataset please cite this paper: Rosende, S.B.; Gavilán, D.S.J.; Fernández-Andrés, J.; Sánchez-Soriano, J. An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator. <em>Data</em> 2024, <em>9</em>, 4. <a href="https://doi.org/10.3390/data9010004">https://doi.org/10.3390/data9010004</a></strong></p> <p>A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. The images have been generated using the CARLA simulator (but would be like those that could be obtained with fixed aerial cameras or by using AUVs) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.</p>
Generation of a network slicing dataset: the foundations for AI-based B5G resource management
<p><span>This paper introduces a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and other data-based resource management and network performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the RAN infrastructure.</span></p> <p><span>Each sample consists of pairs of (network scenario, performance metrics). The network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of realistic scenarios without violating SLAs.</span></p> <p><span>This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.</span></p>
Dataset generated and/or analyzed in the paper "Volcanic unrest after the 2021 eruption of La Palma"
<p>Data generated and/or analyzed in the paper "Volcanic unrest after the 2021 eruption of La Palma" by Jose Fernandez, Joaquin Escayo, Juan F. Prieto, Kristy F. Tiampo, Antonio G. Camacho, and Eumenio Ancochea, submitted to Geophysical Research Letters. Also readme files are included describing the data files.</p>
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
<p>This dataset includes measured photovoltaic (PV) power generation data and on-site weather data collected from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using Brick schema was developed, which simplifies the data comprehension and the development of smart analytics applications. The detailed Brick model is stored in the .ttl file format, which can be accessed for retrieving metadata through the use of SPARQL queries.This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.</p>
Next-Generation Sequencing Dataset of Adult Pilocytic Astrocytomas
<p>Next-Generation Sequencing Dataset of Adult Pilocytic Astrocytomas</p> <p>Pilocytic astrocytoma (PA) is a benign grade 1 glioma according to the World Health Organization (WHO), common in children but rare in adults, where it may have a worse prognosis. Pediatric PA is usually associated with dysregulation of the MAPK pathway, often involving BRAF alterations such as the KIAA1549::BRAF (K-B) fusion or the V600E mutation. This dataset contains molecular data of 28 cases of adult PA obtained by using gene-targeted next-generation sequencing (NGS).</p>
LLM Generated Synthetic Dataset of DoS Exposed Solidity Contracts
<p>This dataset provides the replication package for the paper 'Large Language Models for Synthetic Dataset<br>Generation: A Case Study on Ethereum Smart Contract DoS Vulnerabilities' accepted for publication at the 8th International Workshop on Blockchain Oriented Software Engineering. The provided sources encompass:<br>1) The synthetic contracts (Vulnerable, Exploit, and Patched contract for each use case) generated by Claude and GPT4.<br>2) The configuration files of the hardhat-based testing environment.<br>3) The test suite that showcases the vulnerabilities of the generated contracts (including mock contracts) (hardhat is required to run and test contracts).<br><br></p> <div> <p> </p> </div>
A Subset of HTP-MD dataset used for training different generative models
Open the record for dataset details and reuse information.
PrimeKGQA, the dataset from paper: Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset
<p>Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total,<br>we create 83999 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA system.</p>
Monthly Hydropower Generation Dataset for Western Canada
<p>The presented dataset contains the following simulation-based monthly hydropower generation data for 110 facilities in British Columbia and Alberta, to support Western-US interconnect grid system studies:<br>1) Monthly hydropower generation estimates<br>2) Monthly hydropower flexibility metrics (minimum and maximum hourly generation and daily fluctuations)</p> <p>The hydropower generation estimates are provided with reference to the facility list that contains the corresponding metadata for each facility.</p> <p>For more details, please refer to Son, Y., Bracken, C., Broman, D. et al. Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies. <em>Sci Data</em> <strong>12</strong>, 874 (2025). <a href="https://doi.org/10.1038/s41597-025-05098-2" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-05098-2</a>.</p> <p>Corresponding author(s): Youngjun Son (youngjun.son@pnnl.gov) and Nathalie Voisin (nathalie.voisin@pnnl.gov)</p> <p>For data reproduction, please see the GitHub repository at <a title="tgw-hydro-canada" href="https://github.com/GODEEEP/tgw-hydro-canada" target="_blank" rel="noopener">https://github.com/GODEEEP/tgw-hydro-canada</a>.</p> <h1>Hydropower Facility List</h1> <p>The file, <code><strong>CAN_hydropower_facilities&scaling.csv</strong></code>, provides essential information on 146 hydropower facilities in British Columbia and Alberta, derived from <a title="Renewable Energy Power Plants, 1 MW or more, by Energy Source" href="https://www.eia.gov/trilateral/#!/maps" target="_blank" rel="noopener">Renewable Energy Power Plants, 1 MW or more, by Energy Source</a> by North American Cooperation on Energy Information (NACEI). Additionally, the facility information has been updated with corresponding <a title="National Hydrographic Network (NHN) Work Units" href="https://open.canada.ca/data/en/dataset/a4b190fe-e090-4e6d-881e-b87956c07977">National Hydrographic Network (NHN) Work Units</a>, global reservoir and lake database (<a title="GRanD: Global Reservoirs and Dams Database" href="https://www.globaldamwatch.org/grand" target="_blank" rel="noopener">GRanD: Global Reservoirs and Dams Database</a> and <a title="HydroLAKES" href="https://www.hydrosheds.org/products/hydrolakes" target="_blank" rel="noopener">HydroLAKES</a>), diversion intake flow rates based on water license information (hydropower), and so on. Below are the descriptions for each column in the facility metadata:</p> <ul> <li><em>fid</em>: Facility id according to NACEI data. New four-digit id starting with '9' are assigned for facilities with no fid in NACEI data</li> <li><em>Facility</em>: Name of the facility</li> <li><em>X</em>: Longitude of the facility's powerhouse</li> <li><em>Y</em>: Latitude of the facility's powerhouse</li> <li><em>Province</em>: Province where the facility is located</li> <li><em>Hydro_MW</em>: Nameplate capacity of the facility</li> <li><em>NHN_Work_U</em>: Associated NHN Work Units</li> <li><em>GRanD_ID</em>: Associated reservoir id from the GRanD dataset</li> <li><em>HydroLAKES_ID</em>: Associated lake id from the HydroLAKES dataset</li> <li><em>GINDEX</em>: Grid id from the mosartwmpy Canada model</li> <li><em>GINDEX_CONUS</em>: Grid id from the mosartwmpy CONUS model, used for facilities in the Columbia River Basin</li> <li><em>Basin_Note</em>: Indicator for facilities located in the Columbia River Basin or outside of the meteorological forcing domain of the perturbed thermodynamics simulations</li> <li><em>WECC_ADS_2032</em>: Indicator for facilities without the WECC ADS 2032 reference hydropower generation data</li> <li><em>Intake_Flow_Rate</em>: Diversion intake flow rates based on hydropower water license information</li> <li><em>Type</em>: Type of facility</li> <li><em>Water_License</em>: Link to the source of water license information</li> <li><em>Scaling</em>: Annual total scaling factor (total hydropower generation / total streamflow volume for 2008)</li> <li><em>Scaling_IntakeCap</em>: Annual total scaling factor, constrained by intake flow rates from hydropower water license (total hydropower generation / total streamflow volume not exceeding intake flow rate constraint for 2008)</li> </ul> <p>Among the 146 hydropower facilities listed, only 110 facilities, which are within the applied meteorological forcings domain and have reference hydropower generation data, are considered for monthly hydropower generation estimates.</p> <h1>Monthly Hydropower Generation Estimates and Flexibility Metrics</h1> <p>Each file contains a monthly timeseries dataset (rows: monthly timestamps) from 1981 to 2019 for 110 facilities (columns: <em>Facility</em> listed in <strong><code>CAN_hydropower_facilities&scaling.csv</code>).</strong></p> <ol> <li><code><strong>CAN_hydropower_monthly_generation_MWh.csv</strong></code>: monthly total hydropower generation in MWh</li> <li><code><strong>CAN_hydropower_monthly_p_min_MW.csv</strong></code>: monthly flexibility metric of minimum generation capacity in MW</li> <li><code><strong>CAN_hydropower_monthly_p_max_MW.csv</strong></code>: monthly flexibility metric of maximum generation capacity in MW</li> <li><code><strong>CAN_hydropower_monthly_p_ador_MW.csv</strong></code>: monthly flexibility metric of the daily operation range in MW</li> </ol> <h1>Update Log</h1> <p><strong>- V</strong><strong>ersion 1.1.0</strong>: "Scaling" and "Scaling_IntakeCap" colums have been added to <strong>Hydropower Facility List</strong>, and the file for hydropower facilities has been renamed from <code><strong>CAN_hydropower_facilities.csv</strong></code> to <code><strong>CAN_hydropower_facilities&scaling.csv</strong></code>.</p> <h1>Funding Acknowledgements</h1> <p>This work was supported under the Laboratory Directed Research and Development (LDRD) Program (Project # 79583) at the Pacific Northwest National Laboratory (PNNL).</p> <p>The PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the U.S. Department of Energy (DOE) under Contract No. DE-AC05-76RL01830.</p> <h1>Disclaimer</h1> <p>The presented dataset aims to support robust, long-term power system planning under diverse water conditions. However, it should not be used to assess hydropower generation during extreme flood events when facilities may need to be disconnected from power grids due to dam safety and potential loss of control that could propagate into grid instability. Similarly, the dataset should not be utilized for unprecedented drought conditions where reservoir levels may fall below critical power pool levels. Furthermore, evolving water policies, including the Columbia River Treaty, can alter seasonal and monthly hydrological patterns. It is important to note that our hydropower generation dataset, which is derived based on Year 2008, does not account for any historical and future changes in environmental regulations, water management, or water policies.</p> <p>The dataset was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor the U.S. Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof, or Battelle Memorial Institute.</p>
Dataset of "Critical role of H-aggregation for high-efficiency photoinduced charge generation in pristine pentamethine cyanine salts"
<p>Dataset underpinning the published article:</p> <p>Critical role of H-aggregation for high-efficiency photoinduced charge generation in pristine pentamethine cyanine salts Phys. Chem. Chem. Phys. 2021, 23, 23886-23895. DOI: 10.1039/D1CP03251H</p>
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </p>
Podcast annotation dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts "
<p>Dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts". Please refer to the paper for details. Compared to the dataset used in the paper, 20 out of the 417 episodes have been removed due to copyright issues. </p> <p>The data file contains the following fields:</p> <p>- "episode_intro_start": the time stamp for episode introduction start (in milliseconds)</p> <p>- "episode_intro_end": the time stamp for episode introduction end (in milliseconds)</p> <p>- "program_intro_start": the time stamp for program introduction start (in milliseconds)</p> <p>- "program_intro_end": the time stamp for program introduction end (in milliseconds)</p> <p>- "program_name": name of the podcast program</p> <p>- "episode_name": name of the podcast episode</p> <p>- "transcription": JSON string containing the transcription, including the timestamps.</p> <p>- "annotator": anonymized annotator ID.</p>
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset
<p>Jets at generator and reconstruction level saved in .npy format.</p> <p>Each jet is represented as an array of jet constituents characterized by their particle momentum in Cartesian coordinates, i.e., (px, py, pz). For both generator-level and reconstruction-level jets, jet constituents are ordered by decreasing pT.</p> <p>The shape of the datasets is [N, 50, 3], where N is the total number of jets, 50 is the number of particles per jet, and 3 is the number of particle features (in order): [px, py, pz].<br> About 1.7M jets split into training, validation and testing sets at 60%, 20% and 20% respectively.</p>
deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques
<p>In this paper, we present datasets that can be utilised for synthetic near infrared (NIR) image and bounding box level fruit detection system. It is undeniable fact that high-caliber machine learning software frameworks such as Tensorflow or Pytorch and large scale dataset such as ImageNet and COCO, and accelerated GPU hardware support have pushed the limit of machine learning for more than decades.</p> <p>Among these breakthroughs quality dataset is one of important key building blocks that can lead to success in model generalisation and deployment for data-driven deep neural networks. Particularly, synthetic data generation such as generative adversarial networks often requires relatively larger scale data than other supervised approaches. In addition, posing constrains such as geometrical facial constrains in fake face generation or consistent and radiometrically calibrated reflectances from satellite imagery commonly yield better results. We share NIR+RGB dataset that are re-processed from other two public datasets (nirscene and SEN12MS) and our own novel sweetpepper dataset to be able to timely adopt to other following studies.</p> <p>We oversampled from original nirscene dataset at 10, 100, 200, and 400 ratios and total of 127k pair of images. For SEN12MS satellite multispectral dataset, we selected one largest subset; Summer (45k) and All seasons (180k). Our sweetpeppr dataset consists of 1,615 pairs of NIR+RGB images. We demonstrate these NIR+RGB datasets are sufficient to be used for synthetic NIR generation quantitatively and qualitatively. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweetpepper dataset respectively.</p> <p>We also release <em>11</em> fruits' bounding box annotations that can be exported as various formats using cloud service. 4 newly added fruits [blueberry, cherry, kiwi, and wheat] compounds 11 novel bounding box dastaset together with our previous work in deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances is 162k and all bounding box dataset is ready for use from cloud service. For evaluation of these dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.5:0.95] results of [min:0.49, max:0.812]. We hope these dataset is useful and serves as one of baseline for the following up studies.</p>
Textomics: A Dataset for Genomics Data Summary Generation
<p>This is the dataset of our ACL 2022 paper:</p> <p>Textomics: A Dataset for Genomics Data Summary Generation.</p> <p>Please read the "readme.md" in it for the format of the dataset.</p>
Anytown dataset generated with DHALSIM
<p>Simulations of the Anytown water distribution system under normal operating conditions, non disruptive network anomalies, and disruptive network anomalies or 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>
HDF5 datasets and python scripts to generate figures in "Butterfly distribution of relativistic electrons driven by parallel propagating lower band whistler chorus waves"
<p>HDF5 datasets and python scripts to generate figures in "Butterfly distribution of relativistic electrons driven by parallel propagating lower band whistler chorus waves"</p> <p>RBW simulation datasets in HDF5 format:</p> <ul> <li>300pT.h5 The particle dataset to generate the figures.</li> </ul> <p>Python scripts to generate figures in the manuscript.</p> <p>- Environment: Python 3.6.7 :: Anaconda 4.4.0 (64-bit)</p> <p>- Required modules: matplotlib, numpy, h5py</p> <ul> <li>Figure1.py Generate figure 1.</li> <li>Figure2.py Generate figure 2.</li> <li>Figure3.py Generate figure 3.</li> <li>Figure4.py Generate figure 4.</li> <li>QLDe.py Calculate bounce averaged diffusion coefficients according to Shprits et al. (2006) (doi: https://doi.org/10.1029/ 2006JA011725).</li> </ul> <p> </p>
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
<p>We propose the end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidences and predicting a truthfulness label (i.e., support, refute and not enough information), and to generate a rationalization statement to explain the reasoning and ruling process. To support this research, we construct MOCHEG, a large-scale dataset consisting of 21,184 claims where each claim is annotated with a truthfulness label and ruling statement, with 43,148 text evidences and 15,373 image evidences. </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.