Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

ReSyRIS: Real-Synthetic Rock Instance Segmentation dataset

<pre># ReSyRIS The Real-Synthetic Rock Instance Segmentation dataset (ReSyRIS) is created for training and evaluation of rock segmentation, detection and instance segmentation in (quasi-)extra-terrestrial environments. It consists of a set of annotated, real images of rocks on a lunar-like surface, a precisely mimicked synthetic version thereof, and respective synthetic assets for training data generation. In the folders, you find the following structure: - `stone_models`: all 36 .obj files of the 3d reconstructed stones - `test_data_realworld`: the real world recordings with accompanying ground truth - `test_data_synthetic`: the synthetic renderings matching approximately the real world recordings, with accompanying ground truth - `oaisys`: config files for rendering synthetic training data with oaisys If you find this dataset useful for your work please consider citing our paper: https://elib.dlr.de/194113/. </pre>

opencc-by-nc-4.0Dec 2022View details →

SynthRAD2023 Grand Challenge dataset: synthetizing computed tomography for radiotherapy

DATASET STRUCTURE The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/10.5281/zenodo.7260705</a> and a detailed description is offered at "synthRAD2023_dataset_description.pdf". The training datasets for Task1 is in Task1.zip, while for Task2 in Task2.zip. After unzipping, each Task is organized according to the following folder structure: Task1.zip/ ├── Task1    ├── brain     ├── 1Bxxxx        ├── mr.nii.gz        ├── ct.nii.gz        └── mask.nii.gz     ├── ... └── overview        ├── 1_brain_train.xlsx        ├── 1Bxxxx_train.png       └── ...      └── pelvis     ├── 1Pxxxx        ├── mr.nii.gz        ├── ct.nii.gz        ├── mask.nii.gz    ├── ... └── overview        ├── 1_pelvis_train.xlsx        ├── 1Pxxxx_train.png        └── .... Task2.zip/ ├──Task2    ├── brain     ├── 2Bxxxx        ├── cbct.nii.gz        ├── ct.nii.gz        └── mask.nii.gz     ├── ... └── overview        ├── 2_brain_train.xlsx        ├── 2Bxxxx_train.png       └── ...        └── pelvis     ├── 2Pxxxx        ├── cbct.nii.gz        ├── ct.nii.gz        ├── mask.nii.gz ├── ... └── overview       ├── 2_pelvis_train.xlsx       ├── 2Pxxxx_train.png       └── .... Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below: [Task]    [Anatomy]    [Center]    [PatientID] 1    B        A        001 In each patient folder, three files can be found:  <ul> <li> ct.nii.gz: CT image  </li> <li> mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image </li> <li> mask.nii.gz:image containing a binary mask of the dilated patient outline  </li> </ul> For each task and anatomy, an overview folder is provided which contains the following files: <ul> <li> [task]_[anatomy]_train.xlsx: This file contains information about the image acquisition protocol for each patient. </li> <li> [task][anatomy][center][PatientID]_train.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data. </li> </ul> DATASET DESCRIPTION This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks. Data was collected at 3 Dutch university medical centers: <ul> <li> Radboud University Medical Center </li> <li> University Medical Center Utrecht </li> <li> University Medical Center Groningen </li> </ul> For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to. The following number of patients is available in the training set. Training <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> <td> 120 </td> <td> 0 </td> <td> 60 </td> <td> 180 </td> </tr> <tr> <td> Task 2 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> </tr> </tbody> </table> Each subset generally contains equal amounts of patients from each center, except for task 1 brain, where center B had no MR scans available. To compensate for this, center A provided twice the number of patients than in other subsets. Validation <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 20 </td> <td> 0 </td> <td> 10 </td> <td> 30 </td> </tr> <tr> <td> Task 2 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> </tr> </tbody> </table> Testing <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> <td> 40 </td> <td> 0 </td> <td> 20 </td> <td> 60 </td> </tr> <tr> <td> Task 2 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> </tr> </tbody> </table> In total, for all tasks and anatomies combined, 1080 image pairs (720 training, 120 validation, 240 testing) are available in this dataset. This repository only contains the training data. All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure). Data was acquired with the following scanners: <ul> <li> Center A: <ul> <li> MRI: Philips Ingenia 1.5T/3.0T </li> <li> CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT </li> <li> CBCT: Elekta XVI </li> </ul> </li> <li> Center B: <ul> <li> MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T </li> <li> CT: Siemens SOMATOM Definition AS </li> <li> CBCT: IBA Proteus+ or Elekta XVI </li> </ul> </li> <li> Center C: <ul> <li> MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T </li> <li> CT: Philips Brilliance Big Bore </li> <li> CBCT: Elekta XVI </li> </ul> </li> </ul> For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast. For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT. The following pre-processing steps were performed on the data: <ul> <li> Conversion from dicom to compressed nifti (nii.gz) </li> <li> Rigid registration between CT and MR/CBCT </li> <li> Anonymization (face removal, only for brain patients) </li> <li> Patient outline segmentation (provided as a binary mask) </li> <li> Crop MR/CBCT, CT and mask to remove background and reduce file sizes </li> </ul> The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics. ETHICAL APPROVAL Each institution received ethical approval from their internal review board/Medical Ethical committee: <ul> <li> UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”. </li> <li> UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”. </li> <li> Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”. </li> </ul> CHALLENGE DESIGN The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746020">https://doi.org/10.5281/zenodo.7746020</a>.

opencc-by-4.0Mar 2023View details →

RecurM test synthetic dataset

Test synthetic dataset containing 20 synthetic metagenomes for ground truth RecurM testing. Assembled using "metaspades --meta -k 21,33,55,77,99,127" via spades v3.15.2.

openother-ncJun 2023View details →

Parcel3D - A Synthetic Dataset of Damaged and Intact Parcel Images with 2D and 3D Annotations

Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the <a href="https://cocodataset.org/#format-data">COCO format</a>. For details see our <a href="https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Naumann_Parcel3D_Shape_Reconstruction_From_Single_RGB_Images_for_Applications_in_CVPRW_2023_paper.html">paper</a> and for visual samples our <a href="https://a-nau.github.io/parcel3d/">project page</a>. Relevant computer vision tasks: <ul> <li>bounding box detection</li> <li>classification</li> <li>instance segmentation</li> <li>keypoint estimation</li> <li>3D bounding box estimation</li> <li>3D voxel reconstruction</li> <li>3D reconstruction</li> </ul>   The dataset is for academic research use only, since it uses resources with restrictive licenses. For a detailed description of how the resources are used, we refer to our <a href="https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Naumann_Parcel3D_Shape_Reconstruction_From_Single_RGB_Images_for_Applications_in_CVPRW_2023_paper.html">paper</a> and <a href="https://a-nau.github.io/parcel3d/">project page</a>. Licenses of the resources in detail: <ul> <li><a href="https://research.google/resources/datasets/scanned-objects-google-research/">Google Scanned Objects</a>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> (for details on which files are used, see the respective meta folder)</li> <li><a href="https://zenodo.org/record/8041823">Cardboard Dataset</a>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></li> <li><a href="https://ieeexplore.ieee.org/abstract/document/8999123">Shipping Label Dataset</a>: <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a></li> <li>Other Labels: See file misc/source_urls.json</li> <li><a href="https://github.com/weberhen/learning_indoor_lighting">LDR Dataset</a>: License for Non-Commercial Use</li> <li><a href="https://data.vision.ee.ethz.ch/sagea/lld/">Large Logo Dataset (LLD)</a>: Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.</li> </ul> You can use our textureless models (i.e. the obj files) of damaged parcels under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> (note that this does not apply to the textures).   If you use this resource for scientific research, please consider citing <pre><code>@inproceedings{naumannParcel3DShapeReconstruction2023, author = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai}, title = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {4402-4412} }</code></pre>

openSep 2023View details →

ODSS: An Open Dataset of Synthetic Speech

ODSS is a multilingual, multispeaker dataset of synthetic and natural speech, designed to foster research and benchmarking of novel studies on synthetic speech detection.  ODSS comprises audio utterances generated from text by state-of-the-art synthesis methods, paired with their corresponding natural counterparts. The synthetic audio data includes several languages, with an equal representation of genders. Natural and synthetic speech audio files within ODSS are released under the CC-BY-SA 4.0 license: Usage, extension and redistribution by the research community are strongly encouraged.

opencc-by-nc-sa-4.0Oct 2023View details →

Synthetic datasets for end-to-end Relation Extraction of relationships between Organisms and Natural-Products

Synthetic datasets (training/validation) for end-to-end Relation Extraction of relationships between Organisms and Natural-Products. The datasets are provided for reproducibility purposes, but, can also be used to train new models.As in the corresponding article, 3 subtypes of synthetic datasets are provided:<ul><li>Diversity-synt: The seed literature references used in the generation process correspond to the top-500 extracted items per biological kingdoms using the <a href="https://github.com/idiap/gme-sampler">GME-sampler</a>.</li><li>Random-synt: 5 datasets of equivalent sizes as Diversity-synt, but using randomly sampled seed literature references.</li><li>Extended-synt: A merge of Diversity-synt and the 5 Random-synt datasets.  </li></ul>All datasets were produced with <a href="https://huggingface.co/lmsys/vicuna-13b-v1.3">Vicuna-13b-v1.3</a>. Like the model, the produced synthetic data are also submitted to the License of the model used for generation, see the original <a href="https://github.com/facebookresearch/llama/blob/llama_v1/MODEL_CARD.md">LLaMA model card</a>.LLaMA is licensed under the <a href="https://github.com/facebookresearch/llama/blob/llama_v1/MODEL_CARD.md">LLaMA License</a>, Copyright (c) Meta Platforms, Inc. All Rights Reserved.

publicDec 2022View details →

Synthetic vehicle trajectory dataset for the metropolitan city of Los Angeles using DDTG

Open the record for dataset details and reuse information.

publicMar 2025View details →

Experimental and synthetic datasets supporting FITSA: Statistical analysis of fluorescence intensity transients with Bayesian methods

Open the record for dataset details and reuse information.

publicMay 2024View details →

Synthetic temporal dataset for temporal trend analysis and retrieval

Open the record for dataset details and reuse information.

publicMar 2025View details →

acusim: a synthetic dataset for cervicocranial acupuncture points localisation

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2019View details →

Synthetic Datasets for Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms

Synthetic Dataset for Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms, as published on F1000Research.

opencc-by-4.0May 2020View details →

Synthetic Datasets for Evaluation of Spa-Typing Methods

This dataset contains 3 sets of wgsim simulated reads (paired-end, 100 bp length) of MRSA. Synthetic dataset A contains reads sampled from a reference genome where the X region was artificially replaced with the t032 spa-type sequence. The reads contain no errors. Synthetic dataset B contains reads sampled from a reference genome where the X region was artificially replaced with the t032 spa-type sequence. The reads contain point mutation errors. Synthetic dataset C contains reads sampled from a reference genome where the X region was artificially replaced with a specific spa-type sequence. The reads contain point mutation errors.

opencc-by-4.0May 2020View details →

AAU synthetic ROS dataset for VIO

Synthetic ROS data set for Visual-Inertial Odometry, providing IMU measurements (linear acceleration and angular velocities) as well as camera images with different resolutions (320x240, 640x480, and 1280x960) at rates of 500 Hz and 100 Hz, respectively. The camera images are obtained from a texture-rich outdoor scene created Unity.  Two circles with a diameter of 10 m are performed within 72 seconds. Besides the noisy measurements, ground-truth poses are included in the ROS bag file. The intrinsic and extrinsic camera calibrations as well as the IMU noise characteristics are provided. Contained ROS topics: <ul> <li>/mus/ground_truth_pose_imu : geometry_msgs/PoseStamped</li> <li>/mus/image : sensor_msgs/Image</li> <li>/mus/imu : sensor_msgs/Imu</li> <li>/tf : tf/tfMessage</li> </ul>

opencc-by-4.0May 2020View details →

Synthetic EUNOMIA Mastodon Testbed Dataset

EMTD is a specifically crafted dataset of posts on an isolated Mastodon testbed, using dummy Mastodon user account for emulating user posts and information cascades. The dataset consists of 616 posts, with over 100 information cascades, consisting of 3-4 posts from individual users.

opencc-by-4.0Oct 2020View details →

I-BiDaaS - CAIXA - IP addresses - Synthetic Dataset

The generated dataset provides data on the relationships between customers in order to build part of the social graph of the bank. The data was synthetically generated based on real data coming from a set of restricted tables (relational database), with information related to the customers and their IP address when connecting online. CAIXA and IBM generated the data recipe for the data fabrication using IBM TDF. Through an iterative analysis of obtained results, the rules were improved in order to obtain the fabricated dataset used for testing the MVP, with more than 1 million entries.

opencc-by-4.0Oct 2020View details →

SDT Dataset | Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification

The Synthetic Depth & Thermal (SDT) dataset consists of 40k synthetic and 8k real depth and thermal stereo images, depicting human behavior in indoor environments. Included samples show uniquely posed lying, sitting, and standing persons within four different room types (living room, bedroom, bathroom, and kitchen), recorded from an elevated position. Furthermore, a fourth control class with empty rooms is provided as well. Both parts of SDT are balanced sets of these four classes and room types. The synthetic part of the dataset is intended to be used as training (and validation) data for uni-/multi-modal pose classification or person detection models, while the real part can be used to assess the generalization performance. To facilitate supervised training, pose labels and person bounding boxes are provided for all images. The real images in the dataset were captured by a multi-modal stereo camera system, consisting of an Orbbec Astra depth camera and a FLIR Lepton 3.5 thermal camera, while synthetic images, which share the image characteristics of these cameras, were acquired through 3D rendering of virtual scenes within Blender and subsequent introduction of camera-specific noise. Download and Use This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1]. [1] C. Pramerdorfer, J. Strohmayer and M. Kampel, "Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification," 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 1611-1615, doi: 10.1109/ICIP40778.2020.9191284. BibTeX citation: <pre>@INPROCEEDINGS{9191284, author={Pramerdorfer, C. and Strohmayer, J. and Kampel, M.}, booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, title={Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification}, year={2020}, volume={}, number={}, pages={1611-1615}, doi={10.1109/ICIP40778.2020.9191284}}</pre>

opencc-by-4.0Dec 2014View details →

SIDIRE: Synthetic Image Dataset for Illumination Robustness Evaluation

SIDIRE is a freely available image dataset which provides synthetically generated images allowing to investigate the influence of illumination changes on object appearance. The images are renderings of 3D coin models with different material BRDFs and levels of texturedness. Thus, the dataset makes it possible to directly evaluate the influence of these conditions on the performance of image recognition without introducing a bias due to different objects used between image sets. The dataset has been used for evaluation in [1]. Usage The dataset is freely available for non-commercial research use. Please cite our paper [1] when using the dataset for your research. Technical Details Full Image Dataset The full image dataset consists of images of 14 coin models which have been rendered using the open-source graphics software <a href="http://www.blender.org">Blender</a>. For each model, twelve sets of 500×500 images with 65 illumination directions were rendered where each set represents one out of four material BRDFs and one out of three texture density levels. Material BRDFs are intended to represent different levels of specularity starting from a Lambertian material with zero specularity up to specular intensity values of 0.25, 0.50 and 1.00. The first texture density level shows no texture and thus represents the set of textureless objects. For the remaining two levels synthetically generated textures were used. The camera image plane is placed parallel to the coin and light source positions are defined by their azimuth angle φ and elevation angle λ. We used eight levels of λ with eight levels of φ each to produce 64 images. The 65th image is rendered with the light placed at the camera position (i.e. λ=90°). In the provided RAR-file, all the 65 images of a specific model, specularity level and texturedness level are contained in separate directories. For instance, the directory ‘texture_level0\Ref_level2\2874-back’ contains the images of the model ‘2874-back’ rendered without texture and a specularity of 0.50. Patch Dataset The patch dataset contains 50000 matching patch pairs for every of the 12 subsets of SIDIRE. It can be used to generate groups of feature distances by means of true and false patch pairs, in the same manner as, e.g., Matthew Brown’s <a href="http://phototour.cs.washington.edu/patches/default.htm">patch dataset</a>. Please see [1,2] for a detailed description of the evaluation scheme of patch pair databases. The patches have a size of 64×64 and are arranged in images of size 3200×3200. Thus, every image contains 2500 patches where corresponding patches are placed side by side. The patches of the 12 subsets are contained in directories indicating their texture density and reflectance level, e.g. patches rendered without texture and a specularity of 0.50 are contained in the directory ‘tex0_ref2’.   References [1] Zambanini S., Kampel M. “Evaluation of Low-Level Image Representations for Illumination-Insensitive Recognition of Textureless Objects”, International Conference on Image Analysis and Processing – ICIAP’13, Naples, Italy, September 2013. (<a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/iciap13.pdf">pdf</a>, <a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/iciap13_supp1.pdf">supplementary material</a>) [2] Brown, M., Gang Hua, Winder, S., “Discriminative Learning of Local Image Descriptors”, Pattern Analysis and Machine Intelligence, vol.33, no.1, pp.43-57, 2011.

opencc-by-4.0Aug 2016View details →

Dataset for the publication "Neutrophilic Bioleaching of Synthetic Covellite – A Model System Combining Experimental Data and Geochemical Modeling"

This dataset contains data, which were used in preparation of the publication "Neutrophilic Bioleaching of Synthetic Covellite – A Model System Combining Experimental Data and Geochemical Modeling"

opencc-by-4.0Nov 2023View details →

Dataset in csv format containing labor force data in a synthetic population

Open the record for dataset details and reuse information.