cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/56ytijxm
title: VisDrone-2019-DET dataset

description: The VisDrone-2019-DET dataset is a comprehensive benchmark facilitating the integration of drone technology and visual perception.
DOI: 10.57702/0e2tergg
title: IXI, ADNI, OASIS-3, MCIC, NUSDAST, NYU, KKI

description: The dataset used in this study for brain sMRI analysis, which includes seven public datasets, including one dataset for pre-training and other six datasets across three diseases (i.e., AD, SZ, ADHD) for downstream classification tasks.
DOI: 10.57702/t3bhqkn5
title: Knee Menisci Segmentation and Relaxometry in 3D UTE Cones MR Imaging

description: The dataset used for knee menisci segmentation and relaxometry in 3D UTE cones MR imaging using attention U-Net with transfer learning
DOI: 10.57702/uljk4aks
title: The KIT Motion-Language Dataset

description: The KIT Motion-Language Dataset consists of 3,911 motion sequences with 12.5 FPS and 6,278 language annotations.
DOI: 10.57702/9j8bo03w
title: Generating Diverse and Natural 3D Human Motions from Text

description: Generating diverse and natural 3D human motions from text.
DOI: 10.57702/za04p9ct
title: CLAMM16 and CLAMM17

description: The dataset used for writer identiôcation and script type classiôcation.
DOI: 10.57702/tckl39z2
title: InertialHAR

description: The dataset used in the paper is a large unstructured industrial dataset.
DOI: 10.57702/3b2zo8d8
title: Fashion

description: The Fashion dataset is used for human image animation task. It contains videos of humans performing different actions.
DOI: 10.15468/dl.3hdv22
title: Occurrence Download

description: A dataset containing 2785 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Searsia lancea (L.fil.) F.A.Barkley"
 ]
}

 The dataset includes 2785 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014138-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14264867
title: StateSwitch (dynamic) BIDS dataset

description: Raw BIDS data for StateSwitch (dynamic)

 

The data are distributed as nested DataLad datasets that are hosted on gin. Please see the repository URL for access.

 





Please cite the following references if you use these data:


Kosciessa, J. Q., Lindenberger, U. & Garrett, D. D. Thalamocortical excitability modulation guides human perception under uncertainty. Nat. Commun. 12, 2430 (2021).

 

Kosciessa, J. Q., Mayr, U., Lindenberger, U. & Garrett, D. D. Broadscale dampening of uncertainty adjustment in the aging brain. Nat. Commun. (in press, 2024).


A static copy of the data for younger adults is also provided at https://osf.io/mgxqr/.


In case of questions, contact kosciessa@mpib-berlin.mpg.de or [MPIB Research Data Management](<rdm@mpib-berlin.mpg.de>).
DOI: 10.5281/zenodo.14264868
title: StateSwitch (dynamic) BIDS dataset

description: Raw BIDS data for StateSwitch (dynamic)

 

The data are distributed as nested DataLad datasets that are hosted on gin. Please see the repository URL for access.

 





Please cite the following references if you use these data:


Kosciessa, J. Q., Lindenberger, U. & Garrett, D. D. Thalamocortical excitability modulation guides human perception under uncertainty. Nat. Commun. 12, 2430 (2021).

 

Kosciessa, J. Q., Mayr, U., Lindenberger, U. & Garrett, D. D. Broadscale dampening of uncertainty adjustment in the aging brain. Nat. Commun. (in press, 2024).


A static copy of the data for younger adults is also provided at https://osf.io/mgxqr/.


In case of questions, contact kosciessa@mpib-berlin.mpg.de or [MPIB Research Data Management](<rdm@mpib-berlin.mpg.de>).
DOI: 10.57702/46j4yj6o
title: Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects

description: The dataset used in the paper Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects. The dataset consists of 30 unique 3D object models corresponding to 30 ImageNet classes relevant to a trafﬁc environment.
DOI: 10.57702/2vzxqnu1
title: A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football

description: Football Event data, including 15,575 shots, used to develop and apply machine learning models for Expected Goals (xG) prediction
DOI: 10.57702/4c0biv1x
title: Simple Dataset

description: The dataset contains data collected in various behavioral and social scenarios, including polls of public opinion, visual illusions, conjoint choices, and psychophysical matching.
DOI: 10.57702/qv3vyeyi
title: Complex Dataset

description: A dataset for training a visual transformer to predict a scene descriptor from a single wire-frame image.
DOI: 10.17632/vg95gvhj3y.2
title: Bone Fracture X-ray Dataset: Simple vs. Comminuted Fractures

description: Description:
 This dataset has been curated to support research in bone fracture classification, specifically focusing on simple and comminuted fractures. It includes high-quality X-ray images to aid in the development and 
 evaluation of machine learning models for medical imaging applications. This dataset is ideal for image classification, segmentation, and fracture type recognition tasks.

Dataset Overview:
 The dataset contains X-ray images of bones affected by two types of fractures:

Simple Fracture:
 Images exclusively sourced from hospital records.
 Original: 164 images, augmented to 3,280 images.

Comminuted Fracture:
 A mix of hospital-sourced images and web-sourced images (approximately one-third from web pages).
 Original: 145 images, augmented to 2,900 images.
 The dataset has been updated to provide a larger variety of augmented images using additional augmentation techniques. It now includes a total of 6,489 augmented images, ensuring more comprehensive 
 coverage of potential fracture patterns.

Key Features:

 Number of Original Images: 309
 Number of Augmented Images: 6,489
 Total Dataset Size: 6,798 images (Original + Augmented)
 File Formats: JPG, PNG
 Augmentation Techniques: High brightness, low brightness, high contrast, low contrast, rotations, and varied orientations.

Update Details (Version 2):
 Original Submission Date: 15 Nov 2024
 Update Date: 23 Nov 2024

Changes in This Version:
 The dataset size has been increased with additional augmented images (from 3,090 to 6,489 augmented images).
 Updated augmentation techniques to include more diverse transformations.

Improved class balance:
 Simple Fracture: Increased from 1,640 to 3,280 augmented images.
 Comminuted Fracture: Increased from 1,450 to 2,900 augmented images.

Applications:
 1.Medical Imaging: Useful for training models in fracture classification and identification.
 2.Healthcare Technology: Supports the development of diagnostic tools and mobile applications for real-time fracture detection.
 3.Medical Research: Assists in understanding fracture patterns and their visual indicators.

Dataset Collection:
 The simple fracture images were exclusively sourced from hospitals, ensuring clinical relevance. The comminuted fracture images were gathered from a combination of hospital and web sources, with approximately 
 one-third originating from web pages. This dataset aims to provide realistic scenarios for training models in a clinical setting, incorporating diverse lighting conditions and image orientations to simulate various 
 imaging environments.
DOI: 10.17632/vg95gvhj3y.1
title: Bone Fracture X-ray Dataset: Simple vs. Comminuted Fractures

description: Description:
This dataset has been curated to support research in bone fracture classification, specifically focusing on simple and comminuted fractures. It includes high-quality X-ray images to aid in the development and evaluation of machine learning models for medical imaging applications. This dataset is ideal for image classification, segmentation, and fracture type recognition tasks.

Dataset Overview:
The dataset contains X-ray images of bones affected by two types of fractures:

 1.Simple Fracture: Images sourced directly from hospital records (164 images, augmented to 1,640 images).
 2.Comminuted Fracture: A mix of hospital-sourced images and web-sourced images (145 images, augmented to 1,450 images).
The images are captured under diverse real-world settings to ensure a robust dataset for training and testing purposes. Augmentation techniques, including adjustments in brightness, contrast, orientation, and angle, have been applied to increase the variety and volume of images.

Key Features:
 Number of Original Images: 309
 Number of Augmented Images: 3,090
 File Formats: JPG, PNG
 Augmentation Techniques: High brightness, low brightness, high contrast, low contrast, different angles, and orientations.

Applications:
 Medical Imaging: Useful for training models in fracture classification and identification.
 Healthcare Technology: Supports the development of diagnostic tools and mobile applications for real-time fracture detection.
 Medical Research: Assists in understanding fracture patterns and their visual indicators.

Dataset Collection:
 The simple fracture images were exclusively sourced from hospitals, while the comminuted fracture images were gathered from a combination of hospital and web sources (approximately one-third from web pages). 
 This dataset aims to provide realistic scenarios for training models in a clinical setting, with diverse lighting and orientation conditions to simulate various imaging environments.
DOI: 10.57702/555o20bs
title: Paris Street View dataset

description: Paris Street View dataset is a dataset of street view images.
DOI: 10.17632/vg95gvhj3y.3
title: Bone Fracture X-ray Dataset: Simple vs. Comminuted Fractures

description: Dataset Overview:
 This dataset has been curated to support research in bone fracture classification, focusing on simple and comminuted fractures. It includes high-quality X-ray images and a diverse set of augmented images to 
 facilitate the development and evaluation of machine learning models in medical imaging. The dataset is ideal for image classification, segmentation, and fracture type recognition tasks.

Fracture Categories:
 1. Simple Fracture:
 Source: Exclusively sourced from hospital records.
 Original Images: 1,211 images.
 Augmented Images: 6,311 images.
 Total Images: 7,522 images.
 2. Comminuted Fracture:
 Source: A mix of hospital-sourced images and web-sourced images (approximately one-tenth from web pages).
 Original Images: 1,173 images.
 Augmented Images: 7,366 images.
 Total Images: 8,539 images.
 Key Features:
 Number of Original Images: 2,384.
 Number of Augmented Images: 13,677.
 Total Dataset Size: 16,061 images (Original + Augmented).
 File Formats: JPG.
 Augmentation Techniques:
 Zoom: Randomized scaling.
 Rotation: ±30° rotation.
 Brightness and Contrast Adjustments: 80%–120% range.
 Flips: Vertical and horizontal flips.

Update Details (Version 3):
 Original Submission Date: 15 Nov 2024.
 Update Date: 02 Dec 2024.

Changes in This Version:
 Expanded Dataset Size:
 From: 6,798 total images (previous version).
 To: 16,061 total images.

Augmented Images:
 Simple Fracture: Increased from 3,280 to 6,311 images.
 Comminuted Fracture: Increased from 2,900 to 7,366 images.
 Augmentation Enhancements: Additional transformations were applied to ensure varied and realistic images for training.

Applications:

 Medical Imaging:
 Train and evaluate models for fracture classification and identification.
 Healthcare Technology:
 Support the development of diagnostic tools and mobile applications for real-time fracture detection.
 Medical Research:
 Aid in understanding fracture patterns and their visual indicators.

Dataset Collection:
 Simple Fracture Images:
 Exclusively sourced from hospitals to ensure clinical accuracy and relevance.
 Comminuted Fracture Images:
 Sourced from a mix of hospital records and web pages, representing diverse imaging conditions and perspectives.

Realistic Scenarios:
 The dataset simulates real-world clinical settings with varying lighting conditions, orientations, and imaging environments to provide robust training data.
 This dataset is a valuable resource for advancing medical imaging research and developing machine learning models tailored for fracture detection and classification tasks.
DOI: 10.57702/stwhmm06
title: CelebA-HQ256 dataset

description: CelebA-HQ256 dataset is a dataset of face images.
DOI: 10.57702/n6aolu7y
title: Visual Geo-localization from images

description: This paper presents a visual geo-localization system capable of determining the geographic locations of places (buildings and road intersections) from images without relying on GPS data.
DOI: 10.57702/5lrvmnlb
title: SIFT descriptor for binary shape discrimination, classification and matching

description: The dataset used for training the SIFT descriptor for binary shape discrimination, classification and matching.
DOI: 10.17632/xg5k8tkc96.1
title: Dataset for IoT-based Indoor Air Quality Management System for Intelligent Education Environments

description: This is the dataset for our paper entitled “IoT-Based Indoor Air Quality Management System for an Intelligent Education Environment”. This dataset contains the following information: 
- the flow defined in the low-code platform for IoT, CEP and Blockchain integration (IAQNodeREDFlow.png).
- the event patterns defined in the CEP platform for the indoor air quality domain (IAQCEPApp.siddhi).
- the smart contract used to store situations of interest in blockchain (ComplexEvents.sol).
- the code that reads the air pollution data and applies LSTM to it (Weather_data_analysis.ipynb). The results are also displayed for the trained model. 
- all complex events generated by the CEP engine from 07/05/2023 to 07/16/2023 (complex_events_07-05-2023_07-16-2023.csv).
- a subset of the complex events generated by the CEP engine on 07/06/2023 (complex_events_07-06-2023.csv), which was used to generate a time-processing plot included in the paper (see Figure 5).
DOI: 10.57702/fd4abok9
title: Simulated Purchasing Datasets

description: Simulated purchasing datasets with non-stationary reward distributions
DOI: 10.57702/4km9ia55
title: Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks

description: The dataset is used for training autonomous quadrotor navigation policies for direct, real-world deployment.
DOI: 10.23729/c9e6e2d6-0402-4271-a0c0-edd75e365fa3
title: Finnish Internet Parsebank project data

description: Data collection archiving all stages of the Finnish Internet Parsebank web crawl dataset.
DOI: 10.17632/s6p5crrg9n
title: References

description: References
DOI: 10.17632/s6p5crrg9n.1
title: References

description: References
DOI: 10.57702/5ehqun2i
title: MNIST-D

description: One domain consists of the original MNIST digits, while the other is generated by applying multiple transformations including rotation, downscaling, and Gaussian blurring.
DOI: 10.17632/m73wzvgjz9.1
title: Chapter 4.- Exploring Radiomics and Unveiling Novel Qualitative Imaging Biomarkers for Glioma Diagnosis in Dogs

description: Radiomics features and python codes for SVM binary and multi-class classification
DOI: 10.57702/6v2akt0e
title: RNA-ATAC

description: Jointly measured observations of single-cell RNA-seq and ATAC-seq data.
DOI: 10.57702/5smtq2al
title: stl10

description: A popular dataset for computer vision, where the first domain contains the original images, and the second is generated by applying brightness, gray scaling, and Gaussian blurring.
DOI: 10.57702/lim1r21q
title: LLFF, DTU, and Blender datasets

description: The dataset used for training and testing the Uncertainty-guided Optimal Transport (UGOT) approach for depth supervision in sparse-view 3D Gaussian splatting for novel view synthesis.
DOI: 10.57702/o3kmvxiy
title: ImageNet, COCO, and Unpaired real dataset

description: The dataset used in the paper is a large set of real images extracted from various object categories of the ImageNet, COCO, and Unpaired real dataset.
DOI: 10.57702/e6a0s8p0
title: Jailbreaking Large Language Models

description: The dataset used in the paper is a jailbreaking dataset for large language models, containing pairs of provocative questions and their corresponding affirmative responses.
DOI: 10.15468/dl.vub29u
title: Occurrence Download

description: A dataset containing 1818 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dussumieria elopsoides Bleeker, 1849",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1818 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014141-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/e02s74au
title: WikiMatrix

description: The WikiMatrix dataset is a multilingual dataset that contains parallel texts between English and other languages.
DOI: 10.57702/3z14x1lh
title: Argoverse and TrajNet++ Datasets

description: The Argoverse and TrajNet++ datasets are used for multi-agent trajectory forecasting tasks.
DOI: 10.15468/dl.byyhur
title: Occurrence Download

description: A dataset containing 303 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dipturus oxyrinchus (Linnaeus, 1758)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 303 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014140-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/vlf6lg15
title: ROAD-R: the autonomous driving dataset with logical requirements

description: The ROAD-R dataset contains autonomous driving scenarios with logical requirements.
DOI: 10.57702/qsvrulws
title: Microstructure-evolution dataset

description: The dataset used in this paper for training and testing the autoencoder-DeepONet model for phase-field modeling of microstructure evolution.
DOI: 10.57702/8w3wvahr
title: Phase-ﬁeld model of the spinodal decomposition of a two-phase mixture

description: The dataset used in this paper for training and testing the autoencoder-DeepONet model for phase-field modeling of microstructure evolution.
DOI: 10.57702/5kj8o1yg
title: Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D

description: The dataset used in the paper is a multi-view image dataset, where each view is a 2D image of a 3D scene. The dataset is used to evaluate the performance of the Lift3D method, which is a framework for transforming 2D vision models to 3D predictions.
DOI: 10.57702/4873o439
title: Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items

description: The dataset used in the paper is a 3D scanned household items dataset, which is used to evaluate the performance of the Lift3D method.
DOI: 10.57702/g8htwyhp
title: ModelNet10 dataset

description: The ModelNet10 dataset contains 8,000 training and 2,000 test 3D models of objects.
DOI: 10.57702/g3r1l970
title: FRED-MD database

description: The dataset used in this paper is the FRED-MD database, which contains monthly observations of macroeconomic variables for the US.
DOI: 10.15468/dl.8nhyn6
title: Occurrence Download

description: A dataset containing 15324 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diplodus vulgaris (Geoffroy Saint-Hilaire, 1817)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 15324 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014139-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/461nmun3
title: The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

description: Anomaly detection in semantic segmentation
DOI: 10.57702/fzkfn5kr
title: Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles

description: Detecting small road hazards for self-driving vehicles
DOI: 10.57702/tlx6fnug
title: JCT-VC Dataset

description: A dataset of images and videos for video compression research.
DOI: 10.57702/a9pq7qu9
title: Labadain-30k+

description: The Labadain-30k+ dataset is a monolingual Tetun document-level audited dataset.
DOI: 10.57702/1sn75diu
title: Tetun Test Collection

description: The Tetun test collection is a document-level audited dataset for relevance judgments.
DOI: 10.57702/d4702xf1
title: UCF-101, Sky Time-lapse, and Taichi datasets

description: UCF-101, Sky Time-lapse, and Taichi datasets are used for video generation tasks.
DOI: 10.57702/vv95ukns
title: RCV1-v2

description: Multi-label text classification dataset RCV1-v2, Reuters Corpus Volume I
DOI: 10.57702/k4a908gw
title: Ren-CECps

description: Multi-label text classification dataset Ren-CECps
DOI: 10.57702/2lkei111
title: NYUv2 dataset

description: The NYUv2 dataset is a large-scale dataset for 3D object recognition and semantic segmentation. It contains 206 test set video sequences with 135 classes.
DOI: 10.57702/qezreejv
title: RGBDD dataset

description: Guided depth super-resolution (GDSR) involves restor- ing missing depth details using the high-resolution RGB image of the same scene.
DOI: 10.57702/w26bdv9h
title: Deep learning and insomnia: assisting clinicians with their diagnosis

description: Deep learning and insomnia: assisting clinicians with their diagnosis
DOI: 10.57702/17u8vgx8
title: ADE20K Dataset

description: The ADE20K dataset is a large-scale dataset for semantic segmentation. It contains 20,000 images with 150 semantic categories, with 20,000 images for training, 2,000 images for validation, and 2,000 images for testing.
DOI: 10.17632/vt67fzbv4z.1
title: Optimal Trade Policy with International Technology Diffusion

description: Replication package for "Optimal Trade Policy with International Technology Diffusion" by Yan Bai, Keyu Jin, Dan Lu, and Hanxi Wang
DOI: 10.17632/vt67fzbv4z.2
title: Optimal Trade Policy with International Technology Diffusion

description: Replication package for "Optimal Trade Policy with International Technology Diffusion" by Yan Bai, Keyu Jin, Dan Lu, and Hanxi Wang
DOI: 10.57702/alkl7vh5
title: EM Segmentation Challenge

description: The dataset is used for biomedical image segmentation tasks, specifically for the segmentation of neuronal structures in electron microscopic stacks and cell tracking in light microscopy images.
DOI: 10.57702/nbw4dlvj
title: ISBI Cell Tracking Challenge 2015

description: The dataset is used for cell tracking in light microscopy images.
DOI: 10.57702/is1oobyd
title: FastMRI knee dataset

description: The fastMRI knee dataset consists of 12,366 slices from 332 subjects, captured on one of three clinical 3T MR scanners.
DOI: 10.57702/sqsb6yvk
title: Cross-View Image Synthesis

description: Cross-view image synthesis aims to translate images between two distinct views, such as synthesizing ground images from aerial images, and vice versa.
DOI: 10.57702/psgvkukh
title: Localizing and orienting street views using overhead imagery

description: Localizing and orienting street views using overhead imagery.
DOI: 10.17632/z2syvptbx4.1
title: Structured exploration of machine learning model complexity for spatio-temporal forecasting of urban flooding

description: Datasets employed in Structured exploration of machine learning model complexity for spatio-temporal forecasting of urban flooding
DOI: 10.57702/i4htor48
title: Mobile device location data for Baltimore-D.C. Metropolitan Regions

description: Mobile device location data for Baltimore-D.C. Metropolitan Regions
DOI: 10.57702/bpnx2r9s
title: GPS-based travel survey data

description: GPS-based travel survey data
DOI: 10.57702/o2klt9np
title: Anonymized mobile device location data

description: Anonymized mobile device location data