cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/v0uajdbr
title: Dataset for Image-to-Image Translation for Semantic Segmentation

description: The dataset used for the experiments with the proposed approach to augment image data for semantic segmentation networks by applying image-to-image translation with both, a domain-speciﬁc mathematical model and an approach entirely based on generative models.
DOI: 10.57702/gq16uwi9
title: ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning for Landcover Classification on SAR Images

description: The proposed ClassWise-SAM-Adapter (CWSAM) is designed to adapt the high-performing SAM for landcover classification on space-borne Synthetic Aperture Radar (SAR) images.
DOI: 10.57702/l9w49jem
title: RTSEG: REAL-TIME SEMANTIC SEGMENTATION COMPARATIVE STUDY

description: Semantic segmentation benchmarking framework with decoupled design for feature extraction and decoding methods.
DOI: 10.57702/v7ut1sm0
title: Pascal Visual Object Classes (VOC) Challenge

description: The Pascal Visual Object Classes (VOC) challenge is a benchmark for object detection and segmentation.
DOI: 10.57702/b35ezmet
title: Wikitext-103

description: The dataset used in this paper is Wikitext-103, a general English language corpus containing good and featured Wikipedia articles.
DOI: 10.57702/dlkv4m7f
title: LAION-Aesthetics2

description: A dataset of images generated by various text-to-image models, including Stable Diffusion and Midjourney.
DOI: 10.57702/u4daksq2
title: Markov Decision Process

description: The dataset used in the paper is a Markov Decision Process, where states can take values in a state space X, corresponding to a state x ∈ X, we can take an action u ∈ U, corresponding to which we get a reward r ∈ R.
DOI: 10.57702/9jkvv0mf
title: 3D Warehouse

description: A 3D warehouse dataset used to create the dataset mentioned in the paper.
DOI: 10.57702/5afwcuwb
title: Concrete slump test dataset

description: The concrete slump test dataset records the test results of two executed tests on concrete to study its behavior.
DOI: 10.57702/rqg0dc3x
title: GaitMM: Multi-Granularity Motion Sequence Learning for Gait Recognition

description: Gait recognition aims to identify individual-specific walk- ing patterns by observing the different periodic movements of each body part. However, most existing methods treat each part equally and fail to account for the data redundancy caused by the different step frequencies and sampling rates of gait sequences.
DOI: 10.57702/e755zzuw
title: Synthetic Graph Classification Dataset

description: The dataset used in the paper is a synthetic dataset for graph classification tasks.
DOI: 10.57702/xfs6e1tm
title: DD

description: The dataset used in the paper is a real-world dataset for molecular structure classification.
DOI: 10.57702/duleik11
title: 2.5D Off-Road Terrain Environment

description: The dataset used in the paper is a 2.5D off-road terrain environment for ground vehicles.
DOI: 10.57702/xkqkfx3j
title: ARCH: Animatable Reconstruction of Clothed Humans

description: ARCH (Animatable Reconstruction of Clothed Humans) is a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image.
DOI: 10.57702/d82a9l1i
title: BUFF

description: The dataset used for testing and evaluation of the volumetric performance capture system.
DOI: 10.57702/r1thcb4d
title: GLOW : Global Weighted Self-Attention Network for Web Search

description: GLOW is a novel Global Weighted Self-Attention Network for web document search. It leverages global corpus statistics into the deep matching model.
DOI: 10.57702/tzel7fpm
title: Bing dataset

description: The Bing dataset is a large-scale dataset for natural language understanding and question answering.
DOI: 10.57702/uxsc8v38
title: VinDr-Mammo dataset

description: A large-scale benchmark dataset for full-field digital mammography.
DOI: 10.57702/hibtfrtd
title: SC-MAD: MIXTURES OF HIGHER-ORDER NETWORKS FOR DATA AUGMENTATION

description: The dataset used in the paper is a collection of simplicial complexes, which are higher-order networks that model complex interactions.
DOI: 10.57702/a3kdln60
title: UNIVERSALPERSONA

description: The UNIVERSALPERSONA dataset is a comprehensive dataset for evaluating persona biases in dialogue systems.
DOI: 10.57702/8ehdiwz9
title: DisC-Diff: Disentangled Conditional Diffusion Model for Super-Resolution

description: Multi-contrast MRI super-resolution
DOI: 10.57702/erdnhd76
title: Multi-contrast super-resolution mri through a progressive network

description: Multi-contrast super-resolution MRI through a progressive network
DOI: 10.57702/z8dv4wdf
title: AR1, COP, and WEB datasets

description: The dataset is a synthetic time series dataset, i.e., ﬁrst order autoregression (AR1), and two real-world datasets, i.e., crude oil price (COP) and web trafﬁc (WEB).
DOI: 10.57702/lzk1nd0l
title: TDD-net: a tiny defect detection network for printed circuit boards

description: The dataset is used for printed circuit board defect detection.
DOI: 10.6084/m9.figshare.24894363
title: A dataset containing speciation rates, uncertainty, and presence-absence matrices for more than 34,000 vertebrate species

description: The increasing availability of phylogenetic data has facilitated the exploration of macroecological and macroevolutionary patterns across diverse spatial and temporal scales. However, calculating some model-based diversification metrics often requires significant computational power and time. To address this, I present a comprehensive dataset of Bayesian diversification rates for over 34,000 vertebrate species, spanning five major groups: amphibians, birds, mammals, reptiles, and sharks. This is the first large-scale dataset of its kind, providing both continuous and binary data for speciation rates and presence-absence matrices, respectively, with global coverage. The dataset not only enables analyses of evolutionary and spatial diversity patterns but also democratizes access to data-intensive studies. Additionally, as it is based on Markov chains, the dataset can be customized and extended without the need to start from scratch, offering flexibility for future research on diversification dynamics.
DOI: 10.57702/pkscv4hn
title: Leather defect classification and segmentation using deep learning architecture

description: The dataset is used for leather defect classification and segmentation.
DOI: 10.6084/m9.figshare.24894363.v1
title: A dataset containing speciation rates, uncertainty, and presence-absence matrices for more than 34,000 vertebrate species

description: <b>Motivation:</b> Today, the large amount and availability of phylogenetic data have allowed the exploration of different types of patterns at various evolutionary and spatial scales. However, the calculation of some metrics used for this purpose can be computationally demanding and complex, such as those related to diversification rates. Therefore, the existence of a database that provides such information can streamline the process. It also makes science fairer and more accessible for those without access to great computational power or advanced programming skills.<b>Main types of variables contained:</b> Continuous and binary data depicting the speciation rates and presence-absence matrices, respectively, for more than 34,000 vertebrate species.<b>Spatial location and grain:</b> Worldwide at a specific level.<b>Time period and grain:</b> Present, but based on phylogenetic hypotheses for groups with between 300–100 Myr of evolutionary history.<b>Major taxa and level of measurement:</b> Vertebrates, including the Lissamphibia subclass (amphibians), the Squamata suborder (reptiles), and the classes Aves (birds), Mammalia (mammals), and Chondrichthyes (sharks), all of them at the species level.<b>Software format:</b> Various, including Excel files (.xlsx), nexus files (.nex), tree files (.tre), text files (.txt), raster files (.tiff), and RData files (.RData).
DOI: 10.57702/49kh5263
title: Synthetic data generation for steel defect detection and classiﬁcation using deep learning

description: The dataset is used for steel defect detection and classification.
DOI: 10.57702/85lp18rm
title: HCP Dataset

description: The dataset used in the paper is a subset of the Human Connectome Project dataset, containing functional connectivity MRI scans from 865 subjects.
DOI: 10.57702/zqhnlbuz
title: Cifar-10 and Cifar-100

description: The dataset used in the paper is Cifar-10 and Cifar-100, which are commonly used datasets for image classification tasks.
DOI: 10.17608/k6.auckland.25808182
title: Sediment oxygen demand measured with Resuspension Assay

description: This dataset contains sediment oxygen demand of marine surface sediments determined through a resuspension assay. The resuspension assay is based on the BOD method and conducted on pairs of samples, one an undisturbed sediment core to measure undisturbed sediment oxygen consumption, and the other a resuspension treatment to measure sediment oxygen consumption after disturbance by shaking.
DOI: 10.17608/k6.auckland.25808182.v2
title: Sediment oxygen demand measured with Resuspension Assay

description: This dataset contains sediment oxygen demand of marine surface sediments determined through a resuspension assay. The resuspension assay is based on the BOD method and conducted on pairs of samples, one an undisturbed sediment core to measure undisturbed sediment oxygen consumption, and the other a resuspension treatment to measure sediment oxygen consumption after disturbance by shaking.
DOI: 10.17608/k6.auckland.25808182.v1
title: Sediment oxygen consumption rates measured with Resuspension Assay

description: This dataset contains sediment oxygen consumption rates of marine surface sediments determined through a resuspension assay. The resuspension assay is based on the BOD method and conducted on pairs of samples, one an undisturbed sediment core to measure undisturbed sediment oxygen consumption, and the other a resuspension treatment to measure sediment oxygen consumption after disturbance by shaking.
DOI: 10.57702/i399k2z1
title: SAR image denoising via bayesian wavelet shrinkage based on heavy-tailed modeling

description: SAR image denoising via bayesian wavelet shrinkage based on heavy-tailed modeling.
DOI: 10.57702/1xkmz8ti
title: A non-local algorithm for image denoising

description: A non-local algorithm for image denoising.
DOI: 10.57702/kjvs5wfa
title: UC Merced land-use data

description: UC Merced land-use data
DOI: 10.57702/uhdmfu1r
title: Hierarchical Question-Image Co-Attention for Visual Question Answering

description: A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling “where to look” or visual attention, it is equally important to model “what words to listen to” or question attention.
DOI: 10.57702/gj9qvuik
title: VQA: Visual Question Answering

description: Visual Question Answering (VQA) has emerged as a prominent multi-discipline research problem in both academia and industry.
DOI: 10.57702/m29879l4
title: Exploring Models and Data for Image Question Answering

description: Exploring models and data for image question answering.
DOI: 10.57702/663o35jw
title: F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting

description: The authors propose a novel Factorized 3D Gaussian Splatting (F-3DGS) method to reduce the storage requirements while maintaining image quality compared to 3DGS.
DOI: 10.57702/28bo8bxa
title: UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection

description: Aerial-ground collaborative perception framework for 3D object detection
DOI: 10.57702/olqxjz31
title: DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection

description: Vehicle-infrastructure cooperative 3D object detection dataset
DOI: 10.57702/d709lj74
title: V2X-Sim: A Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving

description: Multi-agent collaborative perception dataset and benchmark for autonomous driving
DOI: 10.57702/nakp4wyy
title: OpenOOD: Benchmarking Generalized Out-of-distribution Detection

description: OpenOOD is a benchmarking dataset for generalized out-of-distribution detection.
DOI: 10.57702/0q0qf1af
title: Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds

description: Gesture recognition interface for point clouds generated by mmWave Radars
DOI: 10.57702/jyt7rk9p
title: UBPG dataset

description: Upper Body Point Cloud Gestures (UBPG) RGB-D gesture point cloud dataset
DOI: 10.57702/glv732p3
title: SoHO-ML-Data-Ready

description: A Python tool to generate a standard dataset from solar images that allows for user-deﬁned selection criteria and a range of pre-processing steps.
DOI: 10.57702/3i4ljslh
title: SoHO Dataset

description: A dataset of solar images that can be used for machine learning and space weather forecasting.
DOI: 10.57702/lt2qfebn
title: JSB Chorale dataset

description: The JSB Chorale dataset is used for music generation learning from a single music segment.
DOI: 10.57702/x9tdc4wm
title: TCC Benchmark

description: Temporal Colour Constancy (TCC) dataset, a benchmark for temporal color constancy
DOI: 10.57702/uvltnul0
title: Trafﬁc4Cast

description: The trafﬁc data is given as a two-dimensional heat-map-like image representation of size 495 × 436 pixels with eight channels.
DOI: 10.57702/ol12wvn9
title: Image-to-image learning to predict trafﬁc speeds

description: The dataset is used for trafﬁc speed prediction using image-to-image learning.
DOI: 10.57702/mkot2r5e
title: Accelerating Convergence of Score-Based Diffusion Models, Provably

description: Diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampling phase.
DOI: 10.57702/85y9yozb
title: NUS-WIDE

description: The dataset used in the paper is a multi-view clustering dataset, which contains 6 views of 30000 samples each. The dataset is used to evaluate the performance of the proposed multi-view clustering algorithm.
DOI: 10.57702/8hcvteh1
title: PASCAL VOC 2007

description: Multi-label image recognition is a practical and challenging task compared to single-label image classification.
DOI: 10.57702/7votdvky
title: G. Washington dataset

description: A dataset for page segmentation of handwritten historical document images.
DOI: 10.57702/rowvz0nf
title: St. Gall dataset

description: A dataset for page segmentation of handwritten historical document images.
DOI: 10.57702/izs66w9s
title: CSG863 dataset

description: A dataset for page segmentation of handwritten historical document images.
DOI: 10.57702/wdfdq65f
title: CVC-09: FIR Sequence Pedestrian Dataset

description: CVC-09: FIR Sequence Pedestrian Dataset
DOI: 10.57702/77m7woeu
title: Tooth Root Segmentation Dataset

description: Tooth root segmentation dataset with patients' consent, containing 248 root canal therapy X-ray images from different patients.
DOI: 10.57702/fhn4y57y
title: LightZero: A unified benchmark for Monte Carlo Tree Search in general sequential decision scenarios

description: The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used Atari environments and board games to evaluate the proposed algorithm.
DOI: 10.57702/3u27px8s
title: Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning

description: The dataset used in the paper is Metadrive, a driving simulator.
DOI: 10.57702/wgpeg5j4
title: Direct preference optimization: Your language model is secretly a reward model

description: The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used a language model to optimize the performance of a reinforcement learning algorithm.
DOI: 10.57702/89d6c19t
title: SEED: A Simple and Effective 3D DETR in Point Clouds

description: SEED is a 3D DETR framework for detecting 3D objects from point clouds. It involves a dual query selection module and a deformable grid attention module.
DOI: 10.57702/ss70hyrm
title: Moons, Checkerboards, Concentric Rings, Concentric Squares, Parallel Rings, and Parallel Squares

description: Moons, Checkerboards, Concentric Rings, Concentric Squares, Parallel Rings, and Parallel Squares are used for synthetic experiments.
DOI: 10.57702/2cbmd0g6
title: Facades and Maps

description: Facades and Maps are used for paired domain translation.
DOI: 10.57702/7qk4krtl
title: ImageNet Classes

description: ImageNet classes are used for conditional ImageNet translation.
DOI: 10.57702/mi12qr8j
title: MultiresUNet

description: The dataset used in this paper for medical image segmentation, including images from the ISBI-2012, CVC-ClinicDB, ISIC-2018, and DRIVE datasets.
DOI: 10.57702/1xoxhr2p
title: Encoder-Decoder with Atrous Separable Convolution

description: The dataset used in this paper for medical image segmentation, including images from the ISBI-2012 and CVC-ClinicDB datasets.
DOI: 10.57702/z4qucmzd
title: ESPNetv2

description: The dataset used in this paper for medical image segmentation, including images from the ISIC-2018 and CVC-ClinicDB datasets.
DOI: 10.57702/i542c90q
title: SEG C3

description: The dataset used in the paper for seismic data reconstruction
DOI: 10.57702/ll2z7cwq
title: Mobil Avo Viking Graben Line 12

description: The dataset used in the paper for seismic data reconstruction
DOI: 10.57702/26jc5k7r
title: Bottleneck Layer

description: The dataset used in this paper is a Bottleneck layer, which is a type of convolutional neural network layer. The dataset is used to evaluate the performance of the proposed heterogeneous system.
DOI: 10.57702/tjfnspid
title: CheMBL, BindingDB, ToxCat

description: A collection of molecules with known activity against various proteases
DOI: 10.57702/725bd3ld
title: ssEMnet: Serial-section Electron Microscopy Image Registration

description: The dataset used for serial-section electron microscopy (ssEM) image registration.
DOI: 10.6084/m9.figshare.27936756.v5
title: GSE185381 29_Nov_2024 sc_adult_AML

description: GSE185381 sc_adult_AML
DOI: 10.57702/qsgk7can
title: Waymo

description: Cross-LiDAR 3D detection dataset
DOI: 10.57702/fm8c48yg
title: PreSIL

description: Cross-LiDAR 3D detection dataset
DOI: 10.57702/nqdfiv72
title: YouTube-VOS 2018 and 2019

description: YouTube-VOS 2018 and 2019 datasets are benchmarks for video object segmentation, featuring various challenging scenarios.
DOI: 10.57702/3818dftq
title: FastSAM-3DSlicer

description: FastSAM-3DSlicer is a 3D Slicer extension for interactive volumetric segmentation of 3D medical images with SAMs.
DOI: 10.57702/7p1md4st
title: PFID

description: A food image dataset for training a generative model that learns the “image-to-energy mapping”.
DOI: 10.57702/w7cqawcz
title: Moongate

description: Moongate dataset consists of images of a moongate.
DOI: 10.57702/x830uq70
title: Hereditary Diseases (HD) dataset

description: The Hereditary Diseases (HD) dataset contains more than 550 biomedical articles classified into one of 26 hereditary diseases.
DOI: 10.57702/jugqu39e
title: Amazon review dataset

description: The Amazon review dataset is used for multi-source domain adaptation. It contains review texts and ratings of bought products. Products are grouped into categories. Following [66, 51], we perform tf-idf transformation and select the top 1, 000 frequent words. Ratings are used as the target labels.
DOI: 10.57702/h33pxswm
title: DiffuseStyleGesture+

description: The DiffuseStyleGesture+ entry to the GENEA Challenge 2023
DOI: 10.57702/e0dmjbhe
title: Talking With Hands

description: Talking With Hands 16.2 m: A large-scale dataset of synchronized body-finger motion and audio for conversational motion analysis and synthesis
DOI: 10.57702/o19uulx1
title: Beat

description: Beat: A large-scale semantic and emotional multi-modal dataset for conversational gestures synthesis
DOI: 10.57702/hac8pwek
title: Adaptive Clustering Transformer

description: End-to-end Object Detection with Adaptive Clustering Transformer
DOI: 10.57702/ktk521wa
title: MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion

description: MotionDiffuser is a learned representation for the joint distribution of future trajectories over multiple agents using diffusion models.
DOI: 10.57702/z320qmgz
title: Coverage-Aware Navigation for Cellular-Connected UAVs

description: The dataset used in this paper for coverage-aware navigation for cellular-connected UAVs.
DOI: 10.57702/4losfob9
title: ControlNet dataset

description: ControlNet dataset for image generation
DOI: 10.57702/xgnjv0h5
title: DeepLabCut dataset

description: DeepLabCut dataset for pose estimation
DOI: 10.57702/7z5471rc
title: S3VAE: Self-Supervised Sequential VAE

description: A sequential variational autoencoder for sequential data, including videos and audios, with self-supervision and regularization.
DOI: 10.57702/45esgq9e
title: Stochastic Moving MNIST

description: A dataset of stochastic moving MNIST, consisting of sequences of 15 frames of size 64x64.
DOI: 10.57702/03wzia4v
title: MUG Facial Expression

description: A dataset of MUG facial expression, consisting of videos of 52 actors performing 6 different facial expressions.
DOI: 10.57702/21p241rp
title: Multimodal Brain Tumor Image Segmentation Challenge (BraTS) 2018

description: Multimodal Brain Tumor Image Segmentation Challenge (BraTS) 2018 dataset used for brain tumor segmentation and overall survival prediction.
DOI: 10.57702/eit4ss5y
title: 3D Kimberlina dataset for full-waveform inversion

description: The 3D Kimberlina dataset is generated from hypothetical numerical models built on the geologic structure of a commercial-scale geologic carbon sequestration (GCS) reservoir at the Kimberlina site in the southern San Joaquin Basin, 30 km northwest of Bakersfield, CA, USA.
DOI: 10.57702/u5gg4t6i
title: Penn Treebank

description: The Penn Treebank dataset contains one million words of 1989 Wall Street Journal material annotated in Treebank II style, with 42k sentences of varying lengths.
DOI: 10.57702/ig58ixql
title: Yahoo Answers

description: The dataset Yahoo Answers contains 730,000 questions and answers.
DOI: 10.57702/23hdd7et
title: RSNA Pneumonia Detection Challenge dataset

description: The RSNA Pneumonia Detection Challenge dataset comprises 30,000 frontal-view CXR images, with each image labeled as “Normal,” “No Opacity/Not Normal,” or “Opacity” by one to three board-certiﬁed radiologists.
DOI: 10.57702/vp3hsjlt
title: Nordland Winter, Nordland Fall, Day-Right, St. Lucia

description: Visual place recognition datasets for testing the proposed Patch-DrosoNet algorithm
DOI: 10.57702/a6j3d7xj
title: Learning Graphon Autoencoders for Generative Graph Modeling

description: Graphon is a nonparametric model that generates graphs with arbitrary sizes and can be induced from graphs easily. Based on this model, we propose a novel algorithmic framework called graphon autoencoder to build an interpretable and scalable graph generative model.
DOI: 10.57702/p3av8yz5
title: MUTAG, PTC-MR, PROTEIN, ENZYMES, IMDB-B, and IMDB-M datasets

description: The MUTAG, PTC-MR, PROTEIN, ENZYMES, IMDB-B, and IMDB-M datasets are used to evaluate the performance of the proposed GNAE model.
DOI: 10.57702/oxmzts0a
title: IMDB-B and IMDB-M datasets

description: The IMDB-B and IMDB-M datasets are used to evaluate the transferability of the proposed GNAE model.
DOI: 10.57702/2bo4bn13
title: CIFAR-10, CIFAR-100, Stanford background dataset, VOC2012 dataset, Rotten Tomatoes dataset

description: The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used CIFAR-10 and CIFAR-100 datasets for image classification, and Stanford background dataset and VOC2012 dataset for semantic segmentation, and Rotten Tomatoes dataset for text classification.