cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/avc7e2eh
title: OpenfMRI dataset

description: OpenfMRI dataset with T1 weighted anatomical MRI and rs-fMRI data.
DOI: 10.57702/olg828xm
title: Blocksworld Dataset

description: The Blocksworld dataset is a photo-realistic environment, where the goal is to move blocks around to achieve a goal state. The dataset consists of 480/2592 possible states/transitions, which are used to train the First-Order State AutoEncoder.
DOI: 10.57702/co12u23u
title: 3DFusion

description: Real-time 3D object reconstruction pipeline based on streamed instance segmented data
DOI: 10.57702/1gnccuzc
title: Argoverse-HD, Cityscapes, and nuScenes

description: The dataset used in the paper is Argoverse-HD, Cityscapes, and nuScenes.
DOI: 10.57702/l97fa8jz
title: Broad Bioimage Benchmark Collection (BBBC)

description: The proposed model uses the Broad Bioimage Benchmark Collection (BBBC) dataset to detect objects in images.
DOI: 10.57702/6li69fu8
title: A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation

description: A neural network-based framework to optimize the perceptions simulated by the in silico retinal implant model pulse2percept.
DOI: 10.57702/f75cjmxf
title: Synthetic Anomaly Detection

description: The Synthetic Anomaly Detection dataset is a time series classification dataset.
DOI: 10.57702/5ois0332
title: DDP: Diffusion Model for Dense Visual Prediction

description: The DDP framework is a simple, efficient, and powerful framework for dense visual predictions based on conditional diffusion.
DOI: 10.57702/2trxb09c
title: Healing MNIST

description: Krishnan et al. generated a data set called Healing MNIST, which is designed to reﬂect many properties that one also ﬁnds in real medical data.
DOI: 10.57702/roo72wpc
title: Video Games Level Corpus (VGLC)

description: A dataset of 150 classic Lode Runner levels.
DOI: 10.57702/9ms9f3am
title: Deeper, broader and artier domain generalization

description: Deeper, broader and artier domain generalization.
DOI: 10.57702/i3u46ae7
title: RealToxicityPrompts

description: RealToxicityPrompts constitutes a collection of 100k naturally occurring sentences, amassed from various internet sources and designed to function as LM prompts.
DOI: 10.57702/qqx3qox1
title: RedPajama

description: The RedPajama dataset is an open-source recipe to reproduce the LLaMA training dataset.
DOI: 10.57702/9afi6t5o
title: Falcon 7B
DOI: 10.57702/l75qdx0i
title: DIALOCONAN
DOI: 10.57702/62pxbedl
title: VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation

description: Text-to-3D generation using 2D visual prompts
DOI: 10.57702/gri9v79r
title: fastmri dataset

description: The fastmri dataset is used for MRI reconstruction. It contains fully sampled knee MR images and k-spaces from 1500 patients obtained from 1.5 and 3.0T scanners.
DOI: 10.57702/gq323y5f
title: Spherical-MNIST, atomic energy, Shrec17, diffusion MRI

description: The dataset used in this paper for classification tasks on spherical-MNIST, atomic energy, Shrec17 data sets, and group testing on diffusion MRI data.
DOI: 10.57702/m7pwoaly
title: QM7 dataset

description: The dataset used in this paper for regression task on atomization energies of molecules.
DOI: 10.57702/shdi2zzs
title: CIFAR-20

description: The dataset used in the paper is a subset of the CIFAR-100 dataset, consisting of 10,000 training samples randomly sampled from CIFAR-100's training samples corresponding to the "large carnivores", "large omnivores and herbivores", "medium mammals", and "vehicles 1" classes.
DOI: 10.57702/g8qf7bko
title: ArtBench (Post-Impressionism)

description: The dataset used in the paper is a subset of the ArtBench (Post-Impressionism) dataset, consisting of 5,000 training images.
DOI: 10.57702/b0vw4310
title: Stitching Sub-Trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL

description: Offline Goal-Conditioned Reinforcement Learning (Offline GCRL) is an important problem in RL that focuses on acquiring diverse goal-oriented skills solely from pre-collected behavior datasets.
DOI: 10.57702/n8mprvnw
title: Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images

description: Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields. However, their ability to create hyper-realistic images poses significant challenges in distinguishing between real and synthetic content, raising concerns about digital authenticity and potential misuse in creating deepfakes. This work introduces a robust detection framework that integrates image and text features extracted by CLIP model with a Multilayer Perceptron (MLP) classifier.
DOI: 10.57702/qls6u2x6
title: Diffusion-generated Deepfake Detection dataset (D3)

description: This dataset contains images generated by various text-to-image models, including Stable Diffusion 1.4, Stable Diffusion 2.1, Stable Diffusion XL, and DeepFloyd IF.
DOI: 10.57702/h0fqzbkg
title: auton-survival

description: Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization, and mortality. Such outcomes are typically subject to censoring due to loss of follow up. Standard machine learning methods cannot be applied in a straightforward manner to datasets with censored outcomes.
DOI: 10.5066/p13wcfna
title: Data in support of flow statistics for the Delaware River Basin

description: The U.S. Geological Survey (USGS) operates a dense network of streamgages within the Delaware River Basin (DRB) that provides near real-time and daily mean streamflow values, many of which have a decades long period of record. This long-term, historical dataset of daily mean streamflows is crucial for prediction of flows at ungaged stream reaches by means of parameter-based regression equations and can assist in informing water management and use decisions within the DRB. The USGS computed updated flow-duration exceedance probabilities for 98 current (2022) reference streamgages within the DRB with at least 10 years of record and minimally altered (nominal anthropogenic activity such as mining, diversion, or impoundment) streamflow regimes. The Make Plotting Position (MkPP; Granato, 2009) program with Weibull plotting position was used to calculate flow-duration exceedance probabilities for 21 streamflow percentiles. Percent difference comparison between these resulting flow-duration exceedances were made with observed and regression equation predicted streamflow values published by Stuckey (2016) for the 1960 through 2010 time period. A time-series trend analysis of 1979–2022 daily mean streamflows at 19 percentiles was computed using a non-parametric Mann-Kendall trend test (Hirsch and others, 2015). Associated basin characteristic raster layers that are currently used in StreamStats for the DRB, including percentage sand in soil, percentage poorly drained soils, percentage urban land use, mean annual precipitation, mean winter precipitation (December–February), and soil hydraulic conductivity, are included in this data release.

Citations:

Granato, G.E., 2009, Computer programs for obtaining and analyzing daily mean streamflow data from the U.S. Geological Survey National Water Information System Web Site: U.S. Geological Survey Open-File Report 2008–1362, 123 p. on CD-ROM, 5 appendixes, https://pubs.usgs.gov/publication/ofr20081362

Hirsch R.M., DeCicco L.A., 2015, User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data: U.S. Geological Survey Techniques and Methods 4-A10, https://pubs.usgs.gov/tm/04/a10/

Stuckey, M.H., 2016, Estimation of daily mean streamflow for ungaged stream locations in the Delaware River Basin, water years 1960–2010: U.S. Geological Survey Scientific Investigations Report 2015–5157, 42 p., https://doi.org/10.3133/sir20155157
DOI: 10.57702/m100ywss
title: ImageNet, MS COCO, and Pascal VOC datasets

description: The dataset used in the paper is ImageNet, MS COCO, and Pascal VOC datasets.
DOI: 10.57702/praj88u2
title: YOLOF dataset

description: The dataset used in the paper is YOLOF dataset.
DOI: 10.57702/wefpen1b
title: YOLOX-Nano dataset

description: The dataset used in the paper is YOLOX-Nano dataset.
DOI: 10.57702/g1j6fygm
title: DeepLabV3 dataset

description: The dataset used in the paper is DeepLabV3 dataset.
DOI: 10.57702/s57lbmk5
title: GALA

description: GALA: Generating Animatable Layered Assets from a Single Scan
DOI: 10.57702/qfdi1cjc
title: CAPE

description: CAPE dataset
DOI: 10.57702/txvqbn6y
title: AFVSGP-HOCBF

description: The dataset used in the paper for real-time adaptive safety-critical control with Gaussian processes in high-order uncertain models.
DOI: 10.57702/6b0i75n8
title: Roboschool

description: The dataset used in the ACE algorithm for continuous control problems.
DOI: 10.57702/aq0uyier
title: DPT-Large

description: A dataset of images used for training and testing the DPT-Large depth estimation model.
DOI: 10.57702/7saoh7m5
title: DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models

description: DiffuGen generates high-quality labeled image datasets using stable diffusion models.
DOI: 10.57702/ka6ag2b5
title: CO2 concentration dataset

description: The dataset used in the paper is a real CO2 concentration dataset.
DOI: 10.57702/9kglowwv
title: Bonobos S0

description: The Bonobo dataset, the classification models and the training methods presented are available on the Project GitHub page
DOI: 10.57702/pemgl0fg
title: Bonobos S0.5

description: The Bonobo dataset, the classification models and the training methods presented are available on the Project GitHub page
DOI: 10.57702/vk9atjty
title: CT Film Recovery via Disentangling Geometric Deformation and Illumination

description: The CTFilm20K dataset is a large-scale head CT film database containing approximately 20,000 pictures, with various real-world warping scenarios and different contents.
DOI: 10.57702/jh9upmvs
title: COCO-LT

description: The COCO-LT dataset is a long-tail version of the COCO dataset.
DOI: 10.57702/10ghn427
title: CIFAR-10, ImageNet and CelebA dataset

description: The dataset used in this paper is the CIFAR-10, ImageNet and CelebA dataset.
DOI: 10.57702/fna4roqa
title: ADE20K, Cityscapes, and COCO-Stuff

description: The dataset used for large-vocabulary semantic segmentation, including ADE20K, Cityscapes, and COCO-Stuff.
DOI: 10.57702/55fgtju2
title: Envision3D: One Image to 3D

description: Envision3D generates 32 dense view images and extracts high-quality 3D content from one input image in 3-4 minutes.
DOI: 10.57702/i26700xu
title: Google Scanned Object dataset

description: Google Scanned Object dataset contains 3D scanned household items.
DOI: 10.57702/b55l74tf
title: COCO-Stuff, Pascal-VOC, and Pascal-Context

description: The dataset used in the paper is COCO-Stuff, Pascal-VOC, and Pascal-Context. COCO-Stuff is an extensive semantic segmentation dataset comprising 171 categories, encompassing 80 things classes and 91 stuff classes. It contains 117k training images and 5k validation images and it is divided into 156 seen classes and 15 unseen classes. Pascal-VOC consists of 11,185 training images and 1,449 validation images across 20 classes. PASCAL Context provides supplementary annotations for PASCAL VOC 2010, consisting of 4,998 training images and 5,005 validation images.
DOI: 10.57702/hryubhz2
title: TNBC

description: A dataset used for medical image segmentation tasks
DOI: 10.57702/7a3ck6c3
title: CHAOS

description: This dataset contains abdominal CT and MRI images from 40 patients.
DOI: 10.57702/xt49u632
title: Covid-19

description: A dataset used for medical image segmentation tasks
DOI: 10.57702/tut6xruw
title: Heart

description: A dataset used for medical image segmentation tasks
DOI: 10.57702/f5p3c5le
title: Hippocampus

description: Medical image segmentation dataset for hippocampus images
DOI: 10.57702/qijplud2
title: Memorization in Unconditional Latent Diffusion Models

description: A chest X-ray dataset, a knee MRI dataset, and a PCCTA dataset were used to assess memorization in unconditional latent diffusion models for medical image synthesis.
DOI: 10.57702/eiwgxz1h
title: Panda-70M

description: A dataset for training text-to-video synthesis models
DOI: 10.57702/yaejtrl2
title: GlaS

description: The GlaS dataset is a collection of 612 Standard Definition (SD) frames from 31 sequences, each with a resolution of 384x288.
DOI: 10.57702/p8fohpjv
title: AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design

description: Structure-based drug design (SBDD), which aims to generate molecules that can bind tightly to the target protein, is an essential problem in drug discovery, and previous approaches have achieved initial success. However, most existing methods still suffer from invalid local structure or unrealistic conformation issues, which are mainly due to the poor learning of bond angles or torsional angles.
DOI: 10.57702/8eu94uqn
title: AFHQ

description: The dataset used in the paper is a set of images from the AFHQ dataset, containing 1.5K images of different animal faces.
DOI: 10.57702/nwxsef6y
title: COVID-19 CT Database (COV19-CT-DB)

description: COVID-19 CT Database (COV19-CT-DB) is a dataset used for Covid-19 Detection and Covid-19 Domain Adaptation Challenges.
DOI: 10.57702/aps74yt6
title: A large imaging database and novel deep neural architecture for covid-19 diagnosis

description: A large imaging database and novel deep neural architecture for covid-19 diagnosis.
DOI: 10.57702/xuz14bpg
title: Ai-enabled analysis of 3-d ct scans for diagnosis of covid-19 & its severity

description: Ai-enabled analysis of 3-d ct scans for diagnosis of covid-19 & its severity.
DOI: 10.57702/xkd1g8pl
title: Transparent adaptation in deep medical image diagnosis

description: Transparent adaptation in deep medical image diagnosis.
DOI: 10.57702/pb6ec1hm
title: GraspNet-1Billion

description: GraspNet-1Billion is a large-scale real-world grasping dataset containing 190 cluttered grasping scenes and 97,280 RGB-D images captured by 2 kinds of RGB-D cameras from 256 different views.
DOI: 10.57702/45ny7jzc
title: Facial Makeup Transfer Dataset

description: This dataset is used for facial makeup transfer.
DOI: 10.57702/svdex3z7
title: Text-to-Image Synthesis Dataset

description: This dataset is used for text-to-image synthesis.
DOI: 10.57702/xv5cl3f6
title: Deep Convolutional Generative Adversarial Networks

description: The authors used three datasets: Large-scale Scene Understanding (LSUN), Imagenet-1k, and a newly assembled Faces dataset.
DOI: 10.57702/wnwtpj90
title: Vimeo90k dataset

description: The Vimeo90k dataset is a large-scale video frame interpolation dataset.
DOI: 10.57702/gdfx9bam
title: Covariate Feature Dataset

description: COVID-19 data and SafeGraph POI based Mobility Data
DOI: 10.57702/wpl90eko
title: CIFAR-10, CelebA-HQ, LSUN-Church and LSUN-Bedrooms

description: The dataset used in this paper is CIFAR-10, CelebA-HQ, LSUN-Church and LSUN-Bedrooms.
DOI: 10.57702/ui2ausel
title: Network-based malware detection on mobile devices

description: Network-based malware detection on mobile devices
DOI: 10.57702/63yjdlir
title: Ember: an open dataset for training static pe malware machine learning models

description: Ember: an open dataset for training static pe malware machine learning models
DOI: 10.57702/46jr1588
title: Matching Latent Encoding for Audio-Text based Keyword Spotting

description: The proposed end-to-end model architecture for flexible keyword spotting, consisting of encoder, projector, and audio-text aligner modules.
DOI: 10.57702/xvg4jrkz
title: BERT: Pre-training of deep bidirectional transformers for language understanding

description: This paper proposes BERT, a pre-trained deep bidirectional transformer for language understanding.
DOI: 10.57702/tfihu705
title: Time Series Simulation by Conditional Generative Adversarial Net

description: The dataset used in this paper is a time series data, which is used to test the performance of the Conditional Generative Adversarial Net (CGAN) method.
DOI: 10.57702/6ku9htht
title: EEG Synthetic Data Generation Using Probabilistic Diﬀusion Models

description: EEG Synthetic Data Generation Using Probabilistic Diﬀusion Models
DOI: 10.57702/8x98hfpm
title: CIFAR-10, CIFAR-100, Tiny-Imagenet, and Imagenet datasets

description: The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, Tiny-Imagenet, and Imagenet datasets.
DOI: 10.57702/3fgzpqor
title: Airfoil, Exchange-Rate, and Electricity datasets

description: The dataset used in the paper is not explicitly described, but it is mentioned that the authors used Airfoil, Exchange-Rate, and Electricity datasets.
DOI: 10.57702/d4guo0gb
title: Planning CT and structure datasets for 120 intact prostate cancer patients

description: Planning CT and structure datasets for 120 intact prostate cancer patients were retrospectively selected and divided for 10-fold cross-validation.
DOI: 10.57702/a9en4usv
title: MDD Dataset

description: This public dataset was published by Mumtaz et al. It consists of EEG recordings from 34 MDD patients and 30 HCs.
DOI: 10.57702/7uuyofll
title: SNLI

description: The dataset used in the paper is the Stanford Natural Language Inference (SNLI) dataset, which consists of 549,367 premise-hypothesis pairs for train/dev/test sets and target labels indicating their relation.
DOI: 10.57702/k6zi33tn
title: Training and Validation Datasets

description: A collection of datasets used for training and validation of the S-MEDSeg model.
DOI: 10.57702/7okuh6dp
title: tic-tac-toe, breast-cancer, poker, and mnist

description: The dataset used in the paper is a classification dataset, specifically tic-tac-toe, breast-cancer, poker, and mnist.
DOI: 10.57702/c6sd1f7q
title: poker

description: The dataset used in the paper is a classification dataset, specifically poker.
DOI: 10.57702/wiijqub6
title: CIFAR-10, CIFAR-100, and MNIST

description: The dataset used in the paper is a benchmark dataset for diffusion models, specifically denoising diffusion probabilistic models (DDPM). The dataset consists of images from CIFAR-10, CIFAR-100, and MNIST.
DOI: 10.57702/e7wwo0zk
title: Reading digits in natural images with unsupervised feature learning

description: The paper presents a method for reading digits in natural images using unsupervised feature learning.
DOI: 10.57702/zz8w5z3w
title: T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations

description: Generating motion from textual descriptions can be used in numerous applications in the game industry, ﬁlm-making, and animating robots. For example, a typical way to access new motion in the game industry is to perform motion capture, which is expensive. Therefore automatically generating motion from textual descriptions, which allows producing meaningful motion data, could save time and be more economical.
DOI: 10.57702/cwv4xff7
title: PatternNet, RSICD, RESISC45, MLRSNet, EuroSat

description: The dataset used in the paper is a remote sensing image classification dataset, which includes five optical remote sensing (RS) image classification benchmarks.
DOI: 10.57702/5f5fx0ji
title: VQA 2.0

description: The VQA 2.0 dataset is used for visual question answering task. It consists of three sets with a train set containing 83k images and 444k questions, a validation set containing 41k images and 214k questions, and a test set containing 81k images and 448k questions.
DOI: 10.57702/5vayq3qz
title: dSprites2, Cars3D, and SmallNorb

description: The dataset used in this paper is dSprites2, Cars3D, and SmallNorb.
DOI: 10.57702/79urwmkv
title: Symsol_reduced and Celeba

description: The dataset used in this paper is Symsol_reduced and Celeba.
DOI: 10.57702/ulr64jll
title: Pendulum and Reacher

description: The Pendulum swing-up task, the agent tries to keep the pendulum upright and balanced under the constraint of keeping away from unsafe angles. In the Reacher task, the robotic arm needs to move towards the goal while avoiding the unsafe region.
DOI: 10.57702/ey8qzki7
title: False Negative/Positive Control for SAM on Noisy Medical Images

description: Kidney and placenta ultrasound datasets for medical image segmentation
DOI: 10.57702/g6e5h0rj
title: Free-Bloom: Zero-Shot Text-to-Video Generator

description: Text-to-video is a rapidly growing research area that aims to generate a semantic, identical, and temporal coherence sequence of frames that accurately align with the input text prompt.
DOI: 10.57702/y29mnw1b
title: Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation

description: Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation.
DOI: 10.57702/vldovvqh
title: Videofusion: Decomposed diffusion models for high-quality video generation

description: Videofusion: Decomposed diffusion models for high-quality video generation.
DOI: 10.57702/tbrovv1c
title: Text2video-zero: Text-to-image diffusion models are zero-shot video generators

description: Text2video-zero: Text-to-image diffusion models are zero-shot video generators.
DOI: 10.57702/wk46127z
title: SynthText

description: SynthText dataset is proposed by Gupta et al. for scene text detection. The original dataset is composed of 800,000 scene text images, each with multiple word instances.
DOI: 10.57702/pzx56tal
title: IIIT5k

description: Scene text recognition using higher order language priors
DOI: 10.57702/juslwezd
title: BTCV

description: The Swin Transformer-based TransMorph dataset is used for deformable image registration. The dataset consists of 50 abdominal CT scans, each scan with 13 organ annotations.
DOI: 10.57702/5c7ldj53
title: VGDiffZero: Text-to-Image Diffusion Models Can Be Zero-Shot Visual Grounders

description: VGDiffZero is a zero-shot visual grounding framework that leverages pre-trained text-to-image diffusion models' vision-language alignment abilities.
DOI: 10.57702/w4gv8oxb
title: RefCOCO

description: The dataset used in the paper is a benchmark for referring expression grounding, containing 142,210 referring expressions for 50,000 referents in 19,994 images.
DOI: 10.57702/basawtmd
title: Community Battery Dataset

description: The dataset used in this paper is a community battery model comprising 60 residential homes, each equipped with a 2 kWp solar PV system.
DOI: 10.57702/gjtau5so
title: ImageNet-Compatible and CIFAR-10 datasets

description: The authors used the ImageNet-Compatible and CIFAR-10 datasets for targeted attack experiments.
DOI: 10.57702/86kxdsej
title: ImageNet-1K and MS COCO

description: The dataset used in the paper is ImageNet-1K and MS COCO.
DOI: 10.57702/l3uixjbj
title: SCD RBC dataset

description: The SCD RBC dataset consists of 314 microscopy images (250 for train and 64 for test) from 5 Sickle Cell Disease patients.
DOI: 10.57702/lyz9jxx0
title: Cart-Pole, Pendulum, and Cart-Pole Balance Environments

description: The dataset used in this paper is a set of three classic control environments: cart-pole swing-up (CPSU), pendulum swing-up (PSU), and cart-pole balance (CPB).
DOI: 10.5281/zenodo.12736545
title: Continuum Normalized MARCS synthetic spectra (DR16)

description: Continuum Normalized MARCS synthetic spectra (DR16 MARCS) following the procedure described in Appendix A1 of the research article tonalli: an asexual genetic code to characterize APOGEE-2 stellar spectra. I. Validation with synthetic and solar spectra.
DOI: 10.57702/o00kkyb4
title: Law School Admission Bar Passage

description: The dataset used in the paper for fair subgroup mixup for improving group fairness.
DOI: 10.57702/bjjmwav8
title: Benchmark Fair Classification Dataset

description: The dataset used in the paper for fair subgroup mixup for improving group fairness.
DOI: 10.57702/sqb66f42
title: CIFAR-10/100 and Imagenette datasets

description: The dataset used in this paper is CIFAR-10/100 and Imagenette datasets.