cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/pr74qw1b
title: Stanford-Cars, Oxford-Flowers102, Oxford-IIIT Pets, FGVC Aircraft, CIFAR-10 datasets

description: Stanford-Cars, Oxford-Flowers102, Oxford-IIIT Pets, FGVC Aircraft, CIFAR-10 datasets.
DOI: 10.57702/d730k8fu
title: FK-RFE: A Model-Free Feature Selection Procedure for Ultra-High Dimensional Data

description: A model-free feature selection procedure for ultra-high dimensional data with mass features.
DOI: 10.57702/o01pf2wj
title: FisHook - An Optimized Approach to Marine Species Classification using MobileNetV2

description: A marine species classification using MobileNetV2 model
DOI: 10.57702/u2a4vqip
title: MOKA: Moral Knowledge Augmentation for Moral Event Extraction

description: MORAL EVENTS: a new dataset for moral event extraction, consisting of 5,494 structured event annotations on 474 news articles by diverse US media across the political spectrum.
DOI: 10.57702/msdhvr3u
title: A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

description: Generative models produce realistic objects in many domains, including text, image, video, and audio synthesis. Most popular models—Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)—usually employ a standard Gaussian distribution as a prior. Previous works show that the richer family of prior distributions may help to avoid the mode collapse problem in GANs and to improve the evidence lower bound in VAEs.
DOI: 10.57702/ha1qjhl2
title: CelebFaces Attributes Dataset (CelebA)

description: CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset containing 10,177 celebrities images, each of which has 20 images.
DOI: 10.57702/z3or9i9z
title: DocRED

description: DocRED is a large-scale human-annotated dataset for document-level RE, which is constructed from Wikipedia and Wikidata.
DOI: 10.57702/8z730c9e
title: Re-DocRED

description: A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling
DOI: 10.57702/4nz3lndm
title: Graph Neural Networks Including SparSe inTerpretability (GISST)

description: Graph Neural Networks (GNNs) are versatile, powerful machine learning methods that enable graph structure and feature representation learning, and have applications across many domains. For applications critically requiring interpretation, attention-based GNNs have been leveraged. However, these approaches either rely on specific model architectures or lack a joint consideration of graph structure and node features in their interpretation. Here we present a model-agnostic framework for interpreting important graph structure and node features, Graph neural networks Including SparSe inTerpretability (GISST). With any GNN model, GISST combines an attention mechanism and sparsity regularization to yield an important subgraph and node feature subset related to any graph-based task.
DOI: 10.57702/7paac2j5
title: MAWPS

description: MAWPS is a Chinese math word problem benchmark.
DOI: 10.57702/zzcqcima
title: IMDB Sentiment Classification

description: The IMDB sentiment classification dataset is used for text classification tasks.
DOI: 10.57702/0rrugg9d
title: NIST-SRE 2016

description: The NIST-SRE 2016 dataset contains recordings of speakers from different domains.
DOI: 10.57702/d6rbejtm
title: Fission Gas Bubble Segmentation on U-10Zr Annular Fuels

description: The dataset is used for fission gas bubble segmentation on U-10Zr annular fuels.
DOI: 10.57702/em2u1jih
title: Covertype

description: The dataset used in the paper is a Big Data classification problem.
DOI: 10.15468/dl.2vyfx7
title: Occurrence Download

description: A dataset containing 301 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "TaxonKey is Stangeria eriopus (Kunze) Baill."
 ]
}

 The dataset includes 301 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014169-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/t4e5rwap
title: Ultra-Range Gesture Recognition dataset

description: A dataset for training the HQ-Net model and the GViT model for ultra-range gesture recognition.
DOI: 10.57702/t1gx7jnm
title: Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free ATtention Masks

description: The dataset used in the paper is a large-scale dataset for unsupervised learning on diffusion-generated images.
DOI: 10.57702/m99pne8w
title: Cityspace

description: The dataset used for training and testing the proposed RGBD-based obstacle avoidance system for visually impaired people.
DOI: 10.57702/0m2d7xod
title: ImageNet and Cifar10

description: The dataset used for the ImageNet benchmark and the Cifar10 benchmark.
DOI: 10.57702/xqwc80xp
title: MultiStudioBench

description: The MultiStudioBench dataset contains 25 subjects, including objects, animals, etc., and there are few images for each subject. Images in the dataset are from previous works or collected by the authors.
DOI: 10.57702/rhwywkc5
title: ASVspoof 2019

description: The ASVspoof 2019 dataset is a large-scale public dataset for speaker verification and spoofing countermeasures. The dataset contains various types of audio files, including real and fake audio, with different levels of noise and background music.
DOI: 10.57702/i87z881y
title: ASVspoof 2015 and ASVspoof 2017

description: ASVspoof 2015 and ASVspoof 2017 datasets are used for automatic speaker verification and spoofing detection. The datasets contain genuine and spoofed speech samples for logical and physical access scenarios.
DOI: 10.57702/5k5rw7qh
title: Polyp Segmentation in Endoscopic Images

description: Polyp Segmentation in Endoscopic Images dataset, RETOUCH dataset
DOI: 10.57702/mo3crise
title: High Performance Visual Object Tracking with Uniﬁed Convolutional Networks

description: Visual object tracking is a fundamental problem in many aspects such as visual analysis, automatic driving, pose tracking, robotics, and more.
DOI: 10.57702/my7fvrx8
title: Object Tracking Benchmark

description: The OTB100 dataset is an extension of the OTB50 dataset, containing 100 videos with 1000 frames each.
DOI: 10.57702/46odbrd5
title: Visual Object Tracking VOT2015 Challenge Results

description: The visual object tracking vot2015 challenge results
DOI: 10.57702/e4qxl22h
title: Selection of Observation Position and Orientation in Visual Servoing

description: Selection of observation position and orientation in visual servoing with eye-in-vehicle conﬁguration for manipulator
DOI: 10.57702/7i9q7q85
title: Std: A Stereo Tracking Dataset

description: Std: A stereo tracking dataset for evaluating binocular tracking algorithms
DOI: 10.57702/cnr1nz9b
title: EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits

description: The authors used four real-world datasets: MNIST, Yelp, MovieLens, and Disin.
DOI: 10.57702/hzkw0v5z
title: Fashion MNIST dataset

description: The Fashion MNIST dataset is a large dataset of fashion images, each image is a 28x28 grayscale image, and there are 60,000 training images, 10,000 validation images, and 10,000 test images.
DOI: 10.57702/ky881901
title: Self-built SAR aircraft sliced dataset and large scene SAR image

description: A self-built SAR aircraft sliced dataset and a large scene SAR image are collected for investigating the detection performance of our method in the experiments.
DOI: 10.57702/0125i72t
title: Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations

description: Sea ice at the North Pole is vital to global climate dynamics. However, accurately forecasting sea ice poses a significant challenge due to the intricate interaction among multiple variables. Leveraging the capability to integrate multiple inputs and powerful performances seamlessly, many studies have turned to neural networks for sea ice forecasting.
DOI: 10.57702/s0na3onr
title: Smallest Algae Thrive as the Arctic Ocean Freshens

description: Smallest algae thrive as the Arctic Ocean freshens. Science, 326:539 – 539.
DOI: 10.57702/pupmxvgg
title: Towards Greener Nights: Exploring AI-Driven Solutions for Light Pollution Management

description: A dataset for light pollution management, containing observations of night sky brightness and other relevant variables.
DOI: 10.57702/8x3frq3i
title: Human3.6M dataset

description: The Human3.6M dataset is a large-scale dataset for 3D human pose estimation in videos. It consists of 3.6 million frames captured by four 50 Hz cameras.
DOI: 10.57702/urle1zln
title: Self-distillation with Online Diﬀusion on Batch Manifolds Improves Deep Metric Learning

description: Deep metric learning (DML) methods typically leverage solely class labels to keep positive samples far away from negative ones. However, this type of method normally ignores the crucial knowledge hidden in the data (e.g., intra-class information variation), which is harmful to the generalization of the trained model. To alleviate this problem, in this paper we propose Online Batch Diﬀusion-based Self-Distillation (OBD-SD) for DML.
DOI: 10.15468/dl.zptsad
title: Occurrence Download

description: A dataset containing 4304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lagocephalus sceleratus (Gmelin, 1789)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4304 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014171-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/uxay2p7u
title: Real SAR Images with Background

description: Real SAR images with background. The real-measured dataset is MSTAR with SAR images of ground stationary targets released by the MSTAR program supported by the Defense Advanced Research Projects Agency (DARPA) of the United States.
DOI: 10.15468/dl.nw7skd
title: Occurrence Download

description: A dataset containing 848 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidorhombus boscii (Risso, 1810)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 848 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014173-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/raha1vic
title: Autoencoder& GANs for Imbalanced Multi-Omics

description: The proposed model is applied to two publicly available datasets, the first is the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) dataset, which contains DNA Methylation, Copy Number Alteration (CNA), and Gene information. The second is the TCGA Bladder Urothelial Carcinoma (BLCA) dataset, which contains DNA Methylation, CNA and mRNA
DOI: 10.57702/kg7tm5o2
title: FVC: A New Framework towards Deep Video Compression in Feature Space

description: A video compression framework that performs all operations in the feature space
DOI: 10.15468/dl.wu5376
title: Occurrence Download

description: A dataset containing 6192 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidopus caudatus (Euphrasen, 1788)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6192 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014172-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc110875861
title: Persepolis Fortification Elamite Tablet 0502, PF-NN 0502

description: [Text Description:] Administrative text with seal ❧ [Text or Publication Number Note:] PF-NN 0502 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. Seal impressions on on left edge, right edge. ❧ [Physical Object Note:] Damage or cracks on bottom edge, left edge, obverse, right edge and reverse.
DOI: 10.15468/dl.6szcze
title: Occurrence Download

description: A dataset containing 323521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, United Kingdom of Great Britain and Northern Ireland, Svalbard and Jan Mayen, Norway, Sweden, Finland, Estonia, Latvia, Lithuania, Portugal, Spain, France, Germany, Belgium, Netherlands, Switzerland, Austria, Italy, Greece, Cyprus, Türkiye, Russian Federation, Belarus, Ukraine, Poland, Czechia, Hungary, Slovakia, Romania, Bulgaria, North Macedonia, Albania, Montenegro, Serbia, Kosovo, Bosnia and Herzegovina, Slovenia, Croatia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Symphytum officinale L."
 ]
}

 The dataset includes 323521 records from 1435 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061330-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc111107589
title: Temple administration record, UT 1599-1

description: [Text Description:] Temple administrative record of receipts and disbursements of food rations. ❧ [Text or Publication Number Note:] UT 1599-1 ❧ [Excavation Description:] Drehem ❧ [Medium:] Clay ❧ [Script Note:] Cuneiform, Non-Alphabetic ❧ [Physical Object Description:] Large rectangular clay tablet, orange-red color. 15.5 x 13.5cm. ❧ [Physical Object Note:] Reign of Shu-Sin, fourth dynastic king.
DOI: 10.15468/dl.t57tqq
title: Occurrence Download

description: A dataset containing 460808 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-29.67115 14.65197,60.32948 14.65197,60.32948 30.30425,-29.67115 30.30425,-29.67115 14.65197))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 460808 records from 312 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0425881-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1067510.10fu
title: SH1067510.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1067513.10fu
title: SH1067513.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1067516.10fu
title: SH1067516.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1067521.10fu
title: SH1067521.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1067523.10fu
title: SH1067523.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1067525.10fu
title: SH1067525.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1067526.10fu
title: SH1067526.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1067529.10fu
title: SH1067529.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.