dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
First-Person Hand Action Benchmark | **First-Person Hand Action Benchmark** is a collection of RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations. | Provide a detailed description of the following dataset: First-Person Hand Action Benchmark |
FIVR-200K | The FIVR-200K dataset has been collected to simulate the problem of Fine-grained Incident Video Retrieval (FIVR). The dataset comprises 225,960 videos associated with 4,687 Wikipedia events and 100 selected video queries. | Provide a detailed description of the following dataset: FIVR-200K |
FIW-MM | A large-scale dataset for recognizing kinship in multimedia which extend FIW with multimedia data (i.e., video, audio, and contextual transcripts). | Provide a detailed description of the following dataset: FIW-MM |
FLAME | FLAME is a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The dataset includes video recordings and thermal heatmaps captured by infrared cameras. The captured videos and images are annotated and labeled frame-wise to help researchers easily apply their fire detection and modeling algorithms. | Provide a detailed description of the following dataset: FLAME |
Flickr1024 | Contains 1024 pairs of high-quality images and covers diverse scenarios. | Provide a detailed description of the following dataset: Flickr1024 |
Flickr Cropping Dataset | The Flick Cropping Dataset consists of high quality cropping and pairwise ranking annotations used to evaluate the performance of automatic image cropping approaches.
Source: [https://arxiv.org/abs/1701.01480](https://arxiv.org/abs/1701.01480) | Provide a detailed description of the following dataset: Flickr Cropping Dataset |
Flightmare Simulator | Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently from each other. Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) an integration with a virtual-reality headset for interaction with the simulated environment. Flightmare can be used for various applications, including path-planning, reinforcement learning, visual-inertial odometry, deep learning, human-robot interaction, etc. | Provide a detailed description of the following dataset: Flightmare Simulator |
FLoRes | FLoRes is a benchmark dataset for machine translation between English and four low resource languages, Nepali, Sinhala, Khmer and Pashto, based on sentences translated from Wikipedia. | Provide a detailed description of the following dataset: FLoRes |
Florence 3D Faces | This dataset is being constructed specifically to support research on techniques that bridge the gap between 2D, appearance-based recognition techniques, and fully 3D approaches. It is designed to simulate, in a controlled fashion, realistic surveillance conditions and to probe the efficacy of exploiting 3D models in real scenarios. | Provide a detailed description of the following dataset: Florence 3D Faces |
FMD | The **Fluorescence Microscopy Denoising** (**FMD**) dataset is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. Image averaging is used to effectively obtain ground truth images and 60,000 noisy images with different noise levels.
Source: [https://arxiv.org/abs/1812.10366](https://arxiv.org/abs/1812.10366)
Image Source: [https://github.com/bmmi/denoising-fluorescence](https://github.com/bmmi/denoising-fluorescence) | Provide a detailed description of the following dataset: FMD |
fMoW | Functional Map of the World (fMoW) is a dataset that aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features. | Provide a detailed description of the following dataset: fMoW |
FocusPath | FocusPath is a dataset compiled from diverse Whole Slide Image (WSI) scans in different focus (z-) levels. Images are naturally blurred by out-of-focus lens provided with GT scores of focus levels. The dataset can be used for No-Reference Focus Quality assessment of Digital Pathology/Microscopy images. | Provide a detailed description of the following dataset: FocusPath |
FollowUp | 1000 query triples on 120 tables. | Provide a detailed description of the following dataset: FollowUp |
Fon-French Dataset | FFR Dataset is an ongoing project to collect, clean and store corpora of Fon and French sentences for machine translation from Fon-French. Fon (also called Fongbe) is an African-indigenous language spoken mostly in Benin, by about 1.7 million people. As training data is crucial to the high performance of a machine learning model, the aim of the project is to compile the largest set of training corpora for the research and design of translation and NLP models involving Fon. There are 117,029 parallel Fon-French sentences at the moment. | Provide a detailed description of the following dataset: Fon-French Dataset |
FoodX-251 | FoodX-251 is a dataset of 251 fine-grained classes with 118k training, 12k validation and 28k test images. Human verified labels are made available for the training and test images. The classes are fine-grained and visually similar, for example, different types of cakes, sandwiches, puddings, soups, and pastas. | Provide a detailed description of the following dataset: FoodX-251 |
Ford AV Dataset | A challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. | Provide a detailed description of the following dataset: Ford AV Dataset |
FPL | Supports new task that predicts future locations of people observed in first-person videos. | Provide a detailed description of the following dataset: FPL |
Frames Dataset | This dataset is dialog dataset collected in a Wizard-of-Oz fashion. Two humans talked to each other via a chat interface. One was playing the role of the user and the other one was playing the role of the conversational agent. The latter is called a wizard as a reference to the Wizard of Oz, the man behind the curtain. The wizards had access to a database of 250+ packages, each composed of a hotel and round-trip flights. The users were asked to find the best deal. This resulted in complex dialogues where a user would often consider different options, compare packages, and progressively build the description of her ideal trip. | Provide a detailed description of the following dataset: Frames Dataset |
Fraxtil | **Fraxtil** is an audio dataset where given a raw audio track, the goal is to produce a choreography step chart, similar to those used in the Dance Dance Revolution video game. It contains 90 songs choreographed by a single author, with 450 charts for the 90 songs.
Source: [https://arxiv.org/pdf/1703.06891.pdf](https://arxiv.org/pdf/1703.06891.pdf)
Image Source: [https://github.com/chrisdonahue/ddc](https://github.com/chrisdonahue/ddc) | Provide a detailed description of the following dataset: Fraxtil |
FreebaseQA | FreebaseQA is a data set for open-domain QA over the Freebase knowledge graph. The question-answer pairs in this data set are collected from various sources, including the TriviaQA data set and other trivia websites (QuizBalls, QuizZone, KnowQuiz), and are matched against Freebase to generate relevant subject-predicate-object triples that were further verified by human annotators. As all questions in FreebaseQA are composed independently for human contestants in various trivia-like competitions, this data set shows richer linguistic variation and complexity than existing QA data sets, making it a good test-bed for emerging KB-QA systems. | Provide a detailed description of the following dataset: FreebaseQA |
French CASS dataset | Composed of judgments from the French Court of cassation and their corresponding summaries. | Provide a detailed description of the following dataset: French CASS dataset |
FRSign | A large-scale and accurate dataset for vision-based railway traffic light detection and recognition.The recordings were made on selected running trains in France and benefited from carefully hand-labeled annotations. | Provide a detailed description of the following dataset: FRSign |
FSOCO | **FSOCO** is a collaborative dataset for vision-based cone detection systems in Formula Student Driverless competitions. It contains human annotated ground truth labels for both bounding boxes and instance-wise segmentation masks. The data buy-in philosophy of FSOCO asks student teams to contribute to the database first before being granted access ensuring continuous growth. By providing clear labeling guidelines and tools for a sophisticated raw image selection, new annotations are guaranteed to meet the desired quality. | Provide a detailed description of the following dataset: FSOCO |
FT Speech | FT Speech is a speech corpus created from the recorded meetings of the Danish Parliament, otherwise known as the Folketing (FT). The corpus contains over 1,800 hours of transcribed speech by a total of 434 speakers. It is significantly larger in duration, vocabulary, and amount of spontaneous speech than the existing public speech corpora for Danish, which are largely limited to read-aloud and dictation data. | Provide a detailed description of the following dataset: FT Speech |
FUNSD | Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. | Provide a detailed description of the following dataset: FUNSD |
Fusion 360 Gallery | The **Fusion 360 Gallery** Dataset contains rich 2D and 3D geometry data derived from parametric CAD models. The dataset is produced from designs submitted by users of the CAD package Autodesk Fusion 360 to the Autodesk Online Gallery. The dataset provides valuable data for learning how people design, including sequential CAD design data, designs segmented by modelling operation, and design hierarchy and connectivity data.
Source: [https://github.com/AutodeskAILab/Fusion360GalleryDataset](https://github.com/AutodeskAILab/Fusion360GalleryDataset)
Image Source: [https://github.com/AutodeskAILab/Fusion360GalleryDataset](https://github.com/AutodeskAILab/Fusion360GalleryDataset) | Provide a detailed description of the following dataset: Fusion 360 Gallery |
FVI | The Free-Form Video Inpainting dataset is a dataset used for training and evaluation video inpainting models. It consists of 1940 videos from the YouTube-VOS dataset and 12,600 videos from the YouTube-BoundingBoxes.
Source: [https://arxiv.org/abs/1904.10247](https://arxiv.org/abs/1904.10247)
Image Source: [https://github.com/amjltc295/Free-Form-Video-Inpainting](https://github.com/amjltc295/Free-Form-Video-Inpainting) | Provide a detailed description of the following dataset: FVI |
G1020 | A large publicly available retinal fundus image dataset for glaucoma classification called G1020. The dataset is curated by conforming to standard practices in routine ophthalmology and it is expected to serve as standard benchmark dataset for glaucoma detection. This database consists of 1020 high resolution colour fundus images and provides ground truth annotations for glaucoma diagnosis, optic disc and optic cup segmentation, vertical cup-to-disc ratio, size of neuroretinal rim in inferior, superior, nasal and temporal quadrants, and bounding box location for optic disc. | Provide a detailed description of the following dataset: G1020 |
GamePad Environment | GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. | Provide a detailed description of the following dataset: GamePad Environment |
GameWikiSum | **GameWikiSum** is a domain-specific (video game) dataset for multi-document summarization, which is one hundred times larger than commonly used datasets, and in another domain than news. Input documents consist of long professional video game reviews as well as references of their gameplay sections in Wikipedia pages.
Source: [https://github.com/Diego999/GameWikiSum](https://github.com/Diego999/GameWikiSum) | Provide a detailed description of the following dataset: GameWikiSum |
GAP Coreference Dataset | GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia and released by Google AI Language for the evaluation of coreference resolution in practical applications. | Provide a detailed description of the following dataset: GAP Coreference Dataset |
GASP | **GASP** is a dataset composed by a list of cited abstracts associated with the corresponding source abstract. The dataset is composed by a training set of 100000 elements, a test set and a validation set of 10000 each.
The goal is to generate a paper abstract given cited paper's abstracts and model the human creativity behind the process.
Source: [https://github.com/ART-Group-it/GASP](https://github.com/ART-Group-it/GASP) | Provide a detailed description of the following dataset: GASP |
Gazeta | **Gazeta** is a dataset for automatic summarization of Russian news. The dataset consists of 63,435 text-summary pairs. To form training, validation, and test datasets, these pairs were sorted by time and the first 52,400 pairs are used as the training dataset, the proceeding 5,265 pairs as the validation dataset, and the remaining 5,770 pairs as the test dataset.
Source: [https://github.com/IlyaGusev/gazeta](https://github.com/IlyaGusev/gazeta) | Provide a detailed description of the following dataset: Gazeta |
GCDC | A corpus of real-world texts. | Provide a detailed description of the following dataset: GCDC |
GDXray+ | GDXray+ is a collection of more than 21.100 X-ray images for the development, testing, and evaluation of image analysis and computer vision algorithms.
GDXray includes five groups of images:
* Castings,
* Welds,
* Baggage,
* Nature,
* Settings.
Each group has several series, and each series several X-ray images. | Provide a detailed description of the following dataset: GDXray+ |
GeBioCorpus | A high-quality dataset for machine translation evaluation that aims at being one of the first non-synthetic gender-balanced test datasets. | Provide a detailed description of the following dataset: GeBioCorpus |
General-100 | The **General-100** dataset is a dataset for image super-resolution. It contains 100 bmp format images with no compression) The size of the 100 images ranges from 710 x 704 (large) to 131 x 112 (small). | Provide a detailed description of the following dataset: General-100 |
Real Bacteria Dataset | A genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. | Provide a detailed description of the following dataset: Real Bacteria Dataset |
GeoCoV19 | GeoCoV19 is a large-scale Twitter dataset containing more than 524 million multilingual tweets. The dataset contains around 378K geotagged tweets and 5.4 million tweets with Place information. The annotations include toponyms from the user location field and tweet content and resolve them to geolocations such as country, state, or city level. In this case, 297 million tweets are annotated with geolocation using the user location field and 452 million tweets using tweet content. | Provide a detailed description of the following dataset: GeoCoV19 |
GeoFaces | A large database of geotagged face images. | Provide a detailed description of the following dataset: GeoFaces |
GeoWebNews | GeoWebNews provides test/train examples and enable fine-grained Geotagging and Toponym Resolution (Geocoding). This dataset is also suitable for prototyping and evaluating machine learning NLP models. | Provide a detailed description of the following dataset: GeoWebNews |
Get it #OffMyChest | Corpus and annotations for the CL-Aff Shared Task - Get it #OffMyChest - from Nanyang Technological University Singapore. | Provide a detailed description of the following dataset: Get it #OffMyChest |
GGPONC | German Guideline Program in Oncology NLP Corpus (GGPONC) is a German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. | Provide a detailed description of the following dataset: GGPONC |
GiantMIDI-Piano | **GiantMIDI-Piano** contains 10,854 unique piano solo pieces composed by 2,786 composers. GiantMIDI-Piano contains 34,504,873 transcribed notes, and contains metadata information of each music piece. | Provide a detailed description of the following dataset: GiantMIDI-Piano |
Gibson Environment | Gibson is an opensource perceptual and physics simulator to explore active and real-world perception. The Gibson Environment is used for Real-World Perception Learning. | Provide a detailed description of the following dataset: Gibson Environment |
Gigaword Entailment | The **Gigaword Entailment** dataset is a dataset for entailment prediction between an article and its headline. It is built from the Gigaword dataset.
Source: [https://github.com/nlp-titech/headline-entailment](https://github.com/nlp-titech/headline-entailment) | Provide a detailed description of the following dataset: Gigaword Entailment |
Global Voices | Global Voices is a multilingual dataset for evaluating cross-lingual summarization methods. It is extracted from social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages. | Provide a detailed description of the following dataset: Global Voices |
GloREPlus | A distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. | Provide a detailed description of the following dataset: GloREPlus |
Goldfinch | Goldfinch is a dataset for fine-grained recognition challenges. It contains a list of bird, butterfly, aircraft, and dog categories with relevant Google image search and Flickr search URLs. In addition, it also includes a set of active learning annotations on dog categories. | Provide a detailed description of the following dataset: Goldfinch |
GolfDB | GolfDB is a high-quality video dataset created for general recognition applications in the sport of golf, and specifically for the task of golf swing sequencing. | Provide a detailed description of the following dataset: GolfDB |
Sentence Compression | Sentence Compression is a dataset where the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence where supervised systems which require a structural alignment between the input and output can be successfully trained. | Provide a detailed description of the following dataset: Sentence Compression |
Google Landmarks Dataset v2 | This is the second version of the Google Landmarks dataset (GLDv2), which contains images annotated with labels representing human-made and natural landmarks. The dataset can be used for landmark recognition and retrieval experiments. This version of the dataset contains approximately 5 million images, split into 3 sets of images: train, index and test
Source: [https://github.com/cvdfoundation/google-landmark](https://github.com/cvdfoundation/google-landmark)
Image Source: [https://github.com/cvdfoundation/google-landmark](https://github.com/cvdfoundation/google-landmark) | Provide a detailed description of the following dataset: Google Landmarks Dataset v2 |
Google Refexp | A new large-scale dataset for referring expressions, based on MS-COCO. | Provide a detailed description of the following dataset: Google Refexp |
GOZ | The Generix Object Zero-shot Learning (**GOZ**) dataset is a benchmark dataset for zero-shot learning.
Source: [https://github.com/TristHas/GOZ](https://github.com/TristHas/GOZ) | Provide a detailed description of the following dataset: GOZ |
GRAB | **GRAB** is a dataset of full-body motions interacting and grasping 3D objects. It contains accurate finger and facial motions as well as the contact between the objects and body. It contains 5 male and 5 female participants and 4 different motion intents.
The GRAB dataset also contains binary contact maps between the body and objects.
Source: [https://github.com/otaheri/GRAB](https://github.com/otaheri/GRAB)
Image Source: [https://github.com/otaheri/GRAB](https://github.com/otaheri/GRAB) | Provide a detailed description of the following dataset: GRAB |
GRAL | A new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images. | Provide a detailed description of the following dataset: GRAL |
GraspNet | A large-scale grasp pose detection dataset with a unified evaluation system. The dataset contains 87,040 RGBD images with over 370 million grasp poses. | Provide a detailed description of the following dataset: GraspNet |
Grocery Store | **Grocery Store** is a dataset of natural images of grocery items. All natural images were taken with a smartphone camera in different grocery stores. It contains 5,125 natural images from 81 different classes of fruits, vegetables, and carton items (e.g. juice, milk, yoghurt). The 81 classes are divided into 42 coarse-grained classes, where e.g. the fine-grained classes 'Royal Gala' and 'Granny Smith' belong to the same coarse-grained class 'Apple'. Additionally, each fine-grained class has an associated iconic image and a product description of the item.
Source: [https://github.com/marcusklasson/GroceryStoreDataset](https://github.com/marcusklasson/GroceryStoreDataset)
Image Source: [https://github.com/marcusklasson/GroceryStoreDataset](https://github.com/marcusklasson/GroceryStoreDataset) | Provide a detailed description of the following dataset: Grocery Store |
Groningen Meaning Bank | Groningen Meaning Bank is a semantic resource that anyone can edit and that integrates various semantic phenomena, including predicate-argument structure, scope, tense, thematic roles, animacy, pronouns, and rhetorical relations. | Provide a detailed description of the following dataset: Groningen Meaning Bank |
GTA-IM Dataset | The **GTA Indoor Motion** dataset (GTA-IM) that emphasizes human-scene interactions in the indoor environments. It consists of HD RGB-D image sequences of 3D human motion from a realistic game engine. The dataset has clean 3D human pose and camera pose annotations, and large diversity in human appearances, indoor environments, camera views, and human activities.
Source: [https://github.com/ZheC/GTA-IM-Dataset](https://github.com/ZheC/GTA-IM-Dataset)
Image Source: [https://github.com/ZheC/GTA-IM-Dataset](https://github.com/ZheC/GTA-IM-Dataset) | Provide a detailed description of the following dataset: GTA-IM Dataset |
Gumar Corpus | A large-scale corpus of Gulf Arabic consisting of 110 million words from 1,200 forum novels. | Provide a detailed description of the following dataset: Gumar Corpus |
Gutenberg Dialog Dataset | This is a high-quality dataset consisting of 14.8M utterances in English, extracted from processed dialogues from publicly available online books.
Source: [https://github.com/ricsinaruto/gutenberg-dialog](https://github.com/ricsinaruto/gutenberg-dialog) | Provide a detailed description of the following dataset: Gutenberg Dialog Dataset |
Gutenberg Time Dataset | A data set of hourly time phrases from 52,183 fictional books. | Provide a detailed description of the following dataset: Gutenberg Time Dataset |
H3D | The H3D is a large scale full-surround 3D multi-object detection and tracking dataset. It is gathered from HDD dataset, a large scale naturalistic driving dataset collected in San Francisco Bay Area. H3D consists of following features:
* Full 360 degree LiDAR dataset (dense pointcloud from Velodyne-64)
* 160 crowded and highly interactive traffic scenes
* 1,071,302 3D bounding box labels
* 8 common classes of traffic participants (Manually annotated every 2Hz and linearly propagated for 10 Hz data)
* Benchmarked on state-of-the art algorithms for 3D only detection and tracking algorithms. | Provide a detailed description of the following dataset: H3D |
HAA500 | HAA500 is a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591k labeled frames. Unlike existing atomic action datasets, where coarse-grained atomic actions were labeled with action-verbs, e.g., "Throw", HAA500 contains fine-grained atomic actions where only consistent actions fall under the same label, e.g., "Baseball Pitching" vs "Free Throw in Basketball", to minimize ambiguities in action classification. HAA500 has been carefully curated to capture the movement of human figures with less spatio-temporal label noises to greatly enhance the training of deep neural networks. | Provide a detailed description of the following dataset: HAA500 |
Habitat Platform | A platform for research in embodied artificial intelligence (AI). | Provide a detailed description of the following dataset: Habitat Platform |
HAM | **HAM** is a dataset for molecular graph partitioning. This dataset contains coarse-grained (CG) mappings of 1206 organic molecules with less than 25 heavy atoms. Each molecule was downloaded from the PubChem database as SMILES. One molecule was assigned to two annotators to compare the human agreement between CG mappings. Downloaded SMILES were hand-mapped. The completed annotations were reviewed by a third person, to identify and remove unreasonable mappings (eg: one bead mappings) which did not agree with the given guidelines. Hence, there are 1.68 annotations per molecule in the current database (16% removed).
Source: [https://github.com/rochesterxugroup/HAM_dataset](https://github.com/rochesterxugroup/HAM_dataset) | Provide a detailed description of the following dataset: HAM |
HANS | The HANS (Heuristic Analysis for NLI Systems) dataset which contains many examples where the heuristics fail. | Provide a detailed description of the following dataset: HANS |
Hanabi Learning Environment | A new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. | Provide a detailed description of the following dataset: Hanabi Learning Environment |
HandNet | The HandNet dataset contains depth images of 10 participants' hands non-rigidly deforming in front of a RealSense RGB-D camera. The annotations are generated by a magnetic annotation technique. 6D pose is available for the center of the hand as well as the five fingertips (i.e. position and orientation of each). | Provide a detailed description of the following dataset: HandNet |
HappyDB | **HappyDB** is a corpus of 100,000 crowdsourced happy moments. | Provide a detailed description of the following dataset: HappyDB |
HarperValleyBank | The data simulate simple consumer banking interactions, containing about 23 hours of audio from 1,446 human-human conversations between 59 unique speakers. | Provide a detailed description of the following dataset: HarperValleyBank |
HASY | HASY is a dataset of single symbols similar to MNIST. It contains 168,233 instances of 369 classes. HASY contains two challenges: A classification challenge with 10 pre-defined folds for 10-fold cross-validation and a verification challenge. | Provide a detailed description of the following dataset: HASY |
Hateful Memes Challenge | A new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. | Provide a detailed description of the following dataset: Hateful Memes Challenge |
Hate Speech | Dataset of hate speech annotated on Internet forum posts in English at sentence-level. The source forum in Stormfront, a large online community of white nacionalists. A total of 10,568 sentence have been been extracted from Stormfront and classified as conveying hate speech or not. | Provide a detailed description of the following dataset: Hate Speech |
Hate Speech and Offensive Language | HSOL is a dataset for hate speech detection. The authors begun with a hate speech lexicon containing words and
phrases identified by internet users as hate speech, compiled by Hatebase.org. Using the Twitter API they searched
for tweets containing terms from the lexicon, resulting in a sample of tweets from 33,458 Twitter users. They extracted
the time-line for each user, resulting in a set of 85.4 million tweets. From this corpus they took a random sample of 25k tweets containing terms from the lexicon and had them manually coded by CrowdFlower (CF) workers. Workers were asked to label each tweet as one of three categories: hate speech, offensive but not hate speech, or neither offensive nor hate speech. | Provide a detailed description of the following dataset: Hate Speech and Offensive Language |
HCU400 | The dataset consists of the features associated with 402 5-second sound samples.
The 402 sounds range from easily identifiable everyday sounds to intentionally obscured artificial ones. The dataset aims to lower the barrier for the study of aural phenomenology as the largest available audio dataset to include an analysis of causal attribution. Each sample has been annotated with crowd-sourced descriptions, as well as familiarity, imageability, arousal, and valence ratings.
Source: [https://github.com/mitmedialab/HCU400](https://github.com/mitmedialab/HCU400) | Provide a detailed description of the following dataset: HCU400 |
HDD | Honda Research Institute Driving Dataset (HDD) is a dataset to enable research on learning driver behavior in real-life environments. The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. | Provide a detailed description of the following dataset: HDD |
HDR+ Burst Photography Dataset | The dataset consists of 3640 bursts (made up of 28461 images in total), organized into subfolders, plus the results of an image processing pipeline. Each burst consists of the raw burst input (in DNG format) and certain metadata not present in the images, as sidecar files. | Provide a detailed description of the following dataset: HDR+ Burst Photography Dataset |
Headlines dataset | The **Headlines dataset** for sarcasm detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events. The dataset includes all the headlines from News in Brief and News in Photos categories (which are sarcastic) and real (and non-sarcastic) news headlines from HuffPost.
This dataset has following advantages over the existing Twitter datasets:
* Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings.
* Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, the dataset has high-quality labels with much less noise as compared to Twitter datasets.
* Unlike tweets which are replies to other tweets, the obtained news headlines are self-contained. | Provide a detailed description of the following dataset: Headlines dataset |
HeadQA | HeadQA is a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. | Provide a detailed description of the following dataset: HeadQA |
HELP | The **HELP** dataset is an automatically created natural language inference (NLI) dataset that embodies the combination of lexical and logical inferences focusing on monotonicity (i.e., phrase replacement-based reasoning). The HELP (Ver.1.0) has 36K inference pairs consisting of upward monotone, downward monotone, non-monotone, conjunction, and disjunction. | Provide a detailed description of the following dataset: HELP |
HHOI | A new RGB-D video dataset, i.e., UCLA Human-Human-Object Interaction (HHOI) dataset, which includes 3 types of human-human interactions, i.e., shake hands, high-five, pull up, and 2 types of human-object-human interactions, i.e., throw and catch, and hand over a cup. On average, there are 23.6 instances per interaction performed by totally 8 actors recorded from various views. Each interaction lasts 2-7 seconds presented at 10-15 fps. | Provide a detailed description of the following dataset: HHOI |
HIDE | Consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes. | Provide a detailed description of the following dataset: HIDE |
HiEve | A new large-scale dataset for understanding human motions, poses, and actions in a variety of realistic events, especially crowd & complex events. It contains a record number of poses (>1M), the largest number of action labels (>56k) for complex events, and one of the largest number of trajectories lasting for long terms (with average trajectory length >480). Besides, an online evaluation server is built for researchers to evaluate their approaches. | Provide a detailed description of the following dataset: HiEve |
HIGGS Data Set | The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate the need for physicists to manually develop such features. Benchmark results using Bayesian Decision Trees from a standard physics package and 5-layer neural networks are presented in the original paper. The last 500,000 examples are used as a test set. | Provide a detailed description of the following dataset: HIGGS Data Set |
HindEnCorp | A parallel corpus of Hindi and English, and HindMonoCorp, a monolingual corpus of Hindi in their release version 0.5. Both corpora were collected from web sources and preprocessed primarily for the training of statistical machine translation systems. HindEnCorp consists of 274k parallel sentences (3.9 million Hindi and 3.8 million English tokens). HindMonoCorp amounts to 787 million tokens in 44 million sentences. | Provide a detailed description of the following dataset: HindEnCorp |
Hindi Visual Genome | Hindi Visual Genome is a multimodal dataset consisting of text and images suitable for English-Hindi multimodal machine translation task and multimodal research. | Provide a detailed description of the following dataset: Hindi Visual Genome |
HINT3 | **HINT3** is a dataset for intent detection. It consists of 3 different datasets each containing a diverse set of intents in a single domain - mattress products retail, fitness supplements retail and online gaming named SOFMattress, Curekart and Powerplay11.
Source: [https://github.com/hellohaptik/HINT3](https://github.com/hellohaptik/HINT3) | Provide a detailed description of the following dataset: HINT3 |
Bulgarian Reading Comprehension Dataset | A dataset containing 2,221 questions from matriculation exams for twelfth grade in various subjects -history, biology, geography and philosophy-, and 412 additional questions from online quizzes in history. | Provide a detailed description of the following dataset: Bulgarian Reading Comprehension Dataset |
HJDataset | HJDataset is a large dataset of Historical Japanese Documents with Complex Layouts. It contains over 250,000 layout element annotations of seven types. In addition to bounding boxes and masks of the content regions, it also includes the hierarchical structures and reading orders for layout elements. The dataset is constructed using a combination of human and machine efforts. | Provide a detailed description of the following dataset: HJDataset |
Hong Kong Cantonese corpus | The Hong Kong Cantonese Corpus was collected from transcribed conversations that were recorded between March 1997 and August 1998. About 230,000 Chinese words were collected in the annotated corpus. It contains recordings of spontaneous speech (51 texts) and radio programmes (42 texts), which involve 2 to 4 speakers, with 1 text of monologue. The text were word-segmented, annotated with part-of-speech tagging and Cantonese pronunciation using the romanisation scheme of Linguistic Society of Hong Kong (LSHK). | Provide a detailed description of the following dataset: Hong Kong Cantonese corpus |
HLA-Chat | Models character profiles and gives dialogue agents the ability to learn characters' language styles through their HLAs. | Provide a detailed description of the following dataset: HLA-Chat |
Hollywood 3D dataset | A dataset for benchmarking action recognition algorithms in natural environments, while making use of 3D information. The dataset contains around 650 video clips, across 14 classes. In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data. | Provide a detailed description of the following dataset: Hollywood 3D dataset |
Holopix50k | An in-the-wild stereo image dataset, comprising 49,368 image pairs contributed by users of the Holopix mobile social platform. | Provide a detailed description of the following dataset: Holopix50k |
HolStep | HolStep is a dataset based on higher-order logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. | Provide a detailed description of the following dataset: HolStep |
HoME | HoME (Household Multimodal Environment) is a multimodal environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. | Provide a detailed description of the following dataset: HoME |
HORAE | A new dataset of annotated pages from books of hours, a type of handwritten prayer books owned and used by rich lay people in the late middle ages. The dataset was created for conducting historical research on the evolution of the religious mindset in Europe at this period since the book of hours represent one of the major sources of information thanks both to their rich illustrations and the different types of religious sources they contain. | Provide a detailed description of the following dataset: HORAE |
Horne 2017 Fake News Data | The **Horne 2017 Fake News Data** contains two independed news datasets:
1. Buzzfeed Political News Data:
* News originally analyzed by Craig Silverman of Buzzfeed News in article entitled " This Analysis Shows How Viral Fake Election News Stories Outperformed Real News On Facebook."
* BuzzFeed News used keyword search on the content analysis tool BuzzSumo to find news stories
* Post the analysis of Buzzfeed News, the authors collect the body text and body title of all articles and use the ground truth as set by Buzzfeed as actual ground truth.
* This data set has fewer clear restrictions on the ground truth, including opinion-based real stories and satire-based fake stories. In our study, the authors manually filter this data set down to contain only "hard" news stories and malicious fake news stories. This repository contains the whole dataset with no filtering.
2. Random Political News Data:
* Randomly collected from three types of sources during 2016.
* Sources ground truth determined through: Business Insider’s “Most Trusted” list and Zimdars 2016 Fake news list
* Sources:
- Real: Wall Street Journal, The Economist, BBC, NPR, ABC, CBS, USA Today, The Guardian, NBC, The Washington Post
- Satire: The Onion, Huffington Post Satire, Borowitz Report, The Beaverton, Satire Wire, and Faking News
- Fake: Ending The Fed, True Pundit, abcnews.com.co, DC Gazette, Liberty Writers News, Before its News, InfoWars, Real News Right Now | Provide a detailed description of the following dataset: Horne 2017 Fake News Data |
HotelRec | Publicly available dataset in the hotel domain (50M versus 0.9M) and additionally, the largest recommendation dataset in a single domain and with textual reviews (50M versus 22M). | Provide a detailed description of the following dataset: HotelRec |
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