dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
Melinda | Introduces a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. The dataset is collected in a fully automated distant supervision manner, where the labels are obtained from an existing curated database, and the actual contents are extracted from papers associated with each of the records in the database. | Provide a detailed description of the following dataset: Melinda |
Memeify | A large-scale dataset of memes with captions and class labels. The dataset consists of 1.1 million meme captions from 128 classes. | Provide a detailed description of the following dataset: Memeify |
MetaLWOz | Collected by leveraging background knowledge from a larger, more highly represented dialogue source. | Provide a detailed description of the following dataset: MetaLWOz |
Metaphorics | Metaphorics is a newly introduced non-contextual skeleton action dataset. All the datasets introduced so far in the skeleton human action recognition have categories based only on verb-based actions. | Provide a detailed description of the following dataset: Metaphorics |
Meta-World Benchmark | An open-source simulated benchmark for meta-reinforcement learning and multi-task learning consisting of 50 distinct robotic manipulation tasks. | Provide a detailed description of the following dataset: Meta-World Benchmark |
methods2test | **methods2test** is a supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java software repositories.
Methods2test was constructed by parsing the Java projects to obtain classes and methods with their associated metadata. Next each Test Class was matched to its corresponding Focal Class. Finally, each Test Case within a Test Class was mapped to the related Focal Method to obtain a set of Mapped Test Cases.
Source: [https://github.com/microsoft/methods2test](https://github.com/microsoft/methods2test) | Provide a detailed description of the following dataset: methods2test |
#MeTooMA | The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories. | Provide a detailed description of the following dataset: #MeTooMA |
AraMeter | A dataset to identify the meters of Arabic poems. | Provide a detailed description of the following dataset: AraMeter |
METU-ALET | **METU-ALET** is an image dataset for the detection of the tools in the wild. The dataset has annotations for tools that belongs to the categories such as farming, gardening, office, stonemasonry, vehicle, woodworking and workshop. The images in the dataset contains a total of 22,841 bounding boxes and 49 different tool categories.
Source: [https://github.com/metu-kovan/METU-ALET](https://github.com/metu-kovan/METU-ALET)
Image Source: [https://github.com/metu-kovan/METU-ALET](https://github.com/metu-kovan/METU-ALET) | Provide a detailed description of the following dataset: METU-ALET |
MEVA | Large-scale dataset for human activity recognition. Existing security datasets either focus on activity counts by aggregating public video disseminated due to its content, which typically excludes same-scene background video, or they achieve persistence by observing public areas and thus cannot control for activity content. The dataset is over 9300 hours of untrimmed, continuous video, scripted to include diverse, simultaneous activities, along with spontaneous background activity. | Provide a detailed description of the following dataset: MEVA |
Mewsli-9 | A large new multilingual dataset for multilingual entity linking. | Provide a detailed description of the following dataset: Mewsli-9 |
MEx | A multi-sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD). | Provide a detailed description of the following dataset: MEx |
Microsoft Research Social Media Conversation Corpus | Microsoft Research Social Media Conversation Corpus consists of 127M context-message-response triples from the Twitter FireHose, covering the 3-month period June 2012 through August 2012. Only those triples where context and response were generated by the same user were extracted. To minimize noise, only triples that contained at least one frequent bigram that appeared more than 3 times in the corpus was selected. This produced a corpus of 29M Twitter triples. | Provide a detailed description of the following dataset: Microsoft Research Social Media Conversation Corpus |
Mid-Air Dataset | Mid-Air, The Montefiore Institute Dataset of Aerial Images and Records, is a multi-purpose synthetic dataset for low altitude drone flights. It provides a large amount of synchronized data corresponding to flight records for multi-modal vision sensors and navigation sensors mounted on board of a flying quadcopter. Multi-modal vision sensors capture RGB pictures, relative surface normal orientation, depth, object semantics and stereo disparity. | Provide a detailed description of the following dataset: Mid-Air Dataset |
MIDAS-KIKI | Consists of manually annotated dangerous and non-dangerous Kiki challenge videos. | Provide a detailed description of the following dataset: MIDAS-KIKI |
MIDV-2019 | Contains video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions. | Provide a detailed description of the following dataset: MIDV-2019 |
MilkQA | A question answering dataset from the dairy domain dedicated to the study of consumer questions. The dataset contains 2,657 pairs of questions and answers, written in the Portuguese language and originally collected by the Brazilian Agricultural Research Corporation (Embrapa). All questions were motivated by real situations and written by thousands of authors with very different backgrounds and levels of literacy, while answers were elaborated by specialists from Embrapa's customer service. | Provide a detailed description of the following dataset: MilkQA |
MIMII | **Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection** (MIMII) is a sound dataset
of industrial machine sounds. | Provide a detailed description of the following dataset: MIMII |
MINC | MINC is a large-scale, open dataset of materials in the wild. | Provide a detailed description of the following dataset: MINC |
MinneApple | **MinneApple** is a benchmark dataset for apple detection and segmentation. The fruits are labelled using polygonal masks for each object instance to aid in precise object detection, localization, and segmentation. Additionally, the dataset also contains data for patch-based counting of clustered fruits. The dataset contains over 41, 000 annotated object instances in 1000 images.
Source: [https://github.com/nicolaihaeni/MinneApple](https://github.com/nicolaihaeni/MinneApple)
Image Source: [https://github.com/nicolaihaeni/MinneApple](https://github.com/nicolaihaeni/MinneApple) | Provide a detailed description of the following dataset: MinneApple |
MitoEM | Contains mitochondria instances. | Provide a detailed description of the following dataset: MitoEM |
MITOS_WSI_CMC | A dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. | Provide a detailed description of the following dataset: MITOS_WSI_CMC |
MIZAN | Persian-English parallel corpus with more than one million sentence pairs collected from masterpieces of literature. | Provide a detailed description of the following dataset: MIZAN |
MJU-Waste | **MJU-Waste** is an RGBD waste object segmentation dataset that is made public to facilitate future research in this area. | Provide a detailed description of the following dataset: MJU-Waste |
MKQA | Multilingual Knowledge Questions and Answers (MKQA) is an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). The goal of this dataset is to provide a challenging benchmark for question answering quality across a wide set of languages. Answers are based on a language-independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. | Provide a detailed description of the following dataset: MKQA |
MK-SQuIT | An example dataset of 110,000 question/query pairs across four WikiData domains. | Provide a detailed description of the following dataset: MK-SQuIT |
MLB Dataset | A new dataset on the baseball domain. | Provide a detailed description of the following dataset: MLB Dataset |
MLB-YouTube Dataset | The MLB-YouTube dataset is a new, large-scale dataset consisting of 20 baseball games from the 2017 MLB post-season available on YouTube with over 42 hours of video footage. The dataset consists of two components: segmented videos for activity recognition and continuous videos for activity classification. It is quite challenging as it is created from TV broadcast baseball games where multiple different activities share the camera angle. Further, the motion/appearance difference between the various activities is quite small.
Source: [https://github.com/piergiaj/mlb-youtube](https://github.com/piergiaj/mlb-youtube)
Image Source: [https://github.com/piergiaj/mlb-youtube](https://github.com/piergiaj/mlb-youtube) | Provide a detailed description of the following dataset: MLB-YouTube Dataset |
MLM | A new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. | Provide a detailed description of the following dataset: MLM |
MLMA Hate Speech | A new multilingual multi-aspect hate speech analysis dataset and use it to test the current state-of-the-art multilingual multitask learning approaches. | Provide a detailed description of the following dataset: MLMA Hate Speech |
MLQE | The **MLQE** dataset is a dataset for sentence-level Machine Translation Quality Estimation. It consists of 6 language pairs representing NMT training in high, medium, and low-resource scenarios. The corpus is extracted from Wikipedia, and 10K segments per language pair are annotated.
Source: [https://github.com/facebookresearch/mlqe](https://github.com/facebookresearch/mlqe) | Provide a detailed description of the following dataset: MLQE |
MLQE-PE | The Multilingual Quality Estimation and Automatic Post-editing (**MLQE-PE**) Dataset is a dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains seven language pairs, with human labels for 9,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level good/bad labels. It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text.
Source: [https://github.com/sheffieldnlp/mlqe-pe](https://github.com/sheffieldnlp/mlqe-pe) | Provide a detailed description of the following dataset: MLQE-PE |
MLRSNet | **MLRSNet** is a a multi-label high spatial resolution remote sensing dataset for semantic scene understanding. It provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation. | Provide a detailed description of the following dataset: MLRSNet |
MLS | The **Multiple Light Source** dataset (**MLS**) is a collection of 24 multiple object scenes each recorded under 18 multiple light source illumination scenarios. The illuminants are varying in dominant spectral colours, intensity and distance from the scene. The dataset can be used for the evaluation of computational colour constancy algorithms. Along with the images of the scenes the spectral characteristics of the camera, light sources and the objects are also provided, and each image includes pixel-by-pixel ground truth annotation of uniformly coloured object surfaces thus making this useful for benchmarking colour-based image segmentation algorithms.
Source: [https://arxiv.org/abs/1908.06126](https://arxiv.org/abs/1908.06126)
Image Source: [https://github.com/Visillect/mls-dataset](https://github.com/Visillect/mls-dataset) | Provide a detailed description of the following dataset: MLS |
MLSUM | A large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. | Provide a detailed description of the following dataset: MLSUM |
MMAct | MMAct is a large-scale dataset for multi/cross modal action understanding. This dataset has been recorded from 20 distinct subjects with seven different types of modalities: RGB videos, keypoints, acceleration, gyroscope, orientation, Wi-Fi and pressure signal. The dataset consists of more than 36k video clips for 37 action classes covering a wide range of daily life activities such as desktop-related and check-in-based ones in four different distinct scenarios. | Provide a detailed description of the following dataset: MMAct |
MMD | The MMD (MultiModal Dialogs) dataset is a dataset for multimodal domain-aware conversations. It consists of over 150K conversation sessions between shoppers and sales agents, annotated by a group of in-house annotators using a semi-automated manually intense iterative process. | Provide a detailed description of the following dataset: MMD |
MMED | Contains 25,165 textual news articles collected from hundreds of news media sites (e.g., Yahoo News, Google News, CNN News.) and 76,516 image posts shared on Flickr social media, which are annotated according to 412 real-world events. The dataset is collected to explore the problem of organizing heterogeneous data contributed by professionals and amateurs in different data domains, and the problem of transferring event knowledge obtained from one data domain to heterogeneous data domain, thus summarizing the data with different contributors. | Provide a detailed description of the following dataset: MMED |
MMID | A large-scale multilingual corpus of images, each labeled with the word it represents. The dataset includes approximately 10,000 words in each of 100 languages. | Provide a detailed description of the following dataset: MMID |
MNIST-1D | A minimalist, low-memory, and low-compute alternative to classic deep learning benchmarks. The training examples are 20 times smaller than MNIST examples yet they differentiate more clearly between linear, nonlinear, and convolutional models which attain 32, 68, and 94% accuracy respectively (these models obtain 94, 99+, and 99+% on MNIST). | Provide a detailed description of the following dataset: MNIST-1D |
MNIST-MIX | **MNIST-MIX** is a multi-language handwritten digit recognition dataset. It contains digits from 10 different languages.
Source: [https://github.com/jwwthu/MNIST-MIX](https://github.com/jwwthu/MNIST-MIX) | Provide a detailed description of the following dataset: MNIST-MIX |
Mo2Cap2 | A large ground truth training corpus of top-down fisheye images. | Provide a detailed description of the following dataset: Mo2Cap2 |
MobiBits | A novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device. | Provide a detailed description of the following dataset: MobiBits |
MOCHA | Contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. | Provide a detailed description of the following dataset: MOCHA |
MOD++ | Includes challenging sequences and extensive data stratification in-terms of camera and object motion, velocity magnitudes, direction, and rotational speeds. | Provide a detailed description of the following dataset: MOD++ |
ModaNet | ModaNet is a street fashion images dataset consisting of annotations related to RGB images. ModaNet provides multiple polygon annotations for each image. Each polygon is associated with a label from 13 meta fashion categories. The annotations are based on images in the PaperDoll image set, which has only a few hundred images annotated by the superpixel-based tool. | Provide a detailed description of the following dataset: ModaNet |
Modern Hebrew Sentiment Dataset | Modern Hebrew Sentiment Dataset is a sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. | Provide a detailed description of the following dataset: Modern Hebrew Sentiment Dataset |
Molweni | A machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. | Provide a detailed description of the following dataset: Molweni |
MonoPerfCap Dataset | MonoPerfCap is a benchmark dataset for human 3D performance capture from monocular video input consisting of around 40k frames, which covers a variety of different scenarios. | Provide a detailed description of the following dataset: MonoPerfCap Dataset |
Moral Stories | Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. | Provide a detailed description of the following dataset: Moral Stories |
MOROCO | The MOldavian and ROmanian Dialectal COrpus (MOROCO) is a corpus that contains 33,564 samples of text (with over 10 million tokens) collected from the news domain. The samples belong to one of the following six topics: culture, finance, politics, science, sports and tech. The data set is divided into 21,719 samples for training, 5,921 samples for validation and another 5,924 samples for testing. | Provide a detailed description of the following dataset: MOROCO |
MOR-UAV | A large-scale video dataset for MOR in aerial videos. | Provide a detailed description of the following dataset: MOR-UAV |
MosMedData | MosMedData contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. | Provide a detailed description of the following dataset: MosMedData |
MOTChallenge | The **MOTChallenge** datasets are designed for the task of multiple object tracking. There are several variants of the dataset released each year, such as MOT15, MOT17, MOT20. | Provide a detailed description of the following dataset: MOTChallenge |
MotionSense | This dataset includes time-series data generated by accelerometer and gyroscope sensors (attitude, gravity, userAcceleration, and rotationRate). It is collected with an iPhone 6s kept in the participant's front pocket using SensingKit which collects information from Core Motion framework on iOS devices. All data is collected in 50Hz sample rate. A total of 24 participants in a range of gender, age, weight, and height performed 6 activities in 15 trials in the same environment and conditions: downstairs, upstairs, walking, jogging, sitting, and standing.
Source: [https://github.com/mmalekzadeh/motion-sense](https://github.com/mmalekzadeh/motion-sense)
Image Source: [https://github.com/mmalekzadeh/motion-sense](https://github.com/mmalekzadeh/motion-sense) | Provide a detailed description of the following dataset: MotionSense |
Mouse Embryo Tracking Database | The **Mouse Embryo Tracking Database** is a dataset for tracking mouse embryos. The dataset contains, for each of the 100 examples: (1) the uncompressed frames, up to the 10th frame after the appearance of the 8th cell; (2) a text file with the trajectories of all the cells, from appearance to division (for cells of generations 1 to 3), where a trajectory is a sequence of pairs (center, radius); (3) a movie file showing the trajectories of the cells. | Provide a detailed description of the following dataset: Mouse Embryo Tracking Database |
Mouse Reach | A large, annotated video dataset of mice performing a sequence of actions. The dataset was collected and labeled by experts for the purpose of neuroscience research. | Provide a detailed description of the following dataset: Mouse Reach |
MoVi | Contains 60 female and 30 male actors performing a collection of 20 predefined everyday actions and sports movements, and one self-chosen movement. | Provide a detailed description of the following dataset: MoVi |
MovieFIB | A quantitative benchmark for developing and understanding video of fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired. | Provide a detailed description of the following dataset: MovieFIB |
MovieGraphs | Provides detailed, graph-based annotations of social situations depicted in movie clips. Each graph consists of several types of nodes, to capture who is present in the clip, their emotional and physical attributes, their relationships (i.e., parent/child), and the interactions between them. Most interactions are associated with topics that provide additional details, and reasons that give motivations for actions. | Provide a detailed description of the following dataset: MovieGraphs |
Moviescope | Moviescope is a large-scale dataset of 5,000 movies with corresponding video trailers, posters, plots and metadata. Moviescope is based on the IMDB 5000 dataset consisting of 5.043 movie records. It is augmented by crawling video trailers associated with each movie from YouTube and text plots from Wikipedia. | Provide a detailed description of the following dataset: Moviescope |
Moving Symbols | A parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks. | Provide a detailed description of the following dataset: Moving Symbols |
MPI3D Disentanglement | A data-set which consists of over one million images of physical 3D objects with seven factors of variation, such as object color, shape, size and position. | Provide a detailed description of the following dataset: MPI3D Disentanglement |
MPI FAUST Dataset | Contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology. | Provide a detailed description of the following dataset: MPI FAUST Dataset |
MPII Cooking 2 Dataset | A dataset which provides detailed annotations for activity recognition. | Provide a detailed description of the following dataset: MPII Cooking 2 Dataset |
MQR | A multi-domain question rewriting dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. | Provide a detailed description of the following dataset: MQR |
MRNet | The MRNet dataset consists of 1,370 knee MRI exams performed at Stanford University Medical Center. The dataset contains 1,104 (80.6%) abnormal exams, with 319 (23.3%) ACL tears and 508 (37.1%) meniscal tears; labels were obtained through manual extraction from clinical reports. | Provide a detailed description of the following dataset: MRNet |
MRQA | The MRQA (Machine Reading for Question Answering) dataset is a dataset for evaluating the generalization capabilities of reading comprehension systems. | Provide a detailed description of the following dataset: MRQA |
MS-ASL | **MS-ASL** is a real-life large-scale sign language data set comprising over 25,000 annotated videos. | Provide a detailed description of the following dataset: MS-ASL |
MSC | **MSC** is a dataset for Macro-Management in StarCraft 2 based on the platfrom SC2LE. It consists of well-designed feature vectors, pre-defined high-level actions and final result of each match. It contains 36,619 high quality replays, which are unbroken and played by relatively professional players.
Source: [https://github.com/wuhuikai/MSC](https://github.com/wuhuikai/MSC) | Provide a detailed description of the following dataset: MSC |
MSeg | A composite dataset that unifies semantic segmentation datasets from different domains. | Provide a detailed description of the following dataset: MSeg |
MSR ActionPairs | This is a 3D action recognition dataset, also known as 3D Action Pairs dataset. The actions in this dataset are selected in pairs such that the two actions of each pair are similar in motion (have similar trajectories) and shape (have similar objects); however, the motion-shape relation is different. | Provide a detailed description of the following dataset: MSR ActionPairs |
MTNT | The Machine Translation of Noisy Text (**MTNT**) dataset is a Machine Translation dataset that consists of noisy comments on Reddit and professionally sourced translation. The translation are between French, Japanese and French, with between 7k and 37k sentence per language pair. | Provide a detailed description of the following dataset: MTNT |
MuCo-3DHP | MuCo-3DHP is a large scale training data set showing real images of sophisticated multi-person interactions and occlusions. | Provide a detailed description of the following dataset: MuCo-3DHP |
MultiBooked | MultiBooked is a dataset for supervised aspect-level sentiment analysis in Basque and Catalan, both of which are under-resourced languages. | Provide a detailed description of the following dataset: MultiBooked |
MultiFC | Publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. | Provide a detailed description of the following dataset: MultiFC |
MultiReQA | **MultiReQA** is a cross-domain evaluation for retrieval question answering models. Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus. MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the MRQA shared task.
MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data.
Source: [https://github.com/google-research-datasets/MultiReQA](https://github.com/google-research-datasets/MultiReQA) | Provide a detailed description of the following dataset: MultiReQA |
MultiSenti | MultiSenti presents a labeled dataset called MultiSenti for sentiment classification of code-switched informal short text, (2) explore the feasibility of adapting resources from a resource-rich language for an informal one, and (3) propose a deep learning-based model for sentiment classification of code-switched informal short text. | Provide a detailed description of the following dataset: MultiSenti |
Multi-species fruit flower detection datasets | This dataset consists of four sets of flower images, from three different species: apple, peach, and pear, and accompanying ground truth images. The images were acquired under a range of imaging conditions. These datasets support work in an accompanying paper that demonstrates a flower identification algorithm that is robust to uncontrolled environments and applicable to different flower species. While this data is primarily provided to support that paper, other researchers interested in flower detection may also use the dataset to develop new algorithms. Flower detection is a problem of interest in orchard crops because it is related to management of fruit load. | Provide a detailed description of the following dataset: Multi-species fruit flower detection datasets |
MultiviewX | **MultiviewX** is a synthetic Multiview pedestrian detection dataset. It is build using pedestrian models from PersonX, in Unity.
The MultiviewX dataset covers a square of 16 meters by 25 meters. The ground plane is quantized into a 640x1000 grid. There are 6 cameras with overlapping field-of-view in the MultiviewX dataset, each of which outputs a 1080x1920 resolution image. On average, 4.41 cameras are covering the same location.
Source: [https://github.com/hou-yz/MVDet](https://github.com/hou-yz/MVDet)
Image Source: [https://github.com/hou-yz/MVDet](https://github.com/hou-yz/MVDet) | Provide a detailed description of the following dataset: MultiviewX |
MultiWOZ-coref | **MultiWOZ-coref**, (or MultiWOZ 2.3) is an extension of the MultiWOZ dataset that adds co-reference annotations in addition to corrections of dialogue acts and dialogue states.
Source: [https://github.com/lexmen318/MultiWOZ_2.3](https://github.com/lexmen318/MultiWOZ_2.3) | Provide a detailed description of the following dataset: MultiWOZ-coref |
Multi-XScience | **Multi-XScience** is a large-scale dataset for multi-document summarization of scientific articles. It has 30,369, 5,066 and 5,093 samples for the train, validation and test split respectively. The average document length is 778.08 words and the average summary length is 116.44 words.
Source: [https://github.com/yaolu/Multi-XScience](https://github.com/yaolu/Multi-XScience) | Provide a detailed description of the following dataset: Multi-XScience |
MURA | A large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either normal or abnormal. | Provide a detailed description of the following dataset: MURA |
MUTLA | This dataset includes time-synchronized multimodal data records of students (learning logs, videos, EEG brainwaves) as they work in various subjects from Squirrel AI Learning System (SAIL) to solve problems of varying difficulty levels. The dataset resources include user records from the learner records store of SAIL, brainwave data collected by EEG headset devices, and video data captured by web cameras while students worked in the SAIL products. | Provide a detailed description of the following dataset: MUTLA |
TAPOS | **TAPOS** is a new dataset developed on sport videos with manual annotations of sub-actions, and conduct a study on temporal action parsing on top. A sport activity usually consists of multiple sub-actions and that the awareness of such temporal structures is beneficial to action recognition.
TAPOS contains 16,294 valid instances in total, across 21 action classes. These instances have a duration of 9.4
seconds on average. The number of instances within each class is different, where the largest class high jump has over
1,600 instances, and the smallest class beam has 200 instances. The average number of sub-actions also varies
from class to class, where parallel bars has 9 sub-actions on average, and long jump has 3 sub-actions on average. All instances are split into train, validation and test sets, of sizes 13094, 1790, and 1763, respectively. | Provide a detailed description of the following dataset: TAPOS |
MutualFriends | In MutualFriends, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend. | Provide a detailed description of the following dataset: MutualFriends |
MVOR | Multi-View Operating Room (MVOR) is a dataset recorded during real clinical interventions. It consists of 732 synchronized multi-view frames recorded by three RGB-D cameras in a hybrid OR. It also includes the visual challenges present in such environments, such as occlusions and clutter. | Provide a detailed description of the following dataset: MVOR |
MVS1K | Contains about 1, 000 videos from 10 queries and their video tags, manual annotations, and associated web images. | Provide a detailed description of the following dataset: MVS1K |
MVSEC | The Multi Vehicle Stereo Event Camera (MVSEC) dataset is a collection of data designed for the development of novel 3D perception algorithms for event based cameras. Stereo event data is collected from car, motorbike, hexacopter and handheld data, and fused with lidar, IMU, motion capture and GPS to provide ground truth pose and depth images. | Provide a detailed description of the following dataset: MVSEC |
MWE-CWI | Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. **MWE-CWI** is a dataset for MWE detection based on the Complex Word Identification Shared Task 2018 dataset.
Source: [https://github.com/ekochmar/MWE-CWI](https://github.com/ekochmar/MWE-CWI) | Provide a detailed description of the following dataset: MWE-CWI |
decaNLP | Natural Language Decathlon Benchmark (decaNLP) is a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. The tasks as cast as question answering over a context. | Provide a detailed description of the following dataset: decaNLP |
Nagoya University Extremely Low-resolution FIR Image Action Dataset | A pedestrian dataset for Person Re-identification. | Provide a detailed description of the following dataset: Nagoya University Extremely Low-resolution FIR Image Action Dataset |
NAIST COVID | NAIST COVID is a multilingual dataset of social media posts related to COVID-19, consisting of microblogs in English and Japanese from Twitter and those in Chinese from Weibo. The data cover microblogs from January 20, 2020, to March 24, 2020. | Provide a detailed description of the following dataset: NAIST COVID |
NAS-Bench-101 | **NAS-Bench-101** is the first public architecture dataset for NAS research. To build NASBench-101, the authors carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional
architectures. The authors trained and evaluated all of these architectures multiple times on CIFAR-10 and compiled the results into a large dataset of over 5 million trained models. This allows researchers to evaluate the quality of a diverse range of models in milliseconds by querying the precomputed dataset. | Provide a detailed description of the following dataset: NAS-Bench-101 |
NAS-Bench-1Shot1 | NAS-Bench-1Shot1 draws on the recent large-scale tabular benchmark NAS-Bench-101 for cheap anytime evaluations of one-shot NAS methods. | Provide a detailed description of the following dataset: NAS-Bench-1Shot1 |
NAS-Bench-201 | **NAS-Bench-201** is a benchmark (and search space) for neural architecture search. Each architecture consists of a predefined skeleton with a stack of the searched cell. In this way, architecture search is transformed into the problem of searching a good cell. | Provide a detailed description of the following dataset: NAS-Bench-201 |
NatCat | A general purpose text categorization dataset (NatCat) from three online resources: Wikipedia, Reddit, and Stack Exchange. These datasets consist of document-category pairs derived from manual curation that occurs naturally by their communities. | Provide a detailed description of the following dataset: NatCat |
NATS-Bench | A unified benchmark on searching for both topology and size, for (almost) any up-to-date NAS algorithm. NATS-Bench includes the search space of 15,625 neural cell candidates for architecture topology and 32,768 for architecture size on three datasets. | Provide a detailed description of the following dataset: NATS-Bench |
Natural Stories | The Natural Stories dataset consists of English texts edited to contain many low-frequency syntactic constructions while still sounding fluent to native speakers. The corpus is annotated with hand-corrected parse trees and includes self-paced reading time data. | Provide a detailed description of the following dataset: Natural Stories |
NavigationNet | NavigationNet is a computer vision dataset and benchmark to allow the utilization of deep reinforcement learning on scene-understanding-based indoor navigation. | Provide a detailed description of the following dataset: NavigationNet |
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