Omkar2118/datasets huggingface dataset:
MOROCO
Full name: MOldavian and ROmanian Dialectal COrpus

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. 

Source: [MOROCO: The Moldavian and Romanian Dialectal Corpus](/paper/moroco-the-moldavian-and-romanian-dialectal)

Introducing paper: MOROCO: The Moldavian and Romanian Dialectal Corpus
MOR-UAV

A large-scale video dataset for MOR in aerial videos.

Source: [MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos](/paper/mor-uav-a-benchmark-dataset-and-baselines-for)

Introducing paper: MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos
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.

Source: [MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset](/paper/mosmeddata-chest-ct-scans-with-covid-19)

Introducing paper: MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset
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)

Introducing paper: Protecting Sensory Data against Sensitive Inferences
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.
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. 

Source: [Detecting the Starting Frame of Actions in Video](/paper/detecting-the-starting-frame-of-actions-in)

Introducing paper: Detecting the Starting Frame of Actions in Video
MovieFIB
Full name: Movie Fill-in-the-Blank

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. 

Source: [A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering](/paper/a-dataset-and-exploration-of-models-for)

Introducing paper: A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering
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.

Source: [Moviescope: Large-scale Analysis of Movies using Multiple Modalities](/paper/moviescope-large-scale-analysis-of-movies)

Introducing paper: Moviescope: Large-scale Analysis of Movies using Multiple Modalities
Moving Symbols

A parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks. 

Source: [A Dataset To Evaluate The Representations Learned By Video Prediction Models](/paper/a-dataset-to-evaluate-the-representations)

Introducing paper: A Dataset To Evaluate The Representations Learned By Video Prediction Models
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.

Source: [On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset](/paper/on-the-transfer-of-inductive-bias-from)

Introducing paper: On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
MPI FAUST Dataset

Contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology.

Source: [FAUST: Dataset and Evaluation for 3D Mesh Registration](/paper/faust-dataset-and-evaluation-for-3d-mesh)

Introducing paper: FAUST: Dataset and Evaluation for 3D Mesh Registration
MPII Cooking 2 Dataset

A dataset which provides detailed annotations for activity recognition.

Source: [Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data](/paper/recognizing-fine-grained-and-composite)

Introducing paper: Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data
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.

Source: [How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions](/paper/how-to-ask-better-questions-a-large-scale)

Introducing paper: How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
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. 

Source: [MRNet](https://stanfordmlgroup.github.io/competitions/mrnet/)

Introducing paper: Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
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)

Introducing paper: MSC: A Dataset for Macro-Management in StarCraft II
MSeg

A composite dataset that unifies semantic segmentation datasets from different domains. 

Source: [MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation](/paper/mseg-a-composite-dataset-for-multi-domain)

Introducing paper: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
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. 

Source: [HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences](/paper/hon4d-histogram-of-oriented-4d-normals-for)

Introducing paper: HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences
MultiBooked

MultiBooked is a dataset for supervised aspect-level sentiment analysis in Basque and Catalan, both of which are under-resourced languages. 

Source: [MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification](/paper/multibooked-a-corpus-of-basque-and-catalan)

Introducing paper: MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification
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. 

Source: [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](/paper/multifc-a-real-world-multi-domain-dataset-for)

Introducing paper: MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
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)

Introducing paper: MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
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.

Source: [Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text](/paper/adapting-deep-learning-for-sentiment)

Introducing paper: Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text
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.

Source: [Multi-species fruit flower detection datasets](https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network)

Introducing paper: Multispecies fruit flower detection using a refined semantic segmentation network
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)

Introducing paper: Multiview Detection with Feature Perspective Transformation
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)

Introducing paper: MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation
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)

Introducing paper: Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
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. 

Source: [MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics](/paper/mutla-a-large-scale-dataset-for-multimodal)

Introducing paper: MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics
TAPOS
Full name: 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.

Introducing paper: Intra- and Inter-Action Understanding via Temporal Action Parsing
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.

Source: [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings](/paper/learning-symmetric-collaborative-dialogue)

Introducing paper: Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
MVS1K

Contains about 1, 000 videos from 10 queries and their video tags, manual annotations, and associated web images.

Source: [Query-Aware Sparse Coding for Multi-Video Summarization](/paper/query-aware-sparse-coding-for-multi-video)

Introducing paper: Query-Aware Sparse Coding for Multi-Video Summarization
MVSEC
Full name: Multi Vehicle Stereo Event Camera

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. 

Source: [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](/paper/ev-flownet-self-supervised-optical-flow)

Introducing paper: The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
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)

Introducing paper: Detecting Multiword Expression Type Helps Lexical Complexity Assessment
Nagoya University Extremely Low-resolution FIR Image Action Dataset

A pedestrian dataset for Person Re-identification.

Source: [Nagoya University Extremely Low-resolution FIR Image Action Dataset](https://www.murase.m.is.nagoya-u.ac.jp/~kawanishiy/en/datasets.html)
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.

Source: [NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset](/paper/naist-covid-multilingual-covid-19-twitter-and)

Introducing paper: NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset