Omkar2118/datasets huggingface dataset:
Logic2Text

**Logic2Text** is a large-scale dataset with 10,753 descriptions involving common logic types paired with the underlying logical forms. The logical forms show diversified graph structure of free schema, which poses great challenges on the model's ability to understand the semantics.

Source: [https://github.com/czyssrs/Logic2Text](https://github.com/czyssrs/Logic2Text)

Introducing paper: Logic2Text: High-Fidelity Natural Language Generation from Logical Forms
LogiQA

LogiQA consists of 8,678 QA instances, covering multiple types of deductive reasoning. Results show that state-of-the-art neural models perform by far worse than human ceiling. The dataset can also serve as a benchmark for reinvestigating logical AI under the deep learning NLP setting.

Source: [LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning](/paper/logiqa-a-challenge-dataset-for-machine)
Image Source: [https://arxiv.org/pdf/2007.08124v1.pdf](https://arxiv.org/pdf/2007.08124v1.pdf)

Introducing paper: LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
Logo-2K+

**Logo-2K+**:A Large-Scale Logo Dataset for Scalable Logo Classiﬁcation
The Logo-2K+ dataset contains a diverse range of logo classes from real-world logo images. It contains 167,140 images with 10 root categories and 2,341 leaf categories.
The 10 different root categories are: Food, Clothes, Institution, Accessories, Transportation, Electronic, Necessities, Cosmetic, Leisure and Medical.

Source: [https://github.com/msn199959/Logo-2k-plus-Dataset](https://github.com/msn199959/Logo-2k-plus-Dataset)
Image Source: [https://github.com/msn199959/Logo-2k-plus-Dataset](https://github.com/msn199959/Logo-2k-plus-Dataset)

Introducing paper: Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification
LogoDet-3K

A logo detection dataset with full annotation, which has 3,000 logo categories, about 200,000 manually annotated logo objects and 158,652 images. LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets. 

Source: [LogoDet-3K: A Large-Scale Image Dataset for Logo Detection](/paper/logodet-3k-a-large-scale-image-dataset-for)

Introducing paper: LogoDet-3K: A Large-Scale Image Dataset for Logo Detection
LOGO-Net

A large-scale logo image database for logo detection and brand recognition from real-world product images. 

Source: [LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks](/paper/logo-net-large-scale-deep-logo-detection-and)

Introducing paper: LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
Long-Term Crowd Flow

A synthetic dataset of procedurally generated environments, dynamically simulated crowd flows, and statically derived “proxy” crowd flows (which have more error but are more efficient to compute), for model training and evaluation.

Source: [Laying the Foundations of Deep Long-Term Crowd Flow Prediction](/paper/laying-the-foundations-of-deep-long-term)

Introducing paper: Laying the Foundations of Deep Long-Term Crowd Flow Prediction
Long-term visual localization

Long-term visual localization provides a benchmark datasets aimed at evaluating 6 DoF pose estimation accuracy over large appearance variations caused by changes in seasonal (summer, winter, spring, etc.) and illumination (dawn, day, sunset, night) conditions. Each dataset consists of a set of reference images, together with their corresponding ground truth poses, and a set of query images.

Source: [Long-term visual localization](http://visuallocalization.net/)
Image Source: [https://www.visuallocalization.net/](https://www.visuallocalization.net/)

Introducing paper: Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
LPW
Full name: Labeled Pedestrian in the Wild

**Labeled Pedestrian in the Wild (LPW)** is a pedestrian detection dataset that contains 2,731 pedestrians in three different scenes where each annotated identity is captured by from 2 to 4 cameras. The LPW features a notable scale of 7,694 tracklets with over 590,000 images as well as the cleanliness of its tracklets. It distinguishes from existing datasets in three aspects: large scale with cleanliness, automatically detected bounding boxes and far more crowded scenes with greater age span. This dataset provides a more realistic and challenging benchmark, which facilitates the further exploration of more powerful algorithms.

Introducing paper: Region-based Quality Estimation Network for Large-scale Person Re-identification
LSICC
Full name: Large Scale Informal Chinese Corpus

Large Scale Informal Chinese Corpus (LSICC) is a large-scale corpus of informal Chinese. This corpus contains around 37 million book reviews and 50 thousand netizen's comments to the news.

Source: [LSICC: A Large Scale Informal Chinese Corpus](/paper/lsicc-a-large-scale-informal-chinese-corpus)

Introducing paper: LSICC: A Large Scale Informal Chinese Corpus
LSLF
Full name: Large-scale Labeled Face

Consists of a large number of unconstrained multi-view and partially occluded faces.

Source: [Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild](/paper/large-scale-datasets-faces-with-partial)

Introducing paper: Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild
LSMDC-Context

The Large Scale Movie Description Challenge (LSMDC) - Context is an augmented version of the original LSMDC dataset with movie scripts as contextual text.

Source: [https://github.com/primle/LSMDC-Context](https://github.com/primle/LSMDC-Context)

Introducing paper: Enriching Video Captions With Contextual Text
Lyft Level 5 Prediction

A self-driving dataset for motion prediction, containing over 1,000 hours of data. This was collected by a fleet of 20 autonomous vehicles along a fixed route in Palo Alto, California, over a four-month period. It consists of 170,000 scenes, where each scene is 25 seconds long and captures the perception output of the self-driving system, which encodes the precise positions and motions of nearby vehicles, cyclists, and pedestrians over time. 

Source: [One Thousand and One Hours: Self-driving Motion Prediction Dataset](/paper/one-thousand-and-one-hours-self-driving)

Introducing paper: One Thousand and One Hours: Self-driving Motion Prediction Dataset
m2caiSeg

Created from endoscopic video feeds of real-world surgical procedures. Overall, the data consists of 307 images, each of which is annotated for the organs and different surgical instruments present in the scene.

Source: [m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks](/paper/m2caiseg-semantic-segmentation-of)

Introducing paper: m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks
M2E2

Aims to extract events and their arguments from multimedia documents. Develops the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments.

Source: [Cross-media Structured Common Space for Multimedia Event Extraction](/paper/cross-media-structured-common-space-for)

Introducing paper: Cross-media Structured Common Space for Multimedia Event Extraction
Machine Number Sense

Consists of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG). These visual arithmetic problems are in the form of geometric figures: each problem has a set of geometric shapes as its context and embedded number symbols. 

Source: [Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning](/paper/machine-number-sense-a-dataset-of-visual)

Introducing paper: Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning
Mafiascum

A collection of over 700 games of Mafia, in which players are randomly assigned either deceptive or non-deceptive roles and then interact via forum postings. Over 9000 documents were compiled from the dataset, which each contained all messages written by a single player in a single game. This corpus was used to construct a set of hand-picked linguistic features based on prior deception research, as well as a set of average word vectors enriched with subword information. 

Source: [The Mafiascum Dataset: A Large Text Corpus for Deception Detection](/paper/the-mafiascum-dataset-a-large-text-corpus-for)

Introducing paper: The Mafiascum Dataset: A Large Text Corpus for Deception Detection
Makeup Datasets

A dataset of female face images assembled for studying the impact of makeup on face recognition.

Source: [Makeup Datasets](http://www.antitza.com/makeup-datasets.html)
Malaria Dataset

The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells.

Source: [Malaria Dataset](https://lhncbc.nlm.nih.gov/publication/pub9932)
MalayalamMixSentiment

MalayalamMixSentiment is a Sentiment Analysis Dataset for Code-Mixed Malayalam-English.

Source: [https://github.com/bharathichezhiyan/MalayalamMixSentiment](https://github.com/bharathichezhiyan/MalayalamMixSentiment)

Introducing paper: A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
MANTRA

An annotated dataset of 4869 transient and 71207 non-transient object lightcurves built from the Catalina Real Time Transient Survey. 

Source: [MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition](/paper/mantra-a-machine-learning-reference)

Introducing paper: MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition
ManyModalQA

Collects the data by scraping Wikipedia and then utilize crowdsourcing to collect question-answer pairs. 

Source: [ManyModalQA: Modality Disambiguation and QA over Diverse Inputs](/paper/manymodalqa-modality-disambiguation-and-qa)

Introducing paper: ManyModalQA: Modality Disambiguation and QA over Diverse Inputs
Market1203-Reid-Dataset

This dataset contains 1203 individuals captured from two disjoint camera views. To each person, one to twelve images are captured from one to six different orientations under one camera view and are normalized to 128x64 pixels. This dataset is constructed based on the Market-1501 benchmark data and the orientation label for each image has been manually annotated.

Source: [https://github.com/charliememory/Market1203-Reid-Dataset](https://github.com/charliememory/Market1203-Reid-Dataset)

Introducing paper: Orientation Driven Bag of Appearances for Person Re-identification
Marmara Turkish Coreference Resolution Corpus

Describe the Marmara Turkish Coreference Corpus, which is an annotation of the whole METU-Sabanci Turkish Treebank with mentions and coreference chains.

Source: [Marmara Turkish Coreference Corpus and Coreference Resolution Baseline](/paper/marmara-turkish-coreference-corpus-and)

Introducing paper: Marmara Turkish Coreference Corpus and Coreference Resolution Baseline
MASATI
Full name: MAritime SATellite Imagery dataset

The MASATI dataset contains color images in dynamic marine environments, and it can be used to evaluate ship detection methods. Each image may contain one or multiple targets in different weather and illumination conditions. The datasets is composed of 7,389 satellite images labeled according to the following seven classes: land, coast, sea, ship, multi, coast-ship, and detail. In addition, labeling with the bounding box for the location of the vessels is also included.

Source: [MASATI](http://www.iuii.ua.es/datasets/masati/index.html)
MaskedFace-Net

Proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net).

Source: [MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19](/paper/maskedface-net-a-dataset-of-correctly)

Introducing paper: MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19
MASRI-HEADSET

MASRI-HEADSET is a corpus that was developed by the MASRI project at the University of Malta. It consists of 8 hours of speech paired with text, recorded by using short text snippets in a laboratory environment. The speakers were recruited from different geographical locations all over the Maltese islands, and were roughly evenly distributed by gender. 

Source: [MASRI-HEADSET: A Maltese Corpus for Speech Recognition](/paper/masri-headset-a-maltese-corpus-for-speech-1)

Introducing paper: MASRI-HEADSET: A Maltese Corpus for Speech Recognition
MaSS

MaSS (Multilingual corpus of Sentence-aligned Spoken utterances) is an extension of the CMU Wilderness Multilingual Speech Dataset, a speech dataset based on recorded readings of the New Testament.

MaSS extends it by providing a large and clean dataset of 8,130 parallel spoken utterances across 8 languages (56 language pairs). The covered languages are: Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish.

Source: [MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible](/paper/mass-a-large-and-clean-multilingual-corpus-of)
Image Source: [https://arxiv.org/pdf/1907.12895v3.pdf](https://arxiv.org/pdf/1907.12895v3.pdf)

Introducing paper: MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible
MatterportLayout

MatterportLayout extends the Matterport3D dataset with general Manhattan layout annotations. It has 2,295 RGBD panoramic images from Matterport3D which are extended with ground truth 3D layouts.

Source: [https://github.com/ericsujw/Matterport3DLayoutAnnotation](https://github.com/ericsujw/Matterport3DLayoutAnnotation)

Introducing paper: Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods
MAVEN

Contains 4,480 Wikipedia documents, 118,732 event mention instances, and 168 event types. 

Source: [MAVEN: A Massive General Domain Event Detection Dataset](/paper/maven-a-massive-general-domain-event)

Introducing paper: MAVEN: A Massive General Domain Event Detection Dataset
MC-AFP

A dataset of around 2 million examples for machine reading-comprehension.

Source: [Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors](/paper/building-large-machine-reading-comprehension)

Introducing paper: Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors
MCIC-COCO

A large-scale machine comprehension dataset (based on the COCO images and captions).

Source: [Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task](/paper/understanding-image-and-text-simultaneously-a)

Introducing paper: Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task
MC-TACO

MC-TACO is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. The dataset contains five temporal properties, (1) duration (how long an event takes), (2) temporal ordering (typical order of events), (3) typical time (when an event occurs), (4) frequency (how often an event occurs), and (5) stationarity (whether a state is maintained for a very long time or indefinitely). 

Source: [MC-TACO](https://leaderboard.allenai.org/mctaco/submissions/public)

Introducing paper: "Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
MD4K

A small-scale training set, which only contains 4K images.

Source: [A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset](/paper/a-deeper-look-at-salient-object-detection-bi)

Introducing paper: A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset
MD Gender
Full name: Multi-Dimensional Gender Bias Datasets

Provides eight automatically annotated large scale datasets with gender information. 

Source: [Multi-Dimensional Gender Bias Classification](/paper/multi-dimensional-gender-bias-classification)

Introducing paper: Multi-Dimensional Gender Bias Classification
mEBAL

A multimodal database for eye blink detection and attention level estimation. 

Source: [mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation](/paper/mebal-a-multimodal-database-for-eye-blink)

Introducing paper: mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation
MED
Full name: Monotonicity Entailment Dataset

**MED** is a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications. The dataset was constructed by collecting naturally-occurring examples by crowdsourcing and well-designed ones from linguistics publications.
It consists of 5,382 examples.

Source: [https://github.com/verypluming/MED](https://github.com/verypluming/MED)
Image Source: [https://www.aclweb.org/anthology/W19-4804v2.pdf](https://www.aclweb.org/anthology/W19-4804v2.pdf)

Introducing paper: Can neural networks understand monotonicity reasoning?
MeDAL

The  Medical Dataset for Abbreviation Disambiguation for Natural Language Understanding (**MeDAL**) is a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. It was published at the ClinicalNLP workshop at EMNLP.

Source: [https://github.com/McGill-NLP/medal](https://github.com/McGill-NLP/medal)
Image Source: [https://github.com/McGill-NLP/medal](https://github.com/McGill-NLP/medal)

Introducing paper: MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
MedDG

**MedDG** is a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases. It contains more than 17K conversations collected from the online health consultation community. Five different categories of entities, including diseases, symptoms, attributes, tests, and medicines, are annotated in each conversation of MedDG as additional labels.
Two kinds of medical dialogue tasks are proposed for this dataset:
* Next entity prediction
* Doctor response generation

Source: [https://github.com/lwgkzl/MedDG](https://github.com/lwgkzl/MedDG)

Introducing paper: MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
Medical Case Report Corpus

Medical Case Report Corpus is a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central's open access library. 

Source: [Named Entities in Medical Case Reports: Corpus and Experiments](/paper/named-entities-in-medical-case-reports-corpus)

Introducing paper: Named Entities in Medical Case Reports: Corpus and Experiments
MedICaT

**MedICaT** is a dataset of medical images, captions, subfigure-subcaption annotations, and inline textual references.
Figures and captions are extracted from open access articles in PubMed Central and corresponding reference text is derived from S2ORC.
The dataset consists of:
217,060 figures from 131,410 open access papers
7507 subcaption and subfigure annotations for 2069 compound figures
Inline references for ~25K figures in the ROCO dataset

Source: [https://github.com/allenai/medicat](https://github.com/allenai/medicat)

Introducing paper: MedICaT: A Dataset of Medical Images, Captions, and Textual References
MEDIQA-AnS
Full name: MEDIQA-Answer Summarization

The first summarization collection containing question-driven summaries of answers to consumer health questions. This dataset can be used to evaluate single or multi-document summaries generated by algorithms using extractive or abstractive approaches. 

Source: [Question-Driven Summarization of Answers to Consumer Health Questions](/paper/question-driven-summarization-of-answers-to)

Introducing paper: Question-Driven Summarization of Answers to Consumer Health Questions
medisim

**medisim** is a collection of new large-scale medical term similarity datasets based on SNOMED-CT.

Source: [https://github.com/babylonhealth/medisim](https://github.com/babylonhealth/medisim)

Introducing paper: Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!
Medley2K

A dataset called Medley2K that consists of 2,000 medleys and 7,712 labeled transitions. 

Source: [Medley2K: A Dataset of Medley Transitions](/paper/medley2k-a-dataset-of-medley-transitions)

Introducing paper: Medley2K: A Dataset of Medley Transitions
MedMentions

MedMentions is a new manually annotated resource for the recognition of biomedical concepts. What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000 abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over 3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines. 

Source: [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](/paper/medmentions-a-large-biomedical-corpus)

Introducing paper: MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts
MedMNIST

A collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. 

Source: [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](/paper/medmnist-classification-decathlon-a)

Introducing paper: MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
MedQuAD
Full name: Medical Question Answering Dataset

MedQuAD includes 47,457 medical question-answer pairs created from 12 NIH websites (e.g. cancer.gov, niddk.nih.gov, GARD, MedlinePlus Health Topics). The collection covers 37 question types (e.g. Treatment, Diagnosis, Side Effects) associated with diseases, drugs and other medical entities such as tests.  

Source: [A Question-Entailment Approach to Question Answering](/paper/a-question-entailment-approach-to-question)

Introducing paper: A Question-Entailment Approach to Question Answering
MegaAge

MegaAge is a large dataset that consists of 41,941 faces annotated with age posterior distributions.

Introducing paper: Quantifying Facial Age by Posterior of Age Comparisons
Mega-COV

**Mega-COV** is a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets (~32M tweets).

Source: [https://github.com/UBC-NLP/megacov](https://github.com/UBC-NLP/megacov)
Image Source: [https://github.com/UBC-NLP/megacov](https://github.com/UBC-NLP/megacov)

Introducing paper: Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
MeGlass

**MeGlass** is an eyeglass dataset originally designed for eyeglass face recognition evaluation. All the face images are selected and cleaned from MegaFace. Each identity has at least two face images with eyeglass and two face images without eyeglass. It contains 47,817 images from 1,710 different identities.

Source: [https://github.com/cleardusk/MeGlass](https://github.com/cleardusk/MeGlass)
Image Source: [https://github.com/cleardusk/MeGlass](https://github.com/cleardusk/MeGlass)

Introducing paper: Face Synthesis for Eyeglass-Robust Face Recognition
MEIR
Full name: Multimodal Entity Image Repurposing

MEIR is a substantially challenging dataset over that which has been previously available to support research into image repurposing detection. The new dataset includes location, person, and organization manipulations on real-world data sourced from Flickr.

Source: [Deep Multimodal Image-Repurposing Detection](https://arxiv.org/pdf/1808.06686.pdf)

Introducing paper: Deep Multimodal Image-Repurposing Detection
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. 

Source: [MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification](/paper/melinda-a-multimodal-dataset-for-biomedical)

Introducing paper: MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification
Memeify

A large-scale dataset of memes with captions and class labels. The dataset consists of 1.1 million meme captions from 128 classes.

Source: [Memeify: A Large-Scale Meme Generation System](/paper/memeify-a-large-scale-meme-generation-system)

Introducing paper: Memeify: A Large-Scale Meme Generation System
MetaLWOz
Full name: Meta-Learning Wizard-of-Oz

Collected by leveraging background knowledge from a larger, more highly represented dialogue source.

Source: [Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach](/paper/few-shot-dialogue-generation-without)

Introducing paper: Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach
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.

Source: [Metaphorics](https://github.com/skelemoa/quovadis/tree/master/metaphorics)

Introducing paper: Quo Vadis, Skeleton Action Recognition ?
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)

Introducing paper: Unit Test Case Generation with Transformers and Focal Context
#MeTooMA

The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories. 

Source: [#MeTooMA](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU)
AraMeter

A dataset to identify the meters of Arabic poems.

Source: [https://github.com/zaidalyafeai/MetRec](https://github.com/zaidalyafeai/MetRec)
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)

Introducing paper: ALET (Automated Labeling of Equipment and Tools): A Dataset, a Baseline and a Usecase for Tool Detection in the Wild
MEVA
Full name: Multiview Extended Video with Activities

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.

Source: [MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection](/paper/meva-a-large-scale-multiview-multimodal-video)

Introducing paper: MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection
Mewsli-9

A large new multilingual dataset for multilingual entity linking.

Source: [Entity Linking in 100 Languages](/paper/entity-linking-in-100-languages)

Introducing paper: Entity Linking in 100 Languages
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).

Source: [MEx: Multi-modal Exercises Dataset for Human Activity Recognition](/paper/mex-multi-modal-exercises-dataset-for-human)

Introducing paper: MEx: Multi-modal Exercises Dataset for Human Activity Recognition
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.

Source: [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](/paper/a-neural-network-approach-to-context-1)

Introducing paper: A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
MIDAS-KIKI

Consists of manually annotated dangerous and non-dangerous Kiki challenge videos. 

Source: [Kiki Kills: Identifying Dangerous Challenge Videos from Social Media](/paper/kiki-kills-identifying-dangerous-challenge)

Introducing paper: Kiki Kills: Identifying Dangerous Challenge Videos from Social Media
MIDV-2019

Contains video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions.

Source: [MIDV-2019: Challenges of the modern mobile-based document OCR](/paper/midv-2019-challenges-of-the-modern-mobile)

Introducing paper: MIDV-2019: Challenges of the modern mobile-based document OCR
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. 

Source: [MilkQA: a Dataset of Consumer Questions for the Task of Answer Selection](/paper/milkqa-a-dataset-of-consumer-questions-for)

Introducing paper: MilkQA: a Dataset of Consumer Questions for the Task of Answer Selection