Omkar2118/datasets huggingface dataset:
Polyglot-NER

Polyglot-NER builds massive multilingual annotators with minimal human expertise and intervention.

Source: [POLYGLOT-NER: Massive Multilingual Named Entity Recognition](/paper/polyglot-ner-massive-multilingual-named)

Introducing paper: POLYGLOT-NER: Massive Multilingual Named Entity Recognition
PoMo

PoMo consists of more than 231K sentences with post-modifiers and associated facts extracted from Wikidata for around 57K unique entities.

Source: [PoMo: Generating Entity-Specific Post-Modifiers in Context](/paper/pomo-generating-entity-specific-post)

Introducing paper: PoMo: Generating Entity-Specific Post-Modifiers in Context
Pow-Wow

A dataset for studying situated goal-directed human communication.

Source: [Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman](/paper/pow-wow-a-dataset-and-study-on-collaborative)

Introducing paper: Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman
prachathai-67k

The prachathai-67k dataset was scraped from the news site Prachathai excluding articles with less than 500 characters of body text (mostly images and cartoons). It contains 67,889 articles with 51,797 tags from August 24, 2004 to November 15, 2018.

Source: [prachathai-67k](https://github.com/PyThaiNLP/prachathai-67k/)
PRED18
Full name: PRED18: Predator/Prey DAVIS Dataset

Twenty DAVIS recordings with a total duration of about 1.25 hour were obtained by driving the two robots in the robot arena of the University of Ulster in Londonderry.

Source: [Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network](/paper/steering-a-predator-robot-using-a-mixed)

Introducing paper: Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network
PreSIL
Full name: Precise Synthetic Image and LiDAR

Consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. 

Source: [Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception](/paper/precise-synthetic-image-and-lidar-presil)

Introducing paper: Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
PressurePose

A synthetic dataset with 206K pressure images with 3D human poses and shapes.

Source: [Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data](/paper/bodies-at-rest-3d-human-pose-and-shape)

Introducing paper: Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
Procedural Human Action Videos

Procedural Human Action Videos contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. 

Source: [Procedural Generation of Videos to Train Deep Action Recognition Networks](/paper/procedural-generation-of-videos-to-train-deep)

Introducing paper: Procedural Generation of Videos to Train Deep Action Recognition Networks
Procon20

A novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. 

Source: [Stance Prediction for Contemporary Issues: Data and Experiments](/paper/stance-prediction-for-contemporary-issues)

Introducing paper: Stance Prediction for Contemporary Issues: Data and Experiments
Products-10K

Contains 10,000 fine-grained SKU-level products frequently bought by online customers in JD.com.

Source: [Products-10K: A Large-scale Product Recognition Dataset](/paper/products-10k-a-large-scale-product)

Introducing paper: Products-10K: A Large-scale Product Recognition Dataset
Proposal Flow Datasets

Dataset that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. 

Source: [Proposal Flow](/paper/proposal-flow)

Introducing paper: Proposal Flow
Prostate MRI Segmentation Dataset

This prostate MRI segmentation dataset is collected from six different data sources.

Source: [https://github.com/liuquande/SAML](https://github.com/liuquande/SAML)

Introducing paper: Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
ProtoQA

**ProtoQA** is a question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations. The training set is gathered from an existing set of questions played in a long-running international game show FAMILY- FEUD. The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers.

Source: [https://github.com/iesl/protoqa-data](https://github.com/iesl/protoqa-data)

Introducing paper: ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Proto Summ

This is a large-scale court judgment dataset, where each judgment is a summary of the case description with a patternized style. It contains 2,003,390 court judgment documents. The case description is used as the input, and the court judgment as the summary. The average lengths of the input documents and summaries are 595.15 words and 273.57 words respectively.

Source: [https://arxiv.org/pdf/1909.08837.pdf](https://arxiv.org/pdf/1909.08837.pdf)

Introducing paper: How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing
PROX

A dataset composed of 12 different 3D scenes and RGB sequences of 20 subjects moving in and interacting with the scenes. 

Source: [PROX](https://prox.is.tue.mpg.de)
PS-Battles

The PS-Battles dataset is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives.

Source: [The PS-Battles Dataset - an Image Collection for Image Manipulation Detection](/paper/180404866)
Image Source: [Heller et al](https://arxiv.org/pdf/1804.04866v1.pdf)

Introducing paper: The PS-Battles Dataset - an Image Collection for Image Manipulation Detection
PST900

**PST900** is a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge.

Source: [https://arxiv.org/abs/1909.10980](https://arxiv.org/abs/1909.10980)
Image Source: [https://github.com/ShreyasSkandanS/pst900_thermal_rgb](https://github.com/ShreyasSkandanS/pst900_thermal_rgb)

Introducing paper: PST900: RGB-Thermal Calibration, Dataset and Segmentation Network
PTB-TIR

PTB-TIR is a Thermal InfraRed (TIR) pedestrian tracking benchmark, which provides 60  TIR sequences with mannuly annoations.  The benchmark is used to fair evaluate TIR trackers.

Source: [PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark](/paper/ptb-tir-a-thermal-infrared-pedestrian)

Introducing paper: PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark
PTL
Full name: Pedestrian-Traffic-Lights

A dataset of pedestrian traffic lights containing over 5000 photos taken at hundreds of intersections in Shanghai.

Source: [LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired](/paper/lytnet-a-convolutional-neural-network-for)

Introducing paper: LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired
PubFig
Full name: Public Figures Face Database

The PubFig database is a large, real-world face dataset consisting of 58,797 images of 200 people collected from the internet. Unlike most other existing face datasets, these images are taken in completely uncontrolled situations with non-cooperative subjects. Thus, there is large variation in pose, lighting, expression, scene, camera, imaging conditions and parameters, etc. The PubFig dataset is similar in spirit to the Labeled Faces in the Wild (LFW) dataset.

Source: [PubFig: Public Figures Face Database](https://www1.cs.columbia.edu/CAVE/databases/pubfig/)
PUBHEALTH

**PUBHEALTH** is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label.

Source: [https://github.com/neemakot/Health-Fact-Checking](https://github.com/neemakot/Health-Fact-Checking)

Introducing paper: Explainable Automated Fact-Checking for Public Health Claims
public_meetings

The **public_meetings** corpus contains meetings, made of pairs of automatic transcriptions from audio recordings and meeting reports written by a professional. 22 aligned meetings are provided in total.

Source: [public_meetings](https://github.com/pltrdy/public_meetings)

Introducing paper: Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation
Pump and dump dataset

The **Pump and dump dataset** is an annotated set of messages to detect cryptocurrency market manipulations. It consists of a list of a list of pump and dumps arranged by groups on Telegram. All the pump and dumps in the dataset are on the trading pair SYM/BTC.

Source: [https://github.com/SystemsLab-Sapienza/pump-and-dump-dataset](https://github.com/SystemsLab-Sapienza/pump-and-dump-dataset)

Introducing paper: Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations
QBSUM

A high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. 

Source: [QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications](/paper/qbsum-a-large-scale-query-based-document)

Introducing paper: QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications
QMUL-SurvFace

**QMUL-SurvFace** is a surveillance face recognition benchmark that contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes over wide space and time.

Source: [Surveillance Face Recognition Challenge](/paper/surveillance-face-recognition-challenge)

Introducing paper: Surveillance Face Recognition Challenge
Q-Traffic

**Q-Traffic** is a large-scale traffic prediction dataset, which consists of three sub-datasets: query sub-dataset, traffic speed sub-dataset and road network sub-dataset.

Source: [https://github.com/JingqingZ/BaiduTraffic](https://github.com/JingqingZ/BaiduTraffic)
Image Source: [https://github.com/JingqingZ/BaiduTraffic](https://github.com/JingqingZ/BaiduTraffic)

Introducing paper: Deep Sequence Learning with Auxiliary Information for Traffic Prediction
QuAIL
Full name: Question Answering for Artificial Intelligence

A new kind of question-answering dataset that combines commonsense, text-based, and unanswerable questions, balanced for different genres and reasoning types. Reasoning type annotation for 9 types of reasoning: temporal, causality, factoid, coreference, character properties, their belief states, subsequent entity states, event durations, and unanswerable. Genres: CC license fiction, Voice of America news, blogs, user stories from Quora 800 texts, 18 questions for each (~14K questions).

Source: [QuAIL - Question Answering for Artificial Intelligence](http://text-machine.cs.uml.edu/lab2/projects/quail/)
Quda

Aims to help V-NLIs recognize analytic tasks from free-form natural language by training and evaluating cutting-edge multi-label classification models. The dataset contains  diverse user queries, and each is annotated with one or multiple analytic tasks. 

Source: [Quda: Natural Language Queries for Visual Data Analytics](/paper/quda-natural-language-queries-for-visual-data)

Introducing paper: Quda: Natural Language Queries for Visual Data Analytics
QuerYD

A large-scale dataset for retrieval and event localisation in video. A unique feature of the dataset is the availability of two audio tracks for each video: the original audio, and a high-quality spoken description of the visual content.

Source: [QuerYD: A video dataset with high-quality textual and audio narrations](/paper/queryd-a-video-dataset-with-high-quality)

Introducing paper: QuerYD: A video dataset with high-quality text and audio narrations