Omkar2118/datasets huggingface dataset:
Open MIC
Full name: Open Museum Identification Challenge

Open MIC (Open Museum Identification Challenge) contains photos of exhibits captured in 10 distinct exhibition spaces of several museums which showcase paintings, timepieces, sculptures, glassware, relics, science exhibits, natural history pieces, ceramics, pottery, tools and indigenous crafts. The goal of Open MIC is to stimulate research in domain adaptation, egocentric recognition and few-shot learning by providing a testbed complementary to the famous Office 31.

Source: [Museum Exhibit Identification Challenge for Domain Adaptation and Beyond](/paper/museum-exhibit-identification-challenge-for)

Introducing paper: Museum Exhibit Identification Challenge for Domain Adaptation and Beyond
OpenSurfaces

**OpenSurfaces** is a large database of annotated surfaces created from real-world consumer photographs. The  framework used for the annotation process draws on crowdsourcing to segment surfaces from photos, and then annotate them with rich surface properties, including material, texture and contextual information.
OpenViDial

**OpenViDial** is a large-scale open-domain dialogue dataset with visual contexts. The dialogue turns and visual contexts are extracted from movies and TV series, where each dialogue turn is paired with the corresponding visual context in which it takes place. OpenViDial contains a total number of 1.1 million dialogue turns, and thus 1.1 million visual contexts stored in images.

Source: [https://github.com/ShannonAI/OpenViDial](https://github.com/ShannonAI/OpenViDial)
Image Source: [https://github.com/ShannonAI/OpenViDial](https://github.com/ShannonAI/OpenViDial)

Introducing paper: OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts
OPIEC
Full name: Open Information Extraction Corpus

OPIEC is an Open Information Extraction (OIE) corpus, constructed from the entire English Wikipedia. It containing more than 341M triples. Each triple from the corpus is composed of rich meta-data: each token from the subj / obj / rel along with NLP annotations (POS tag, NER tag, ...), provenance sentence (along with its dependency parse, sentence order relative to the article), original (golden) links contained in the Wikipedia articles, space / time.

Source: [OPIEC](https://www.uni-mannheim.de/dws/research/resources/opiec/)

Introducing paper: OPIEC: An Open Information Extraction Corpus
Opinosis

This dataset contains sentences extracted from user reviews on a given topic. Example topics are “performance of Toyota Camry” and “sound quality of ipod nano”, etc. In total there are 51 such topics  with each topic having approximately 100 sentences (on average). The reviews were obtained from various sources – Tripadvisor (hotels), Edmunds.com (cars) and Amazon.com (various electronics).  This dataset was used for the following automatic text summarization project .

Source: [Opinosis](http://kavita-ganesan.com/opinosis-opinion-dataset/)
OPUS-100

OPUS-100 is an English-centric multilingual corpus covering 100 languages. It was randomly sampled from the OPUS collection.

Introducing paper: Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
OrangeSum

Source: [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](/paper/barthez-a-skilled-pretrained-french-sequence)

**OrangeSum** is a single-document extreme summarization dataset with two tasks: title and abstract. Ground truth summaries are respectively 11.42 and 32.12 words in length on average, for the title and abstract tasks respectively, while document sizes are 315 and 350 words.

The motivation for OrangeSum was to put together a French equivalent of the XSum dataset.

Unlike the historical CNN, DailyMail, and NY Times datasets, OrangeSum requires the models to display a high degree of abstractivity to perform well.
OrangeSum was created by scraping articles and their titles and abstracts from the Orange Actu website.

Scraped pages cover almost a decade from Feb 2011 to Sep 2020, and belong to five main categories: France, world, politics, automotive, and society.
The society category is itself divided into 8 subcategories: health, environment, people, culture, media, high-tech, unsual ("insolite" in French), and miscellaneous.

The dataset is publicly available at: https://github.com/Tixierae/OrangeSum.

Introducing paper: BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
ORCAS

ORCAS is a click-based dataset. It covers 1.4 million of the TREC DL documents, providing 18 million connections to 10 million distinct queries.

Introducing paper: ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search
ORConvQA
Full name: Open-Retrieval Conversational Question Answering

Enhances QuAC by adapting it to an open-retrieval setting. It is an aggregation of three existing datasets: (1) the QuAC dataset that offers information-seeking conversations, (2) the CANARD dataset that consists of context-independent rewrites of QuAC questions, and (3) the Wikipedia corpus that serves as the knowledge source of answering questions.

Source: [ORConvQA](https://github.com/prdwb/orconvqa-release)

Introducing paper: Open-Retrieval Conversational Question Answering
ORGaze

A new video dataset for OR, with 30, 000 objects over 5, 000 stereo video sequences annotated for their descriptions and gaze.

Source: [Object Referring in Videos with Language and Human Gaze](/paper/object-referring-in-videos-with-language-and)

Introducing paper: Object Referring in Videos with Language and Human Gaze
ORKG-QA

A preliminary dataset of related tables and a corresponding set of natural language questions.

Source: [Question Answering on Scholarly Knowledge Graphs](/paper/question-answering-on-scholarly-knowledge)

Introducing paper: Question Answering on Scholarly Knowledge Graphs
OTT-QA

The Open Table-and-Text Question Answering (**OTT-QA**) dataset contains open questions which require retrieving tables and text from the web to answer. This dataset is re-annotated from the previous HybridQA dataset. The dataset is collected by UCSB NLP group and issued under MIT license.

Source: [https://github.com/wenhuchen/OTT-QA](https://github.com/wenhuchen/OTT-QA)
Image Source: [https://github.com/wenhuchen/OTT-QA](https://github.com/wenhuchen/OTT-QA)

Introducing paper: Open Question Answering over Tables and Text
Out the Window

The Out the Window (OTW) dataset is a crowdsourced activity dataset containing 5,668 instances of 17 activities from the NIST Activities in Extended Video (ActEV) challenge. These videos are crowdsourced from workers on the Amazon Mechanical Turk using a novel scenario acting strategy, which collects multiple instances of natural activities per scenario. 

Source: [Out the Window: A Crowd-Sourced Dataset for Activity Classification in Security Video](/paper/out-the-window-a-crowd-sourced-dataset-for)

Introducing paper: Out the Window: A Crowd-Sourced Dataset for Activity Classification in Security Video
Oxford Radar RobotCar Dataset

The Oxford Radar RobotCar Dataset is a radar extension to The Oxford RobotCar Dataset. It has been extended with data from a Navtech CTS350-X Millimetre-Wave FMCW radar and Dual Velodyne HDL-32E LIDARs with optimised ground truth radar odometry for 280 km of driving around Oxford, UK (in addition to all sensors in the original Oxford RobotCar Dataset).

Source: [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](/paper/the-oxford-radar-robotcar-dataset-a-radar)

Introducing paper: The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
Oxford RobotCar Dataset

The Oxford RobotCar Dataset contains over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year. The dataset captures many different combinations of weather, traffic and pedestrians, along with longer term changes such as construction and roadworks.

Source: [Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset](/paper/real-time-kinematic-ground-truth-for-the)

Introducing paper: Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
PA-HMDB51
Full name: Privacy Annotated HMDB51

The **Privacy Annotated HMDB51** (**PA-HMDB51**) dataset is a video-based dataset for evaluating pirvacy protection in visual action recognition algorithms. The dataset contains both target task labels (action) and selected privacy attributes (skin color, face, gender, nudity, and relationship) annotated on a per-frame basis.

Source: [https://github.com/VITA-Group/PA-HMDB51](https://github.com/VITA-Group/PA-HMDB51)

Introducing paper: Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
PANDA

PANDA is the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapixel camera and cover real-world scenes with both wide field-of-view (~1 square kilometer area) and high-resolution details (~gigapixel-level/frame). The scenes may contain 4k head counts with over 100x scale variation. PANDA provides enriched and hierarchical ground-truth annotations, including 15,974.6k bounding boxes, 111.8k fine-grained attribute labels, 12.7k trajectories, 2.2k groups and 2.9k interactions.

Source: [PANDA: A Gigapixel-level Human-centric Video Dataset](/paper/panda-a-gigapixel-level-human-centric-video)

Introducing paper: PANDA: A Gigapixel-level Human-centric Video Dataset
Spherical-Navi

A novel 360◦ fisheye panoramas dataset, i.e., the Spherical-Navi image dataset is collected, with a unique labeling strategy enabling automatic generation of an arbitrary number of negative samples (wrong heading direction).

Source: [Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492478/pdf/sensors-17-01341.pdf)
ParaBank

A large-scale English paraphrase dataset that surpasses prior work in both quantity and quality.

Source: [ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation](/paper/parabank-monolingual-bitext-generation-and)

Introducing paper: ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation
PARADE

PARADE contains paraphrases that overlap very little at the lexical and syntactic level but are semantically equivalent based on computer science domain knowledge, as well as non-paraphrases that overlap greatly at the lexical and syntactic level but are not semantically equivalent based on this domain knowledge.

Source: [PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge](/paper/parade-a-new-dataset-for-paraphrase)

Introducing paper: PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge
Parallel Meaning Bank

The **Parallel Meaning Bank** (PMB), developed at the University of Groningen and building upon the Groningen Meaning Bank, comprises sentences and texts in raw and tokenised format, syntactic analysis, word senses, thematic roles, reference resolution, and formal meaning representations. The main objective of the PMB is to provide fine-grained meaning representations for words, sentences and texts. Sentences are, in isolation, often ambiguous. The aim is to provide the most likely interpretation for a sentence, with a minimal use of underspecification.

The PMB annotations include gold standard data, which is fully manually corrected, as well as silver (partially manually corrected) and bronze (with no manual corrections) data. The releases so far contain documents for English, German, Italian and Dutch, but for future releases it is planned to include Chinese and Japanese.

Source: [The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages](/paper/the-parallel-meaning-bank-a-framework-for)

Introducing paper: The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages
Bilingual Corpus of Arabic-English Parallel Tweets

A bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabic tweets regularly.

Source: [Constructing a Bilingual Corpus of Parallel Tweets](/paper/constructing-a-bilingual-corpus-of-parallel)

Introducing paper: Constructing a Bilingual Corpus of Parallel Tweets
ParaPat
Full name: Parallel Corpus of Patents Abstracts

A parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.

Source: [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](/paper/parapat-the-multi-million-sentences-parallel)

Introducing paper: ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
ParCorFull
Full name: Parallel Corpus Annotated with Full Coreference

ParCorFull is a parallel corpus annotated with full coreference chains that has been created to address an important problem that machine translation and other multilingual natural language processing (NLP) technologies face -- translation of coreference across languages. This corpus contains parallel texts for the language pair English-German, two major European languages. Despite being typologically very close, these languages still have systemic differences in the realisation of coreference, and thus pose problems for multilingual coreference resolution and machine translation. This parallel corpus covers the genres of planned speech (public lectures) and newswire. It is richly annotated for coreference in both languages, including annotation of both nominal coreference and reference to antecedents expressed as clauses, sentences and verb phrases.

Source: [ParCorFull: a Parallel Corpus Annotated with Full Coreference](/paper/parcorfull-a-parallel-corpus-annotated-with)

Introducing paper: ParCorFull: a Parallel Corpus Annotated with Full Coreference
Paris Art Deco Facades

Anew dataset of facade images from Paris following the Art-deco style.

Source: [Learning grammars for architecture-specific facade parsing](https://hal.inria.fr/hal-01069379/file/paper2.pdf)
Parkinson's Pose Estimation Dataset

The data includes all movement trajectories extracted from the videos of Parkinson's assessments using Convolutional Pose Machines (CPM) as well as the confidence values from CPM. The dataset also includes ground truth ratings of parkinsonism and dyskinesia severity using the UDysRS, UPDRS, and CAPSIT.

Source: [https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset](https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset)

Introducing paper: Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation
Pars-ABSA

Pars-ABSA is a manually annotated Persian dataset, Pars-ABSA, which is verified by 3 native Persian speakers. The dataset consists of 5,114 positive, 3,061 negative and 1,827 neutral data samples from 5,602 unique reviews.

Source: [Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian](/paper/pars-absa-an-aspect-based-sentiment-analysis)

Introducing paper: Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian
PathVQA

PathVQA consists of 32,799 open-ended questions from 4,998 pathology images where each question is manually checked to ensure correctness.

Source: [PathVQA: 30000+ Questions for Medical Visual Question Answering](/paper/pathvqa-30000-questions-for-medical-visual)

Introducing paper: PathVQA: 30000+ Questions for Medical Visual Question Answering
PCDS
Full name: People Counting Dataset

Contains over 4,500 videos recorded at the entrance doors of buses in normal and cluttered conditions. It also proposes an efficient method for counting people in real-world cluttered scenes related to public transportations using depth videos. 

Source: [Benchmark data and method for real-time people counting in cluttered scenes using depth sensors](/paper/benchmark-data-and-method-for-real-time)

Introducing paper: Benchmark data and method for real-time people counting in cluttered scenes using depth sensors
P-DESTRE

Provides consistent ID annotations across multiple days, making it suitable for the extremely challenging problem of person search, i.e., where no clothing information can be reliably used. Apart this feature, the P-DESTRE annotations enable the research on UAV-based pedestrian detection, tracking, re-identification and soft biometric solutions.

Source: [The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices](/paper/the-p-destre-a-fully-annotated-dataset-for)

Introducing paper: The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices
PEC
Full name: Persona-Based Empathetic Conversational

A novel large-scale multi-domain dataset for persona-based empathetic conversations. 

Source: [Towards Persona-Based Empathetic Conversational Models](/paper/endowing-empathetic-conversational-models)

Introducing paper: Towards Persona-Based Empathetic Conversational Models
PedX

PedX is a large-scale multi-modal collection of pedestrians at complex urban intersections. The dataset provides high-resolution stereo images and LiDAR data with manual 2D and automatic 3D annotations. The data was captured using two pairs of stereo cameras and four Velodyne LiDAR sensors.

Source: [pedx.io](http://pedx.io/)

Introducing paper: PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections
PerKey

A corpus of 553k news articles from six Persian news websites and agencies with relatively high quality author extracted keyphrases, which is then filtered and cleaned to achieve higher quality keyphrases. 

Source: [PerKey: A Persian News Corpus for Keyphrase Extraction and Generation](/paper/perkey-a-persian-news-corpus-for-keyphrase)

Introducing paper: PerKey: A Persian News Corpus for Keyphrase Extraction and Generation
Perlex

Persian dataset for relation extraction, which is an expert-translated version of the "Semeval-2010-Task-8" dataset. 

Source: [PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction](/paper/perlex-a-bilingual-persian-english-gold)

Introducing paper: PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction
Permuted bAbI dialog task

The Permuted bAbi dialog task is an adaptation of the "Dialog bAbI tasks data" dataset released by Facebook. It is used for evaluating end-to-end dialog systems in the restaurant domain. This dataset introduces multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of end-to-end goal-oriented dialog systems in a more realistic setting.

Source: [https://github.com/IBM/permuted-bAbI-dialog-tasks](https://github.com/IBM/permuted-bAbI-dialog-tasks)

Introducing paper: Learning End-to-End Goal-Oriented Dialog with Multiple Answers
PerSenT

PerSenT is a dataset of crowd-sourced annotations of the sentiment expressed by the authors towards the main entities in news articles. The dataset also includes paragraph-level sentiment annotations to provide more fine-grained supervision for the task. 

Source: [Author's Sentiment Prediction](/paper/author-s-sentiment-prediction)

Introducing paper: Author's Sentiment Prediction
Perspectrum

Perspectrum is a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify the dataset. Crowd-sourcing was used to filter out noise and ensure high-quality data. The dataset contains 1k claims, accompanied with pools of 10k and 8k perspective sentences and evidence paragraphs, respectively.

Source: [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](/paper/seeing-things-from-a-different-angle)

Introducing paper: Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims
PEYMA

Peyma is a Persian NER dataset to train and test NER systems. It is constructed by collecting documents from ten news websites.

Introducing paper: PEYMA: A Tagged Corpus for Persian Named Entities
PFN-PIC
Full name: PFN Picking Instructions for Commodities Dataset

This dataset is a collection of spoken language instructions for a robotic system to pick and place common objects. Text instructions and corresponding object images are provided.
The dataset consists of situations where the robot is instructed by the operator to pick up a specific object and move it to another location: for example, Move the blue and white tissue box to the top right bin.
This dataset consists of RGBD images, bounding box annotations, destination box annotations, and text instructions.

Source: [https://github.com/pfnet-research/picking-instruction](https://github.com/pfnet-research/picking-instruction)
Image Source: [https://github.com/pfnet-research/picking-instruction](https://github.com/pfnet-research/picking-instruction)

Introducing paper: Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
PG-19

A new open-vocabulary language modelling benchmark derived from books.

Source: [Compressive Transformers for Long-Range Sequence Modelling](/paper/compressive-transformers-for-long-range-1)

Introducing paper: Compressive Transformers for Long-Range Sequence Modelling
PGR
Full name: Phenotype-Gene Relations

Phenotype-Gene Relations (PGR) is a corpus that consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations. 

Source: [A Silver Standard Corpus of Human Phenotype-Gene Relations](/paper/a-silver-standard-corpus-of-human-phenotype)

Introducing paper: A Silver Standard Corpus of Human Phenotype-Gene Relations
PheMT

**PheMT** is a phenomenon-wise dataset designed for evaluating the robustness of Japanese-English machine translation systems. The dataset is based on the MTNT dataset, with additional annotations of four linguistic phenomena common in UGC; Proper Noun, Abbreviated Noun, Colloquial Expression, and Variant

Source: [https://github.com/cl-tohoku/PheMT](https://github.com/cl-tohoku/PheMT)
Image Source: [Fujii et al](https://arxiv.org/pdf/2011.02121v1.pdf)

Introducing paper: PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents