Omkar2118/datasets huggingface dataset:
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.

Source: [Natcat: Weakly Supervised Text Classification with Naturally Annotated Datasets](/paper/natcat-weakly-supervised-text-classification)

Introducing paper: NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources
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. 

Source: [NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size](/paper/nats-bench-benchmarking-nas-algorithms-for)

Introducing paper: NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size
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.

Source: [The Natural Stories Corpus](/paper/the-natural-stories-corpus)

Introducing paper: The Natural Stories Corpus
NavigationNet

NavigationNet is a computer vision dataset and benchmark to allow the utilization of deep reinforcement learning on scene-understanding-based indoor navigation.

Source: [NavigationNet: A Large-scale Interactive Indoor Navigation Dataset](/paper/navigationnet-a-large-scale-interactive)
Image Source: [https://arxiv.org/pdf/1808.08374v1.pdf](https://arxiv.org/pdf/1808.08374v1.pdf)

Introducing paper: NavigationNet: A Large-scale Interactive Indoor Navigation Dataset
NCBI Datasets

NCBI Datasets is a valuable resource that simplifies the process of gathering data from various NCBI databases. Whether you’re a researcher, scientist, or bioinformatician, NCBI Datasets provides an efficient way to access sequence information, annotations, and metadata for genes and genomes.
NCD
Full name: Natural-Color Dataset

The **Natural-Color Dataset** (**NCD**) is an image colorization dataset where images are true to their colors. For example, a carrot will have an orange color in most images. Bananas will be either greenish or yellowish. It contains 723 images from the internet distributed in 20 categories. Each image has an object and a white background.

Source: [https://github.com/saeed-anwar/ColorSurvey#dataset](https://github.com/saeed-anwar/ColorSurvey#dataset)
Image Source: [https://github.com/saeed-anwar/ColorSurvey](https://github.com/saeed-anwar/ColorSurvey)

Introducing paper: Image Colorization: A Survey and Dataset
NCLS
Full name: Neural Cross-Lingual Summarization Corpora

Presents two high-quality large-scale CLS datasets based on existing monolingual summarization datasets.

Source: [NCLS: Neural Cross-Lingual Summarization](/paper/ncls-neural-cross-lingual-summarization)

Introducing paper: NCLS: Neural Cross-Lingual Summarization
NDD20
Full name: Northumberland Dolphin Dataset 2020

Northumberland Dolphin Dataset 2020 (NDD20) is a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation. This dataset, the first release of the NDD, was created in response to the rapid expansion of computer vision into conservation research and the production of field-deployable systems suited to extreme environmental conditions -- an area with few open source datasets. NDD20 contains a large collection of above and below water images of two different dolphin species for traditional coarse and fine-grained segmentation.

Source: [NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation](/paper/ndd20-a-large-scale-few-shot-dolphin-dataset)
Image Source: [Trotter et al](https://arxiv.org/pdf/2005.13359v1.pdf)

Introducing paper: NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation
N-Digit MNIST

N-Digit MNIST is a multi-digit MNIST-like dataset.

Introducing paper: Modeling Uncertainty with Hedged Instance Embedding
PSU NRTDB
Full name: PSU Near-Regular Texture Database

The **PSU Near-Regular Texture Database** is a texture dataset. It covers the spectrum of textures from completely regular to near-regular to irregular. It also includes video of near-regular textures in motion. The database also contains, or will include, test image sets with ground-truth for translation, rotation, reflection/glide-reflection symmetry detection algorithms.

Source: [Computer Vision Online](https://computervisiononline.com/dataset/1105138706)

Introducing paper: PatchMatch-Based Automatic Lattice Detection for Near-Regular Textures
NEEQ Annual Reports

Business taxonomies automatically constructed from the content of corporate annual reports.

Source: [Business Taxonomy Construction Using Concept-Level Hierarchical Clustering](/paper/business-taxonomy-construction-using-concept)

Introducing paper: Business Taxonomy Construction Using Concept-Level Hierarchical Clustering
Negotiation Dialogues Dataset

This dataset consists of 5808 dialogues, based on 2236 unique scenarios. Each dialogue is converted into two training examples in the dataset, showing the complete conversation from the perspective of each agent. The perspectives differ on their input goals, output choice, and in special tokens marking whether a statement was read or written.

Source: [Negotiation Dialogues Dataset](https://github.com/facebookresearch/end-to-end-negotiator)

Introducing paper: Hierarchical Text Generation and Planning for Strategic Dialogue
NERGRIT Corpus

NERGRIT involves machine learning based NLP Tools and a corpus used for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis. 

Source: [NERGRIT Corpus](https://github.com/grit-id/nergrit-corpus)
NetiLook

A large-scale clothing dataset named NetiLook to discover netizen-style comments.

Source: [NetiLook](https://mashyu.github.io/NSC/)
Neural Code Search Evaluation Dataset

Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. 

Source: [Neural Code Search Evaluation Dataset](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/)
Neural Conversational QA

A modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.

Source: [Neural Conversational QA: Learning to Reason vs Exploiting Patterns](/paper/neural-conversational-qa-learning-to-reason-1)

Introducing paper: Neural Conversational QA: Learning to Reason vs Exploiting Patterns
PAF Benchmark

Introduce three new neuromorphic vision datasets recorded by a novel neuromorphic vision sensor named Dynamic Vision Sensors (DVS).

Source: [Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection](https://www.frontiersin.org/articles/10.3389/fnbot.2019.00038/full)
NewB

A text corpus of more than 200,000 sentences from eleven news sources regarding Donald Trump.

Source: [NewB: 200,000+ Sentences for Political Bias Detection](/paper/newb-200000-sentences-for-political-bias)

Introducing paper: NewB: 200,000+ Sentences for Political Bias Detection
New Brown Corpus

A new dataset for training and evaluating grounded language models. 

Source: [A Visuospatial Dataset for Naturalistic Verb Learning](/paper/a-visuospatial-dataset-for-naturalistic-verb)

Introducing paper: A Visuospatial Dataset for Naturalistic Verb Learning
NewSHead

The **NewSHead** dataset contains 369,940 English stories with 932,571 unique URLs, among which there are 359,940 stories for training, 5,000 for validation, and 5,000 for testing, respectively. Each news story contains at least three (and up to five) articles.

The dataset is collected from news stories published between May 2018 and May 2019, where a proprietary clustering algorithm iteratively loads articles published in a time window and groups them based on content similarity. Up to five representative articles are picked from the cluster for generating the story headline. Curators from a crowd-sourcing platform are requested to provide a headline of up to 35 characters to describe the major information covered by the story.

Source: [NewSHead](https://github.com/google-research-datasets/NewSHead)

Introducing paper: Generating Representative Headlines for News Stories
Newspaper Navigator

The largest dataset of extracted visual content from historic newspapers ever produced. The Newspaper Navigator dataset, finetuned visual content recognition model.

Source: [The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America](/paper/the-newspaper-navigator-dataset-extracting)

Introducing paper: The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America
NewsPH-NLI

NewsPH-NLI is a sentence entailment benchmark dataset in the low-resource Filipino language. 

Source: [Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation](/paper/investigating-the-true-performance-of)

Introducing paper: Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets
NIND
Full name: Natural Image Noise Dataset

An open dataset of real photographs with real noise, from identical scenes captured with varying ISO values.
Most images are taken with a Fujifilm X-T1 and XF18-55mm, other photographers are encouraged to contribute images for a more diverse crowdsourced effort.

Source: [https://commons.wikimedia.org/wiki/Natural_Image_Noise_Dataset](https://commons.wikimedia.org/wiki/Natural_Image_Noise_Dataset)
Image Source: [https://commons.wikimedia.org/wiki/Natural_Image_Noise_Dataset](https://commons.wikimedia.org/wiki/Natural_Image_Noise_Dataset)

Introducing paper: Natural Image Noise Dataset
NLI-PT

The first Portuguese dataset compiled for Native Language Identification (NLI), the task of identifying an author's first language based on their second language writing. The dataset includes 1,868 student essays written by learners of European Portuguese, native speakers of the following L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. NLI-PT includes the original student text and four different types of annotation: POS, fine-grained POS, constituency parses, and dependency parses. NLI-PT can be used not only in NLI but also in research on several topics in the field of Second Language Acquisition and educational NLP. 

Source: [A Portuguese Native Language Identification Dataset](/paper/a-portuguese-native-language-identification)

Introducing paper: A Portuguese Native Language Identification Dataset
NLI-TR
Full name: Natural Language Inference in Turkish

Natural Language Inference in Turkish (NLI-TR) provides translations of two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. 

Source: [Data and Representation for Turkish Natural Language Inference](/paper/use-of-machine-translation-to-obtain-labeled)
Image Source: [https://arxiv.org/pdf/2004.14963.pdf](https://arxiv.org/pdf/2004.14963.pdf)

Introducing paper: Data and Representation for Turkish Natural Language Inference
NoReC
Full name: Norwegian Review Corpus

The Norwegian Review Corpus (NoReC) was created for the purpose of training and evaluating models for document-level sentiment analysis. More than 43,000 full-text reviews have been collected from major Norwegian news sources and cover a range of different domains, including literature, movies, video games, restaurants, music and theater, in addition to product reviews across a range of categories. Each review is labeled with a manually assigned score of 1–6, as provided by the rating of the original author.

Source: [NoReC: The Norwegian Review Corpus](/paper/norec-the-norwegian-review-corpus)

Introducing paper: NoReC: The Norwegian Review Corpus
NoReC (fine-grained)

NoReC_fine is a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion. 

Source: [A Fine-Grained Sentiment Dataset for Norwegian](/paper/a-fine-grained-sentiment-dataset-for)

Introducing paper: A Fine-Grained Sentiment Dataset for Norwegian
Noun-Noun Compound Dataset

The noun–noun compounds dataset created by Fares (2016) consists of compounds annotated with two different taxonomies of relations; that is, for each noun–noun compound there are two distinct relations, drawing on different linguistic schools. The dataset was derived from existing linguistic resources, such as NomBank (Meyers et al., 2004) and the Prague Czech-English Dependency Treebank 2.0 (PCEDT; Hajič et al., 2012).

Source: [Noun-Noun Compound Dataset](https://github.com/ltgoslo/fun-nom)

Introducing paper: Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation
NREC Agricultural Person-Detection

A dataset to encourage research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from RTK GPS. 

Source: [Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset](/paper/comparing-apples-and-oranges-off-road)

Introducing paper: Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset
NSMC
Full name: Naver Sentiment Movie Corpus

This is a movie review dataset in the Korean language. Reviews were scraped from Naver Movies.

Source: [NSMC](https://github.com/e9t/nsmc/)
NTPairs
Full name: News-Tweet Paired Dataset

The **NTPairs** dataset consists of the pairs of news articles and their corresponding tweets that were published by eight media outlets in 2018. The eight outlets were selected to consider diverse outlets, which employ a different editing style for news sharing, in terms of publishing channels and political leaning.

Source: [https://github.com/bywords/NTPairs](https://github.com/bywords/NTPairs)
Image Source: [https://github.com/bywords/NTPairs](https://github.com/bywords/NTPairs)

Introducing paper: How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement
Numeric Fused-Head

The Numeric Fused-Head dataset consists of ~10K examples of crowd-sourced classified examples, labeled into 7 different categories, from two types. In the first type, Reference, the missing head is referenced explicitly somewhere else in the discourse, either in the same sentence or in surrounding sentences. In the second type, Implicit, the missing head does not appear in the text and needs to be inferred by the reader or hearer based on the context or world knowledge. This category was labeled into the 6 most common categories of the dataset. Models are evaluated based on accuracy.

Source: [NLP Progress](http://nlpprogress.com/english/missing_elements.html)

Introducing paper: Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution
NumerSense

Contains 13.6k masked-word-prediction probes, 10.5k for fine-tuning and 3.1k for testing.

Source: [Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models](/paper/birds-have-four-legs-numersense-probing)

Introducing paper: Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models
NWPU-Crowd

NWPU-Crowd consists of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (0~20,033). 

Source: [NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization](/paper/nwpu-crowd-a-large-scale-benchmark-for-crowd)

Introducing paper: NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
NYC3DCars

A vehicle detection database for vision tasks set in the real world.

Source: [NYC3DCars](http://nyc3d.cs.cornell.edu/)
NYTWIT

A collection of over 2,500 novel English words published in the New York Times between November 2017 and March 2019, manually annotated for their class of novelty (such as lexical derivation, dialectal variation, blending, or compounding). 

Source: [NYTWIT: A Dataset of Novel Words in the New York Times](/paper/nytwit-a-dataset-of-novel-words-in-the-new)

Introducing paper: NYTWIT: A Dataset of Novel Words in the New York Times
NYU Symmetry Database

The **NYU Symmetry** database contains 176 single-symmetry and 63 multiple-symmetry images (.png files) with accompanying ground-truth annotations (.mat files). Also included are a .m file to visualize the annotations on top of the images, and a .txt file with instructions on how to interpret the .mat annotations.

Introducing paper: A convolutional approach to reflection symmetry
O4B
Full name: Open4Business

O4B is a dataset of 17,458 open access business articles and their reference summaries. The dataset introduces a new challenge for summarization in the business domain, requiring highly abstractive and more concise summaries as compared to other existing datasets. 

Source: [Open4Business(O4B): An Open Access Dataset for Summarizing Business Documents](/paper/open4business-o4b-an-open-access-dataset-for)

Introducing paper: Open4Business(O4B): An Open Access Dataset for Summarizing Business Documents
OASIS
Full name: Open Annotations of Single Image Surfaces

A dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images.

Source: [OASIS: A Large-Scale Dataset for Single Image 3D in the Wild](/paper/oasis-a-large-scale-dataset-for-single-image-1)

Introducing paper: OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
Objectron

The **Objectron** dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists of 15K annotated video clips supplemented with over 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes. To ensure geo-diversity, the dataset is collected from 10 countries across five continents.

Source: [https://github.com/google-research-datasets/Objectron](https://github.com/google-research-datasets/Objectron)
Image Source: [https://github.com/google-research-datasets/Objectron](https://github.com/google-research-datasets/Objectron)

Introducing paper: Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations
OBP
Full name: Open Bandit Dataset

Open Bandit Dataset is a public real-world logged bandit feedback data. The dataset is provided by ZOZO, Inc., the largest Japanese fashion e-commerce company with over 5 billion USD market capitalization (as of May 2020). The company uses multi-armed bandit algorithms to recommend fashion items to users in a large-scale fashion e-commerce platform called ZOZOTOWN.

Source: [OBP](https://github.com/st-tech/zr-obp)

Introducing paper: Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Occ-Traj120

**Occ-Traj120** is a trajectory dataset that contains occupancy representations of different local-maps with associated trajectories. This dataset contains 400 locally-structured maps with occupancy representation and roughly around 120K trajectories in total.

Source: [https://github.com/soraxas/Occ-Traj120](https://github.com/soraxas/Occ-Traj120)
Image Source: [https://github.com/soraxas/Occ-Traj120](https://github.com/soraxas/Occ-Traj120)

Introducing paper: Occ-Traj120: Occupancy Maps with Associated Trajectories
OCR-VQA

The **OCR-VQA dataset** is a valuable resource for research in the field of **Visual Question Answering (VQA)**. Let me provide you with some details about it:

1. **Dataset Overview**:
    - The **OCR-VQA dataset** contains a total of **207,572 images** along with their associated **question-answer pairs**.
    - These images are related to **document content** and are accompanied by their corresponding **OCR transcriptions**¹².

2. **Purpose and Significance**:
    - **Visual Question Answering (VQA)** tasks require models to reason jointly over visual information (such as images) and natural language inputs (such as questions).
    - By using this dataset, researchers can develop and evaluate AI models that can effectively understand and answer questions based on visual content and textual context.

3. **Other Related VQA Datasets**:
    - Apart from OCR-VQA, there are other VQA datasets available for research and benchmarking:
        - **ScreenQA**: Focused on questions related to screen content.
        - **MP-DocVQA**: A dataset for document-based VQA.
        - **ChartQA**: Specifically designed for answering questions about charts.
        - **InfographicVQA**: For handling questions related to infographics.

Source: Conversation with Bing, 3/15/2024
(1) OCR-VQA Dataset | Papers With Code. https://paperswithcode.com/dataset/ocr-vqa.
(2) GitHub - anisha2102/docvqa: Document Visual Question Answering. https://github.com/anisha2102/docvqa.
(3) VQA: Visual Question Answering. https://visualqa.org/.
(4) allenai/aokvqa: Official repository for the A-OKVQA dataset - GitHub. https://github.com/allenai/aokvqa.
ODMS
Full name: Object Depth via Motion and Segmentation

**ODMS** is a dataset for learning Object Depth via Motion and Segmentation. ODMS training data are configurable and extensible, with each training example consisting of a series of object segmentation masks, camera movement distances, and ground truth object depth. As a benchmark evaluation, the dataset provides four ODMS validation and test sets with 15,650 examples in multiple domains, including robotics and driving.

Source: [https://github.com/griffbr/ODMS](https://github.com/griffbr/ODMS)
Image Source: [https://github.com/griffbr/ODMS](https://github.com/griffbr/ODMS)

Introducing paper: Learning Object Depth from Camera Motion and Video Object Segmentation
ODSQA
Full name: Open-Domain Spoken Question Answering

The **ODSQA** dataset is a spoken dataset for question answering in Chinese. It contains more than three thousand questions from 20 different speakers.

Source: [https://github.com/chiahsuan156/ODSQA](https://github.com/chiahsuan156/ODSQA)

Introducing paper: ODSQA: Open-domain Spoken Question Answering Dataset
OffComBR
Full name: Offensive Comments in the Brazilian Web

Offensive comments obtained from Brazilian website.

Source: [OffComBR](http://www.inf.ufrgs.br/~rppelle/hatedetector/)
Dataset of Structured Queries and Spatial Relations

Provides 450, 000 relevance annotations and 53 structured queries.

Source: [A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation](/paper/a-pooling-approach-to-modelling-spatial)

Introducing paper: A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation
OGTD
Full name: Offensive Greek Tweet Dataset

A manually annotated dataset containing 4,779 posts from Twitter annotated as offensive and not offensive. 

Source: [Offensive Language Identification in Greek](/paper/offensive-language-identification-in-greek)

Introducing paper: Offensive Language Identification in Greek
Oktoberfest Food Dataset

A realistic, diverse, and challenging dataset for object detection on images. The data was recorded at a beer tent in Germany and consists of 15 different categories of food and drink items. 

Source: [Oktoberfest Food Dataset](/paper/oktoberfest-food-dataset)

Introducing paper: Oktoberfest Food Dataset
Okutama-Action

A new video dataset for aerial view concurrent human action detection. It consists of 43 minute-long fully-annotated sequences with 12 action classes. Okutama-Action features many challenges missing in current datasets, including dynamic transition of actions, significant changes in scale and aspect ratio, abrupt camera movement, as well as multi-labeled actors.

Source: [Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection](/paper/okutama-action-an-aerial-view-video-dataset)

Introducing paper: Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection
OmniArt

Presents half a million samples and structured meta-data to encourage further research and societal engagement.

Source: [OmniArt: Multi-task Deep Learning for Artistic Data Analysis](/paper/omniart-multi-task-deep-learning-for-artistic)

Introducing paper: OmniArt: Multi-task Deep Learning for Artistic Data Analysis