Omkar2118/datasets huggingface dataset:
CPCXR
Full name: COVID-19 Posteroanterior Chest X-Ray fused

The **COVID-19 Posteroanterior Chest X-Ray fused** (**CPCXR**) dataset is generated by the fusion of three publicly available datasets: COVID-19 cxr image, Radiological Society of North America (RSNA), and U.S. national library of medicine (USNLM) collected Montgomery country - NLM(MC). The dataset consists of samples of diseases labeled as COVID-19, Tuberculosis, Other pneumonia (SARS, MERS, etc.), and Normal. The dataset can be utilized to train an evaulate deep learning and machine learning models as binary and multi-class classification problem.

Source: [https://github.com/nspunn1993/COVID-19-PA-CXR-fused-dataset](https://github.com/nspunn1993/COVID-19-PA-CXR-fused-dataset)

Introducing paper: Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
CPH

A large-scale database including substantial CU partition data for HEVC intra- and inter-modes. This enables deep learning on the CU partition.

Source: [Reducing Complexity of HEVC: A Deep Learning Approach](/paper/reducing-complexity-of-hevc-a-deep-learning)

Introducing paper: Reducing Complexity of HEVC: A Deep Learning Approach
CPLFW
Full name: Cross-Pose LFW

A renovation of Labeled Faces in the Wild (LFW), the de facto standard testbed for unconstraint face verification. 

There are three motivations behind the construction of CPLFW benchmark as follows:

1.Establishing a relatively more difficult database to evaluate the performance of real world face verification so the effectiveness of several face verification methods can be fully justified.

2.Continuing the intensive research on LFW with more realistic consideration on pose intra-class variation and fostering the research on cross-pose face verification in unconstrained situation. The challenge of CPLFW emphasizes pose difference to further enlarge intra-class variance. Also, negative pairs are deliberately selected to avoid different gender or race. CPLFW considers both the large intra-class variance and the tiny inter-class variance simultaneously.

3.Maintaining the data size, the face verification protocol which provides a 'same/different' benchmark and the same identities in LFW, so one can easily apply CPLFW to evaluate the performance of face verification.

Source: [CPLFW](http://whdeng.cn/CPLFW/index.html)
CPP
Full name: Chinese Polyphones with Pinyin

A benchmark dataset that consists of 99,000+ sentences for Chinese polyphone disambiguation. 

Source: [g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset](/paper/g2pm-a-neural-grapheme-to-phoneme-conversion)

Introducing paper: g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset
CQR
Full name: Contextual Query Rewrite

CQR is an extension to the Stanford Dialogue Corpus. It contains crowd-sourced rewrites to facilitate research in dialogue state tracking using natural language as the interface.

Source: [A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)](/paper/a-dataset-for-resolving-referring-expressions)

Introducing paper: A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
CraigslistBargains

A richer dataset based on real items on Craigslist.

Source: [Decoupling Strategy and Generation in Negotiation Dialogues](/paper/decoupling-strategy-and-generation-in)

Introducing paper: Decoupling Strategy and Generation in Negotiation Dialogues
Common Crawl Domain Names

Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to "common crawl").

Source: [Common Crawl Domain Names](https://github.com/google-research-datasets/common-crawl-domain-names)
CRD3
Full name: Critical Role Dungeons and Dragons Dataset

The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player collaboration and spoken interaction.

Source: [Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset](/paper/storytelling-with-dialogue-a-critical-role)

Introducing paper: Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset
Creative Flow+ Dataset

Includes 3000 animated sequences rendered using styles randomly selected from 40 textured line styles and 38 shading styles, spanning the range between flat cartoon fill and wildly sketchy shading. The dataset includes 124K+ train set frames and 10K test set frames rendered at 1500x1500 resolution, far surpassing the largest available optical flow datasets in size.

Source: [Creative Flow+ Dataset](/paper/creative-flow-dataset)

Introducing paper: Creative Flow+ Dataset
CRL-Person

Provides two large-scale multi-step benchmarks for biometric identification, where the visual appearance of different classes are highly relevant.

Source: [Continual Representation Learning for Biometric Identification](/paper/continual-representation-learning-for)

Introducing paper: Continual Representation Learning for Biometric Identification
Crowd Dataset

A dense crowd dataset with manually annotated groundtruth, collected from different public datasets. This dataset comprises 20 videos that exhibit a multitude of motion behaviors that cover both the obvious and subtle instabilities.

Source: [Crowd Dataset](http://cs-chan.com/project4.htm)

Introducing paper: Detection of Salient Regions in Crowded Scenes
CrowdFix

Contributes dataset: (1) reviewing the dynamics behind saliency and crowds. (2) using eye tracking to create a dynamic human eye fixation dataset over a new set of crowd videos gathered from the Internet. The videos are annotated into three distinct density levels. 

Source: [CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos](/paper/crowdfix-an-eyetracking-data-set-of-human)

Introducing paper: CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos
CrowdFlow
Full name: TUB CrowdFlow

The **TUB CrowdFlow** is a synthetic dataset that contains 10 sequences showing 5 scenes. Each scene is rendered twice: with a static point of view and a dynamic camera to simulate drone/UAV based surveillance. The scenes are render using Unreal Engine at HD resolution (1280x720) at 25 fps, which is typical for current commercial CCTV surveillance systems. The total number of frames is 3200.

Each sequence has the following ground-truth data:

* Optical flow fields
* Person trajectories (up to 1451)
* Dense pixel trajectories

Source: [Optical Flow Dataset and Benchmark for Visual Crowd Analysis](/paper/optical-flow-dataset-and-benchmark-for-visual)

Introducing paper: Optical Flow Dataset and Benchmark for Visual Crowd Analysis
CrowS-Pairs

CrowS-Pairs has 1508 examples that cover stereotypes dealing with nine types of bias, like race, religion, and age. In CrowS-Pairs a model is presented with two sentences: one that is more stereotyping and another that is less stereotyping. The data focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups.

Source: [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](/paper/crows-pairs-a-challenge-dataset-for-measuring)

Introducing paper: CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
CRVD
Full name: Captured Raw Video Denoising

The CRVD dataset consists of 55 groups of noisy-clean videos with ISO values ranging from 1600 to 25600.

Source: [Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes](/paper/supervised-raw-video-denoising-with-a)

Introducing paper: Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes
CS
Full name: Chinese Simile

This dataset is constructed and based on the online free-access fictions that are tagged with sci-fi, urban novel, love story, youth, etc. It is used for Writing Polishment with Smile (WPS) a task that aims to polish plain text with similes.
All similes are extracted by rich regular expression, and the extraction precision is estimated as 92% by labelling 500 random extracted samples.
It contains 5M samples for training and 2.5k for validation and test respectively.

Source: [https://github.com/mrzjy/writing-polishment-with-simile](https://github.com/mrzjy/writing-polishment-with-simile)

Introducing paper: Writing Polishment with Simile: Task, Dataset and A Neural Approach
CSD
Full name: Collaborative SLAM Dataset

Comprises 4 different subsets - Flat, House, Priory and Lab - each containing a number of different sequences that can be successfully relocalised against each other. 

Source: [CSD](https://github.com/torrvision/CollaborativeSLAMDataset)

Introducing paper: Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation
CSPubSum

CSPubSum is a dataset for summarisation of computer science publications, created by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. 

Source: [A Supervised Approach to Extractive Summarisation of Scientific Papers](/paper/a-supervised-approach-to-extractive)

Introducing paper: A Supervised Approach to Extractive Summarisation of Scientific Papers