Omkar2118/datasets huggingface dataset:
AND Dataset

The **AND Dataset** contains 13700 handwritten samples and 15 corresponding expert examined features for each sample. The dataset is released for public use and the methods can be extended to provide explanations on other verification tasks like face verification and bio-medical comparison. This dataset can serve as the basis and benchmark for future research in explanation based handwriting verification.

Source: [Explanation based Handwriting Verification](https://paperswithcode.com/paper/explanation-based-handwriting-verification)

Introducing paper: Explanation based Handwriting Verification
RITE
Full name: Retinal Images vessel Tree Extraction

The RITE (Retinal Images vessel Tree Extraction) is a database that enables comparative studies on segmentation or classification of arteries and veins on retinal fundus images, which is established based on the public available DRIVE database (Digital Retinal Images for Vessel Extraction).

RITE contains 40 sets of images, equally separated into a training subset and a test subset, the same as DRIVE. The two subsets are built from the corresponding two subsets in DRIVE. For each set, there is a fundus photograph, a vessel reference standard, and a Arteries/Veins (A/V) reference standard. 

* The fundus photograph is inherited from DRIVE. 
* For the training set, the vessel reference standard is a modified version of 1st_manual from DRIVE. 
* For the test set, the vessel reference standard is 2nd_manual from DRIVE. 
* For the A/V reference standard, four types of vessels are labelled using four colors based on the vessel reference standard. 
* Arteries are labelled in red; veins are labelled in blue; the overlapping of arteries and veins are labelled in green; the vessels which are uncertain are labelled in white. 
* The fundus photograph is in tif format. And the vessel reference standard and the A/V reference standard are in png format.  

The dataset is described in more detail in our paper, which you will cite if you use the dataset in any way: 

 Hu Q, Abràmoff MD, Garvin MK. Automated separation of binary overlapping trees in low-contrast color retinal images. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):436-43. PubMed PMID: 24579170 https://doi.org/10.1007/978-3-642-40763-5_54
Contract Discovery

A new shared task of semantic retrieval from legal texts, in which a so-called contract discovery is to be performed, where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts.

Source: [Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines](/paper/searching-for-legal-clauses-by-analogy-few)

Introducing paper: Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines
UI-PRMD
Full name: University of Idaho – Physical Rehabilitation Movement Dataset

UI-PRMD is a data set of movements related to common exercises performed by patients in physical therapy and rehabilitation programs. The data set consists of 10 rehabilitation exercises. A sample of 10 healthy individuals repeated each exercise 10 times in front of two sensory systems for motion capturing: a Vicon optical tracker, and a Kinect camera. The data is presented as positions and angles of the body joints in the skeletal models provided by the Vicon and Kinect mocap systems.

Introducing paper: A Deep Learning Framework for Assessing Physical Rehabilitation Exercises
d
Full name: d

s

Introducing paper: 0/1 Deep Neural Networks via Block Coordinate Descent
TVR
Full name: TV show Retrieval

A new multimodal retrieval dataset. TVR requires systems to understand both videos and their associated subtitle (dialogue) texts, making it more realistic. The dataset contains 109K queries collected on 21.8K videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal window. 

Source: [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](/paper/tvr-a-large-scale-dataset-for-video-subtitle)

Introducing paper: TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
DialogRE

**DialogRE** is the first human-annotated dialogue-based relation extraction dataset, containing 1,788 dialogues originating from the complete transcripts of a famous American television situation comedy Friends. The are annotations for all occurrences of 36 possible relation types that exist between an argument pair in a dialogue. DialogRE is available in English and Chinese.

Source: [DialogRE](https://dataset.org/dialogre/)

Introducing paper: Dialogue-Based Relation Extraction
Tweebank

Briefly describe the dataset. Provide:

* a high-level explanation of the dataset characteristics
* explain motivations and summary of its content
* potential use cases of the dataset

If the description or image is from a different paper, please refer to it as follows:
Source: [title](url)
Image Source: [title](url)

Introducing paper: Parsing Tweets into Universal Dependencies
ToLD-Br
Full name: Toxic Language Detection for Brazilian Portuguese

The **Toxic Language Detection for Brazilian Portuguese** (**ToLD-Br**) is a dataset with tweets in Brazilian Portuguese annotated according to different toxic aspects.

Source: [https://github.com/JAugusto97/ToLD-Br](https://github.com/JAugusto97/ToLD-Br)

Introducing paper: Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis
MIMIC-CXR
Full name: MIMIC-CXR

**MIMIC-CXR** from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA.

Source: [Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification](https://arxiv.org/abs/1909.01940)
Image Source: [https://arxiv.org/abs/1901.07042](https://arxiv.org/abs/1901.07042)

Introducing paper: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs