Omkar2118/datasets huggingface dataset:
RSPECT
Full name: The RSNA Pulmonary Embolism CT

**The RSNA Pulmonary Embolism CT** (**RSPECT**) Dataset is composed of CT pulmonary angiogram images and annotations related to pulmonary embolism. It's part of the 2020 RSNA Pulmonary Embolism Detection Challenge which invited researchers to develop machine-learning algorithms to detect and characterize instances of pulmonary embolism (PE) on chest CT studies. The competition, conducted in collaboration with the Society of Thoracic Radiology (STR), involved creating the largest publicly available annotated PE dataset, comprised of more than 12,000 CT studies. Imaging data was contributed by five international research centers and labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists. For the first time in an RSNA data challenge, the rules required competitors to submit and run their code in a standard shared environment, producing simpler, more readily usable models.

Source: [](https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pe-detection-challenge-2020)
SEP-28k
Full name: Stuttering Events in Podcasts

Stuttering Events in Podcasts (SEP-28k) is a dataset containing over 28k clips labeled with five event types including blocks, prolongations, sound repetitions, word repetitions, and interjections. Audio comes from public podcasts largely consisting of people who stutter interviewing other people who stutter. 

Source: [Lea et al.](https://arxiv.org/pdf/2102.12394.pdf)

Image source: [Lea et al.](https://arxiv.org/pdf/2102.12394.pdf)

Introducing paper: SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter
FluencyBank

**FluencyBank** is a shared database for the study of fluency development. Participants include typically-developing monolingual and bilingual children, children and adults who stutter (C/AWS) or who clutter (C/AWC), and second language learners.


Image Source: [FluencyBank](https://fluency.talkbank.org/)
MHIST
Full name: Minimalist Histopathology image analysis dataset

The **m**inimalist **hist**opathology image analysis dataset (**MHIST**) is a binary classification dataset of 3,152 fixed-size images of colorectal polyps, each with a gold-standard label determined by the majority vote of seven board-certified gastrointestinal pathologists. MHIST also includes each image’s annotator agreement level. As a minimalist dataset, MHIST occupies less than 400 MB of disk space, and a ResNet-18 baseline can be trained to convergence on MHIST in just 6 minutes using approximately 3.5 GB of memory on a NVIDIA RTX 3090. As example use cases, the authors use MHIST to study natural questions that arise in histopathology image classification such as how dataset size, network depth, transfer learning, and high-disagreement examples affect model performance.

Source: [Wei et al.](https://arxiv.org/pdf/2101.12355.pdf)

Image source: [Wei et al.](https://arxiv.org/pdf/2101.12355.pdf)

Introducing paper: A Petri Dish for Histopathology Image Analysis
CC-News
Full name: CommonCrawl News dataset

**CommonCrawl News** is a dataset containing news articles from news sites all over the world. The dataset is available in form of Web ARChive (WARC) files that are released on a daily basis.

Source: [https://commoncrawl.org/2016/10/news-dataset-available/](https://commoncrawl.org/2016/10/news-dataset-available/)
MalNet

MalNet is a large public graph database, representing a large-scale ontology of software function call graphs. MalNet contains over 1.2 million graphs, averaging over 17k nodes and 39k edges per graph, across a hierarchy of 47 types and 696 families.

Image Source: [Expore MalNet](https://mal-net.org/explore)

Introducing paper: A Large-Scale Database for Graph Representation Learning
IBM-Rank-30k
Full name: IBM-ArgQ-Rank-30kArgs

The IBM-Rank-30k is a dataset for the task of argument quality ranking. It is a corpus of 30,497 arguments carefully annotated for point-wise quality.

Image Source: [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://arxiv.org/pdf/1911.11408v1.pdf)

Introducing paper: A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis
OTEANNv3

This dataset contains orthographic samples of words in 19 languages (ar, br, de, en, eno, ent, eo, es, fi, fr, fro, it, ko, nl, pt, ru, sh, tr, zh). Each sample contains two text features: a Word (the textual representation of the word according to its orthography) and a Pronunciation (the highest-surface IPA pronunciation of the word as pronunced in its language).

Introducing paper: OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network