Omkar2118/datasets huggingface dataset:
PerCQA

PerCQA is the first Persian dataset for CQA (Community Question Answering). This dataset contains the questions and answers crawled from the most well-known Persian forum.

Introducing paper: PerCQA: Persian Community Question Answering Dataset
ArtImage

**ArtImage** is a synthetic dataset of articulated object models of 5 categories from PartNet-Mobility for articulated object tasks in category level.

Introducing paper: OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval
ASCEND

**ASCEND** (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese code-switching corpus collected in Hong Kong. ASCEND includes 23 bilinguals that are fluent in both Chinese and English and consists of 10.62 hours clean speech corpus.

Introducing paper: ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation
MetaEval

**MetaEval** is a collection of 101 NLP tasks. It consists of 101 tasks in a benchmark that can be used for future probing and transfer learning.

Introducing paper: Analysis and Prediction of NLP Models Via Task Embeddings
Learn2Reg

**Learn2Reg** is a dataset for medical image registration. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation.

Introducing paper: Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
LOOK

**LOOK** is a large-scale dataset for eye contact detection in the wild, which focuses on diverse and unconstrained scenarios for real-world generalization. The dataset focuses on real-world scenarios for autonomous vehicles with no control over the environment or the distance of pedestrians

Introducing paper: Do Pedestrians Pay Attention? Eye Contact Detection in the Wild
VocBench

**VocBench** is a framework that benchmark the performance of state-of-the art neural vocoders. VocBench uses a systematic study to evaluate different neural vocoders in a shared environment that enables a fair comparison between them.

Introducing paper: VocBench: A Neural Vocoder Benchmark for Speech Synthesis
ES-ImageNet

**ES-ImageNet** is a large-scale event-stream dataset for SNNs and neuromorphic vision. It consists of about 1.3 M samples converted from ILSVRC2012 in 1000 different categories. ES-ImageNet is dozens of times larger than other neuromorphic classification datasets at present and completely generated by the software

Introducing paper: ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks
CD&S
Full name: Corn Disease and Severity

The Corn Disease and Severity (**CD&S**) dataset consists of 511, 524, and 562, field acquired raw images, corresponding to three common foliar corn diseases, namely Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and Northern Leaf Spot.

Introducing paper: CD&S Dataset: Handheld Imagery Dataset Acquired Under Field Conditions for Corn Disease Identification and Severity Estimation
NOD
Full name: Night Object Detection

This is a high-quality large-scale Night Object Detection (NOD) dataset of outdoor images targeting low-light object detection. The dataset contains more than 7K images and 46K annotated objects (with bounding boxes) that belong to classes: person, bicycle, and car. The photos were taken on the streets at evening hours, and thus all images present low-light conditions to a varying degree of severity.

Introducing paper: NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset
Curlie

**Curlie dataset** is a dataset with more than 1M websites in 92 languages with relative labels collected from Curlie, the largest multilingual crowdsourced Web directory. The dataset contains 14 website categories aligned across languages. It is used for language-agnostic website embedding and classification

Introducing paper: Homepage2Vec: Language-Agnostic Website Embedding and Classification
DeepLesion

The National Institutes of Health’s Clinical Center has made a large-scale dataset of CT images publicly available to help the scientific community improve detection accuracy of lesions. While most publicly available medical image datasets have less than a thousand lesions, this dataset, named DeepLesion, has over 32,000 annotated lesions (220GB) identified on CT images.
DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications

Introducing paper: DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations
BRACS
Full name: BReAst Carcinoma Subtyping

BReAst Carcinoma Subtyping (**BRACS**) dataset, a large cohort of annotated Hematoxylin & Eosin (H&E)-stained images to facilitate the characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs), and 4539 Regions of Interest (ROIs) extracted from the WSIs. Each WSI, and respective ROIs, are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories.

Introducing paper: BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
EEGEyeNet

**EEEyeNet** is a dataset and benchmark with the goal of advancing research in the intersection of brain activities and eye movements. It consists of simultaneous Electroencephalography (EEG) and Eye-tracking (ET) recordings from 356 different subjects collected from three different experimental paradigms.

Introducing paper: EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction
PaSa

**PaSa** is a dataset to train Machine Learning algorithms to automate the highlighting of patent paragraphs with semantic annotations. It consists of 150k samples obtained by traversing USPTO patents over a decade

Introducing paper: Patent Sentiment Analysis to Highlight Patent Paragraphs
CUB-GHA
Full name: CUB Gaze-based Human Attention

**CUB-GHA** is a dataset for fine-grained classification with human attention annotations. The dataset collects human gaze data for the fine-grained classification dataset CUB and builds a dataset named CUB-GHA (Gaze-based Human Attention).

Introducing paper: Human Attention in Fine-grained Classification
UMLS
Full name: Unified Medical Language System

The **Unified Medical Language System (UMLS)** is a comprehensive resource that integrates and disseminates essential terminology, classification standards, and coding systems. Its purpose is to foster the creation of more effective and interoperable biomedical information systems and services, including electronic health records. Here are the key aspects of the UMLS:

1. **Integration of Vocabularies**: The UMLS brings together various health and biomedical vocabularies and standards. It acts as a bridge, enabling interoperability between different computer systems by harmonizing terminology.

2. **Components**:
    - **Metathesaurus**: This component contains terms and codes from multiple vocabularies, including CPT, ICD-10-CM, LOINC, MeSH, RxNorm, and SNOMED CT. It provides hierarchies, definitions, relationships, and other attributes.
    - **Semantic Network**: The Semantic Network defines broad categories (semantic types) and their relationships (semantic relations).
    - **SPECIALIST Lexicon and Lexical Tools**: These tools include a large syntactic lexicon for biomedical and general English. They assist in normalizing strings, generating lexical variants, and creating indexes.

3. **Use Cases**:
    - **Clinical Practice**: Link terms and codes across healthcare entities (doctors, pharmacies, insurance companies).
    - **Patient Care Coordination**: Facilitate communication among hospital departments.
    - **Text Processing**: Extract concepts, relationships, or knowledge from medical texts.
    - **Terminology Mapping**: Map between different terminologies.
    - **Local Terminology Development**: Create and maintain local terminologies.
    - **Research**: Investigate terminologies or ontologies.

4. **Accessing the UMLS**:
    - **Web Browsers**: You can search and explore UMLS data using UTS applications like the Metathesaurus Browser (retrieve concept information) and the Semantic Network Browser (view semantic types and relations).
    - **Local Installation**: Download UMLS files and use the MetamorphoSys tool to customize the UMLS for your specific needs. Load the customized data into your own database system or browse it using the MetamorphoSys RRF browser.

Source: Conversation with Bing, 3/18/2024
(1) Unified Medical Language System (UMLS) - National Library of Medicine. https://www.nlm.nih.gov/research/umls/index.html.
(2) UMLS Metathesaurus Browser. https://uts.nlm.nih.gov/uts/umls/home.
(3) GitHub - dongwookim-ml/kg-data: knowledge-graph datasets. https://github.com/dongwookim-ml/kg-data.
(4) UMLS Dataset | Papers With Code. https://paperswithcode.com/dataset/umls.

Introducing paper: UMLS::Similarity: Measuring the Relatedness and Similarity of Biomedical Concepts