Omkar2118/datasets huggingface dataset:
ARCTIC
Full name: Articulated Objects in Free-form Hand Interaction

ARCTIC is a dataset of free-form interactions of hands and articulated objects. ARCTIC has 1.2M images paired with accurate 3D meshes for both hands and for objects that move and deform over time. The dataset also provides hand-object contact information.

Introducing paper: ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation
MeSHup
Full name: A Corpus for Full Text Biomedical Document Indexing

Contains 1,342,667 full text articles in English, together with the associated MeSH labels and metadata, authors, and publication venues that are collected from the MEDLINE database.

Introducing paper: MeSHup: A Corpus for Full Text Biomedical Document Indexing
M-Phasis
Full name: A Feature-Based Corpus of Hate Online

A corpus of 9k German and French user comments collected from migration-related news articles. It goes beyond the hate-neutral dichotomy and is instead annotated with 23 features, which in combination become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate. The annotations are performed by 4 native speakers per language and achieve high (0.77) inter-annotator agreements.

Introducing paper: Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online
W-Oops

W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 30 unintentional video-level activity labels collected through human annotations.

Introducing paper: Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos
SkillSpan
Full name: Hard and Soft Skill Extraction from English Job Postings

**SkillSpan** is a dataset for Skill Extraction (SE). It is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, the authors introduce SkillSpan, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans.

Introducing paper: SkillSpan: Hard and Soft Skill Extraction from English Job Postings
CoVERT
Full name: A Corpus of Fact-checked Biomedical COVID-19 Tweets

CoVERT is a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19-related (mis)information. The corpus consists of 300 tweets, each annotated with medical named entities and relations. Employs a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in moderate inter-annotator agreement.

Introducing paper: CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets
Monant Medical Misinformation

This dataset of medical misinformation was collected and is published by Kempelen Institute of Intelligent Technologies (KInIT). It consists of approx. 317k news articles and blog posts on medical topics published between January 1, 1998 and February 1, 2022 from a total of 207 reliable and unreliable sources. The dataset contains full-texts of the articles, their original source URL and other extracted metadata. If a source has a credibility score available (e.g., from Media Bias/Fact Check), it is also included in the form of annotation. Besides the articles, the dataset contains around 3.5k fact-checks and extracted verified medical claims with their unified veracity ratings published by fact-checking organisations such as Snopes or FullFact. Lastly and most importantly, the dataset contains 573 manually and more than 51k automatically labelled mappings between previously verified claims and the articles; mappings consist of two values: claim presence (i.e., whether a claim is contained in the given article) and article stance (i.e., whether the given article supports or rejects the claim or provides both sides of the argument).

The dataset is primarily intended to be used as a training and evaluation set for machine learning methods for claim presence detection and article stance classification, but it enables a range of other misinformation related tasks, such as misinformation characterization or analyses of misinformation spreading.

Introducing paper: Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims
StyleGAN-Human

A large-scale human image dataset with over 230K samples capturing diverse poses and textures.

Introducing paper: StyleGAN-Human: A Data-Centric Odyssey of Human Generation