Omkar2118/datasets huggingface dataset:
CASCONet

**CASCONet** is a a collection of data about the CAS Conference (CASCON) for the past 25 years including information about papers, technology showcase demos, workshops, and keynote presentations.

Introducing paper: CASCONet: A Conference dataset
VeReMi

The Vehicular Reference Misbehavior (VeReMi) dataset, is a dataset for the evaluation of misbehavior detection mechanisms for VANETs (vehicular networks). This dataset consists of message logs of on-board units, including a labelled ground truth, generated from a simulation environment. The dataset includes malicious messages intended to trigger incorrect application behavior, which is what misbehavior detection mechanisms aim to prevent. The initial dataset contains a number of simple attacks: the idea of this dataset release is not just to provide a baseline for the comparison of detection mechanisms, but also to serve as a starting point for more complex attacks.

Introducing paper: VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs
YT-UGC
Full name: YouTube UGC

**YT-UGC** is a large scale UGC (User Generated Content) dataset (1,500 20 sec video clips) sampled from millions of YouTube videos. The dataset covers popular categories like Gaming, Sports, and new features like High Dynamic Range (HDR). This dataset can be used to study video compression and quality assessment.

Introducing paper: YouTube UGC Dataset for Video Compression Research
DeepMIMO

**DeepMIMO** is a generic dataset for mmWave/massive MIMO channels. The DeepMIMO dataset generation framework has two important features. First, the DeepMIMO channels are constructed based on accurate ray-tracing data obtained from Remcom Wireless InSite. The DeepMIMO channels, therefore, capture the dependence on the environment geometry/materials and transmitter/receiver locations, which is essential for several machine learning applications. Second, the DeepMIMO dataset is generic/parameterized as the researcher can adjust a set of system and channel parameters to tailor the generated DeepMIMO dataset for the target machine learning application. The DeepMIMO dataset can then be completely defined by the (i) the adopted ray-tracing scenario and (ii) the set of parameters, which enables the accurate definition and reproduction of the dataset.

Introducing paper: DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications
US-Accidents

This is a countrywide traffic accident dataset, which covers 49 states of the United States. The data is continuously being collected from February 2016, using several data providers, including two APIs which provide streaming traffic event data. These APIs broadcast traffic events captured by a variety of entities, such as the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road-networks. Currently, there are about 4.2 million accident records in this dataset.

Introducing paper: A Countrywide Traffic Accident Dataset
Pushshift Reddit

Pushshift makes available all the submissions and comments posted on Reddit between June 2005 and April 2019. The dataset consists of 651,778,198 submissions and 5,601,331,385 comments posted on 2,888,885 subreddits.

Introducing paper: The Pushshift Reddit Dataset
HoaxItaly

**HoaxItaly** consists of over 1 million tweets shared during 2019 and containing links to thousands of news articles published on two classes of Italian outlets: (1) disinformation websites, i.e. outlets which have been repeatedly flagged by journalists and fact-checkers for producing low-credibility content such as false news, hoaxes, click-bait, misleading and hyper-partisan stories; (2) fact-checking websites which notably debunk and verify online news and claims. The dataset includes title and body for approximately 37k news articles.

Introducing paper: HoaxItaly: a collection of Italian disinformation and fact-checking stories shared on Twitter in 2019
GED
Full name: Gridded Establishment Dataset

**GED** is a dataset on the economic activity of mainland China, which measures the volume of establishments at a 0.01 latitude by 0.01 longitude scale. Specifically, the dataset captures the geographically based opening and closing of approximately 25.5 million firms that registered in mainland China over the period 2005-2015. The characteristics of fine granularity and long-term observability give the GED a high application value.

Introducing paper: A gridded establishment dataset as a proxy for economic activity in China
Interactive Gibson Environment

Interactive Gibson is a comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task. The benchmark has two main components:

* The **Interactive Gibson Environment**,  which simulates high fidelity visuals of indoor scenes, and high fidelity physical dynamics of the robot and common objects found in these scenes.
* set of Interactive Navigation metrics which allows one to study the interplay between navigation and physical simulation.

Introducing paper: Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments