Omkar2118/datasets huggingface dataset:
SuperCaustics

SuperCaustics is a simulation tool made in Unreal Engine for generating massive computer vision datasets that include transparent objects.

SuperCaustics is a real-time, open-source simulation of transparent objects designed for deep learning applications. SuperCaustics features extensive modules for stochastic environment creation; uses hardware ray-tracing to support caustics, dispersion, and refraction; and enables generating massive datasets with multi-modal, pixel-perfect ground truth annotations.

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  <img src="https://github.com/MMehdiMousavi/SuperCaustics/raw/main/Assets/SuperCaustics.gif" alt="drawing" width="600"/>
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Introducing paper: SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications
Supplementary data: "Revealing drivers and risks for power grid frequency stability with explainable AI"

This repository contains processed data and result files for the paper "Revealing drivers and risks for power grid frequency stability with explainable AI".

Introducing paper: Revealing drivers and risks for power grid frequency stability with explainable AI
Pre-Processed Power Grid Frequency Time Series

This repository contains ready-to-use frequency time series as well as the corresponding pre-processing scripts in python.

Introducing paper: Predictability of Power Grid Frequency
CDC fluview
Full name: National, Regional, and State Level Outpatient Illness and Viral Surveillance

country- and state-level historical ILI data from
2010 to 2018 from the CDC (CDC).

Introducing paper: Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
MDBD
Full name: Multicue Dataset for Edge Detection

In order to study the interaction of several early visual cues (luminance, color, stereo, motion) during boundary detection in challenging natural scenes, we have built a multi-cue video dataset composed of short binocular video sequences of natural scenes using a consumer-grade Fujifilm stereo camera (Mély, Kim, McGill, Guo and Serre, 2016). We considered a variety of places (from university campuses to street scenes and parks) and seasons to minimize possible biases. We attempted to capture more challenging scenes for boundary detection by framing a few dominant objects in each shot under a variety of appearances. Representative sample keyframes are shown on the figure below. The dataset contains 100 scenes, each consisting of a left and right view short (10-frame) color sequence. Each sequence was sampled at a rate of 30 frames per second. Each frame has a resolution of 1280 by 720 pixels.
NASA C-MAPSS-2
Full name: Turbofan Engine Degradation Simulation Data Set-2

The generation of data-driven prognostics models requires the availability of datasets with run-to-failure trajectories. In order to contribute to the development of these methods, the dataset provides a new realistic dataset of run-to-failure trajectories for a small fleet of aircraft engines under realistic flight conditions. The damage propagation modelling used for the generation of this synthetic dataset builds on the modeling strategy from previous work . The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model. The data set is been provided by the Prognostics CoE at NASA Ames in collaboration with ETH Zurich and PARC.