Omkar2118/datasets huggingface dataset:
One Million Posts Corpus

An annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).

DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website, there is a discussion section below each news article where readers engage in online discussions. The data set contains a selection of user posts from the 12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and 1,000,000 unlabeled posts in the data set. The labeled posts were annotated by professional forum moderators employed by the newspaper.

Source: [One Million Posts Corpus](https://ofai.github.io/million-post-corpus/)
OneStopEnglish

Useful for through two applications - automatic readability assessment and automatic text simplification. The corpus consists of 189 texts, each in three versions (567 in total).

Source: [OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification](/paper/onestopenglish-corpus-a-new-corpus-for)

Introducing paper: OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification
OneStopQA

OneStopQA provides an alternative test set for reading comprehension which alleviates these shortcomings and has a substantially higher human ceiling performance.

Source: [STARC: Structured Annotations for Reading Comprehension](/paper/starc-structured-annotations-for-reading)

Introducing paper: STARC: Structured Annotations for Reading Comprehension
OOVD
Full name: ORNL Overhead Vehicle Dataset

This data set was created to understand the potential for machine learning, computer vision, and HPC to improve the energy efficiency aspects of traffic control by leveraging GRIDSMART traffic cameras as sensors for adaptive traffic control, with a sensitivity to the fuel consumption characteristics of the traffic in the camera’s visual field. GRIDSMART cameras—an existing, fielded commercial product—sense the presence of vehicles at intersections and replace more conventional sensors (such as inductive loops) to issue calls to traffic control. These cameras, which have horizon-to-horizon view, offer the potential for an improved view of the traffic environment which can be used to generate better control algorithms.

Source: [OOVD](https://www.ornl.gov/project/ornl-overhead-vehicle-dataset-oovd)
openDD

Annotated using images taken by a drone in 501 separate flights, totalling in over 62 hours of trajectory data. As of today, openDD is by far the largest publicly available trajectory dataset recorded from a drone perspective, while comparable datasets span 17 hours at most.

Source: [openDD: A Large-Scale Roundabout Drone Dataset](/paper/opendd-a-large-scale-roundabout-drone-dataset)

Introducing paper: openDD: A Large-Scale Roundabout Drone Dataset
OpenEDS

OpenEDS (Open Eye Dataset) is a large scale data set of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabelled eye-images, (iii) 91,200 frames from randomly selected video sequence of 1.5 seconds in duration and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study. 

Source: [OpenEDS: Open Eye Dataset](/paper/190503702)
Image Source: [https://research.fb.com/programs/openeds-challenge](https://research.fb.com/programs/openeds-challenge)

Introducing paper: OpenEDS: Open Eye Dataset
OpenEDS2020

OpenEDS2020 is a dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras. The dataset, which is anonymized to remove any personally identifiable information on participants, consists of 80 participants of varied appearance performing several gaze-elicited tasks, and is divided in two subsets: 1) Gaze Prediction Dataset, with up to 66,560 sequences containing 550,400 eye-images and respective gaze vectors, created to foster research in spatio-temporal gaze estimation and prediction approaches; and 2) Eye Segmentation Dataset, consisting of 200 sequences sampled at 5 Hz, with up to 29,500 images, of which 5% contain a semantic segmentation label, devised to encourage the use of temporal information to propagate labels to contiguous frames. 

Source: [OpenEDS2020: Open Eyes Dataset](/paper/openeds2020-open-eyes-dataset)
Image Source: [Palmero et al](https://arxiv.org/pdf/2005.03876v1.pdf)

Introducing paper: OpenEDS2020: Open Eyes Dataset