Omkar2118/datasets huggingface dataset:
MuSe-CaR
Full name: Multimodal Sentiment Analysis in Car Reviews

The **MuSe-CAR** database is a large, multimodal (video, audio, and text) dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. 

The estimated age range of the professional, semi-professional (influncers), and casual reviewers is between the middle of 20s until the late 50s. Most are native English speakers from the UK or the US, while a small minority are non-native, yet fluent English speakers.

Introducing paper: The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements
OpenWPM Crawls

**OpenWPM Crawls** is a dataset of 103 online, mostly mainstream news websites. With the help of two experts, alongside data from the Media Ownership Monitor of the Reporters without Borders, we label these websites according to their partisanship (Left, Right, or Centre). We study and compare user tracking on these sites with different metrics: numbers of cookies, cookie synchronizations, device fingerprinting, and invisible pixel-based tracking. We find that Left and Centre websites serve more cookies than Right-leaning websites. However, through cookie synchronization, more user IDs are synchronized in Left websites than Right or Centre. Canvas fingerprinting is used similarly by Left and Right, and less by Centre. Invisible pixel-based tracking is 50% more intense in Centre-leaning websites than Right, and 25% more than Left. Desktop versions of news websites deliver more cookies than their mobile counterparts. A handful of third-parties are tracking users in most websites in this study.

Introducing paper: Under the Spotlight: Web Tracking in Indian Partisan News Websites
MRPB 1.0

**MRPB 1.0** is a mobile robot local planning benchmark. The benchmark facilitates both motion planning researchers who want to compare the performance of a new local planner relative to many other state-of-the-art approaches as well as end users in the mobile robotics industry who want to select a local planner that performs best on some problems of interest.

Introducing paper: MRPB 1.0: A Unified Benchmark for the Evaluation of Mobile Robot Local Planning Approaches
Algonauts 2021
Full name: How the Human Brain Makes Sense of a World in Motion

The Algonauts dataset provides human brain responses to a set of 1,102 3-s long video clips of everyday events. The brain responses are measured with functional magnetic resonance imaging (fMRI). fMRI is a widely used brain imaging technique with high spatial resolution that measures blood flow changes associated with neural responses. 

**Splits:**

*Training:* 
The training set consists of 1,000 video clips and the associated brain responses. The brain responses are provided here in two tracks corresponding to two independent tracks in the Algonauts challenge. In the first track, brain responses provided are from a set of specific regions of interest (ROIs) known to play a key role in visual perception. These ROIs start in early and mid-level visual cortex (V1, V2, V3, and V4) and extend into higher-level cortex that responds preferentially to all objects or particular categories (Body- EBA; Face - FFA, STS; Object - LOC; Scene - PPA).  In the second track, brain responses provided are from selected voxels across the whole brain showing reliable responses to videos.  

*Test:*
The test set consists of 102 short videos. The associated brain responses will be released at a later date.

For further details see [here](http://algonauts.csail.mit.edu/).

Introducing paper: The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motion