Omkar2118/datasets huggingface dataset:
Bus Trajectory Dataset

This dataset contains the bus trajectory dataset collected by 6 volunteers who were asked to travel across the sub-urban city of Durgapur, India, on intra-city buses (route name: 54 Feet). During the travel, the volunteers captured sensor logs through an Android application installed on COTS smartphones.

Introducing paper: Exploiting Multi-modal Contextual Sensing for City-bus's Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction
DukeMTMC-VideoReID-DL

DukeMTMC-VideoReID-DL processed with our re-Detect and Link (DL) module.

Introducing paper: Video-based Person Re-identification without Bells and Whistles
PHASE
Full name: PHysically-grounded Abstract Social Events

PHASE is a dataset of physically-grounded abstract social events, that resemble a wide range of real-life social interactions by including social concepts such as helping another agent. PHASE consists of 2D animations of pairs of agents moving in a continuous space generated procedurally using a physics engine and a hierarchical planner. Agents have a limited field of view, and can interact with multiple objects, in an environment that has multiple landmarks and obstacles. Using PHASE, we design a social recognition task and a social prediction task. PHASE is validated with human experiments demonstrating that humans perceive rich interactions in the social events, and that the simulated agents behave similarly to humans.

Introducing paper: PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception
AGENT

Inspired by cognitive development studies on intuitive psychology, we present a benchmark consisting of a large dataset of procedurally generated 3D animations, AGENT (Action, Goal, Efficiency, coNstraint, uTility), structured around four scenarios (goal preferences, action efficiency, unobserved constraints, and cost-reward trade-offs) that probe key concepts of core intuitive psychology.

Introducing paper: AGENT: A Benchmark for Core Psychological Reasoning
TCIA Brain-Tumor-Progression

This collection includes datasets from 20 subjects with primary newly diagnosed glioblastoma who were treated with surgery and standard concomitant chemo-radiation therapy (CRT) followed by adjuvant chemotherapy.  Two MRI exams are included for each patient: within 90 days following CRT completion and at progression (determined clinically, and based on a combination of clinical performance and/or imaging findings, and punctuated by a change in treatment or intervention). 
All image sets are in DICOM format and contain T1w (pre and post-contrast agent), FLAIR, T2w, ADC, normalized cerebral blood flow, normalized relative cerebral blood volume, standardized relative cerebral blood volume, and binary tumor masks (generated using T1w images).  The perfusion images were generated from dynamic susceptibility contrast (GRE-EPI DSC) imaging following a preload of contrast agent.  All of the series are co-registered with the T1+C images.  The intent of this dataset is for assessing deep learning algorithm performance to predict tumor progression.

### Data Citation

```
Schmainda KM, Prah M (2018). Data from Brain-Tumor-Progression. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.15quzvnb 
```
Ruddit

Ruddit is a dataset of English language Reddit comments that has fine-grained, real-valued scores for offensive language detection between -1 (maximally supportive) and 1 (maximally offensive).

The dataset was annotated using Best--Worst Scaling, a form of comparative annotation that has been shown to alleviate known biases of using rating scales.

Introducing paper: Ruddit: Norms of Offensiveness for English Reddit Comments