Omkar2118/datasets huggingface dataset:
Gait Dataset
Full name: Human Gait Dataset

Details about the creation of the dataset can be seen in https://arxiv.org/abs/2110.06139. 

```
@misc{sa2021classification,
      title={Classification of anomalous gait using Machine Learning techniques and embedded sensors}, 
      author={T. R. D. Sa and C. M. S. Figueiredo},
      year={2021},
      eprint={2110.06139},
      archivePrefix={arXiv},
      primaryClass={eess.SP}
}
```

The dataset can be download through the link posted in github repository.

Introducing paper: Classification of anomalous gait using Machine Learning techniques and embedded sensors
STR-2021

The **STR-2021** dataset has 5,500 English sentence pairs manually annotated for semantic relatedness using a comparative annotation framework.

Introducing paper: What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study
MUNO21

**MUNO21** is a large-scale and comprehensive dataset for the map update task. It includes time series of aerial images and map data to capture the evolution of both the physical road network and real street maps over time -- we collect NAIP aerial images at each of four years over the eight-year timespan from 2012–2019, and OSM extracts from each year during the same timespan.

Introducing paper: Beyond Road Extraction: A Dataset for Map Update using Aerial Images
QMAR
Full name: Quality of Movement Assessment for Rehabilitation

QMAR is an RGB multi-view Quality of Human Movement Assessment  dataset.

QMAR was recorded using 6 Primesense cameras (3 different frontal views and 3 different side views) with 38 healthy subjects, 8 female and 30 male. The subjects were trained by a physiotherapist to perform two different types of movements while simulating three ailments, resulting in five overall possibilities: a return Walk to approximately the original position while simulating Parkinsons (W-P), Stroke (W-S) and Limp (W-L), and Standing up and Sitting down with Parkinson (SS-P) and Stroke (SS-S).

The dataset includes RGB (and depth and skeleton) data, although in the current version contains only the RGB data. Depth/skeleton data can be obtinaed on request, however note they are available for two views only.

All documents and papers that use the QMAR dataset, or any derived part of the dataset, should cite the following paper: F. Sardari, A. Paiement, S. Hannuna, M. Mirmehdi; VI-Net: View-Invariant Quality of Human Movement Assessment, Sensors, 2020, 20, 5258.
Real Life Violence Situations Dataset

This dataset has the following citation: M. Soliman, M. Kamal, M. Nashed, Y. Mostafa, B. Chawky, D. Khattab, “ Violence Recognition from Videos using Deep Learning Techniques”, Proc. 9th International Conference on Intelligent Computing and Information Systems (ICICIS'19), Cairo, pp. 79-84, 2019. please use it in case of using the dataset in research or engineering purpose )
when we start our Graduation Project Violence Recognition from Videos we found that there is shortage in available datasets related to violence between individuals so we decide to create new big dataset with variety of scenes

Content
Our Dataset Contains 1000 Violence and 1000 non-violence videos collected from youtube videos, violence videos in our dataset contain many real street fights situations in several environments and conditions.
also non-violence videos from our dataset are collected from many different human actions like sports, eating, walking …etc.
ImageNet 50 samples per class

This ImageNet version contains only 50 training images per class while the original testing set remains unchanged. It is one of the datasets comprising the data-efficient image classification (DEIC) benchmark.
It was proposed to challenge the generalization capabilities of modern image classifiers.

Introducing paper: Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification
EigenWorms

Caenorhabditis elegans is a roundworm commonly used as a model organism in the study of genetics. The movement of these worms is known to be a useful indicator for understanding behavioural genetics. Brown {\em et al.}[1] describe a system for recording the motion of worms on an agar plate and measuring a range of human-defined features[2]. It has been shown that the space of shapes Caenorhabditis elegans adopts on an agar plate can be represented by combinations of six base shapes, or eigenworms. Once the worm outline is extracted, each frame of worm motion can be captured by six scalars representing the amplitudes along each dimension when the shape is projected onto the six eigenworms. Using data collected for the work described in[1], we address the problem of classifying individual worms as wild-type or mutant based on the time series. The data were extracted from the C. elegans behavioural database [3]. We have 259 cases, which we split 131 train and 128 test. We have truncated each series to the shortest usable. Each series has 17984 observations. Each worm is classified as either wild-type (the N2 reference strain) or one of four mutant types: goa-1; unc-1; unc-38 and unc-63. [1] A. Brown, E. Yemini, L. Grundy, T. Jucikas, and W. Schafer, A dictionary of behavioral motifs reveals clusters of genes affecting caenorhabditis elegans locomotion, Proceedings of the National Academy of Sciences of the United States of America (PNAS), vol. 10, no. 2, pp. 791 796, 2013. [2] E. Yemini, T. Jucikas, L. Grundy, A. Brown, and W. Schafer,  A database of caenorhabditis elegans behavioral phenotypes, Nature Methods, vol. 10, pp. 877 879, 2013. [3] C. elegans behavioural database

Introducing paper: The UEA multivariate time series classification archive, 2018