Omkar2118/datasets huggingface dataset:
KU-HAR

Human Activity Recognition (HAR) refers to the capacity of machines to perceive human actions. This dataset contains information on 18 different activities collected from 90 participants (75 male and 15 female) using smartphone sensors (Accelerometer and Gyroscope). It has 1945 raw activity samples collected directly from the participants, and 20750 subsamples extracted from them. The activities are:

Stand➞ Standing still (1 min)
Sit➞ Sitting still (1 min) 
Talk-sit➞ Talking with hand movements while sitting (1 min) 
Talk-stand➞ Talking with hand movements while standing or walking(1 min) 
Stand-sit➞ Repeatedly standing up and sitting down (5 times)
Lay➞ Laying still (1 min) 
Lay-stand➞ Repeatedly standing up and laying down (5 times)
Pick➞ Picking up an object from the floor (10 times)
Jump➞ Jumping repeatedly (10 times)
Push-up➞ Performing full push-ups (5 times)
Sit-up➞ Performing sit-ups (5 times)
Walk➞ Walking 20 meters (≈12 s)
Walk-backward➞ Walking backward for 20 meters (≈20 s)
Walk-circle➞ Walking along a circular path (≈ 20 s)
Run➞ Running 20 meters (≈7 s)
Stair-up➞ Ascending on a set of stairs (≈1 min)
Stair-down➞ Descending from a set of stairs (≈50 s)
Table-tennis➞ Playing table tennis (1 min)

Introducing paper: KU-HAR: An open dataset for heterogeneous human activity recognition
Wearanize+ Dataset (v1.0)

Wearanize+ includes overnight sleep data from 130 participants (one night each) using three different wearable devices: Zmax headband, Empatica E4 wristband, and ActivPAL leg patch, alongside full-scale PSG recorded with SomnoScreen Plus and Mentalab Explore Pro. It also includes questionnaires, such as PSQI, MADRE, and PHQ-9, providing information on participants’ sleep, dreams, and overall health.
(The link to access the dataset will be added soon).

Introducing paper: Wearanize+: A Multimodal Dataset for Evaluating Wearable Technologies in Sleep Research
Multi Lingual Bug Reports

## Dataset Description

The dataset used in this study comprises bug reports extracted from the Visual Studio Code GitHub repository, specifically focusing on those labeled with the english-please tag. This label indicates that the original submission was written in a language other than English, providing a clear signal for multilingual content. The dataset spans a five-year period (March 2019--June 2024), ensuring a diverse representation of bug types, user environments, and technical contexts.

### Characteristics
The dataset contains **1,381** multilingual bug reports, each consisting of:
- The **original bug report** written in a non-English language.
- A **translated version** in English.
- Metadata such as issue number, creation date, labels, and status.
- Categorization into **functional, UI, and performance-related** issues based on the content.

### Motivation & Summary
This dataset is motivated by the need to **improve multilingual bug tracking and translation evaluation**. Given the increasing globalization of software development, developers and QA teams frequently encounter bug reports in languages they do not understand. By providing a structured corpus of translated bug reports, this dataset facilitates:
- **Comparative translation evaluation** (e.g., ChatGPT vs AWS Translate vs DeepL).
- **Linguistic analysis** of technical bug reporting across different languages.
- **Insights into common software issues** encountered by diverse users.
- **Improving multilingual issue tracking** through automated labeling and categorization.

### Potential Use Cases
This dataset can be beneficial for various research and development applications, including:
- **Machine Translation Benchmarking**: Evaluating the performance of translation models in a technical domain.
- **Natural Language Processing (NLP) Tasks**: Training classifiers to categorize bug reports based on their content.
- **Software Engineering Research**: Understanding trends in bug reporting, issue resolution, and localization challenges.
- **Automated Bug Triage**: Developing AI-driven solutions for assigning and prioritizing bug reports in multilingual repositories.

Introducing paper: English Please: Evaluating Machine Translation with Large Language Models for Multilingual Bug Reports