Omkar2118/datasets huggingface dataset:
Ghana house rental dataset

### Dataset Description  
This dataset contains rental property listings scraped from **Tonaton.com**, one of Ghana's leading online classifieds platforms. It provides valuable information on rental prices across various regions in Ghana, along with other property details. The dataset is designed to support analysis, visualization, and modeling of rental prices in the Ghanaian real estate market.

### Features  
The dataset includes the following columns:  
- **url**: The link to the listing.  
- **name**: The headline or title of the rental property listing.  
- **price**: The rental price of the property in Ghanaian Cedis (GHS).  
- **category**: The type of rental property (e.g., apartment, house, room, office).  
- **bedrooms**: The number of bedrooms available in the property.  
- **bathrooms**: The number of bathrooms available in the property.  
- **floor_area**: The floor area of the property in square meters.  
- **location**: The address location where the property is located.  
- **condition**: Condition of the property e.g new, used, off-plan etc.
- **amenities**: Amenities provided in the property.
- **region**: Geographic administrative region of the property location.
- **locality**: Represent the town or city where the property is located.
- **parking_space**: Indicates if there is parking space available.
- **is_furnished**: Indicates if the property is furnished.
- **lat**: Longitude location of the property.
- **lng**: Latitude location of the property.

### Potential Use Cases  

1. **Real Estate Market Analysis**: Analyze rental price trends across different locations and property types in Ghana.  
2. **Price Prediction Models**: Train machine learning models to predict rental prices based on features like location, property type, and number of bedrooms.  
3. **Geographical Insights**: Investigate how location impacts rental prices across urban and rural areas in Ghana.  
4. **Consumer Trends**: Study patterns in rental property preferences, such as the most popular types of properties or regions.  

### Source  
The data was scraped from **Tonaton.com** as of November, 2024. Please note that this dataset reflects the listings available during that period and may not include all rental properties in Ghana.

### Disclaimer  
This dataset is shared for educational and research purposes only. It is not intended for commercial use or to reproduce Tonaton.com’s proprietary information. Users are responsible for ensuring their use complies with Tonaton.com’s terms and conditions.

### Conflict of Interest  
There are no conflicts of interest associated with the creation or use of this dataset.

Introducing paper: Predicting House Rental Prices in Ghana Using Machine Learning
ASRD
Full name: Anime Style Recognition Dataset

A well-labeled challenging dataset, to facilitate the research on style recognition on anime images by collecting images from 190 anime and cartoon works covering 93 years from 13 countries and regions, 2D and 3D work into consideration concurrently. We choose at most ten roles for each work. All the images are obtained from the Internet. The images in the LSASRD dataset are mainly from existing anime and cartoons. Moreover, some are from comics or games of the same anime series. Unlike illustration or video datasets, we provide a moderate amount of contextual information in a wide variety of styles. LSASRD requires the ability of context understanding of image models.

Introducing paper: A Challenging Benchmark of Anime Style Recognition
MaNGA
Full name: Mapping Nearby Galaxies at APO

MaNGA is a component of the Fourth-Generation Sloan Digital Sky Survey whose goal is to map the detailed composition and kinematic structure of nearby galaxies. MaNGA uses integral field unit (IFU) spectroscopy to measure spectra for hundreds of points within each galaxy.  MaNGA’s goal is to understand the “life history” of present-day galaxies from imprinted clues of their birth and assembly, through their ongoing growth via star formation and merging, to their death from quenching at late times.

The primary MaNGA data products are composed of 3-D calibrated data cubes produced by the DRP[89] and 2-D maps of derived quantities, such as emission line fluxes, gas and stellar kinematics, and stellar population properties, produced by the DAP[149] from those cubes. The 3-D data cubes are constructed from a few tens to a few thousands of individual spectra that have been combined onto a regular grid. The 2-D maps of derived quantities are constructed by analyzing individual or binned groups of spaxels and constructing maps of the quantities at the relevant on-sky location.

Introducing paper: Overview of the SDSS-IV MaNGA Survey: Mapping Nearby Galaxies at Apache Point Observatory
CityTopia
Full name: CityTopia

CityTopia is the largest synthetic dataset for 3D cities with annotations, offering high-fidelity scenes generated using 3D assets from the Unreal Engine 5 CitySample project.

Features

- High Fidelity: Created using cutting-edge Unreal Engine 5 assets for realistic urban scenes.
- Diverse Annotations: Includes semantic and instance annotations at 1080p resolution.
- Versatile Renders: Contains day and night scenes in multiple resolutions.
- Customizability: Provides source files for city generation via Houdini, enabling custom dataset creation.

Introducing paper: CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities
tiktok-gaza
Full name: #israel & #palestine on TikTok

This dataset contains data scraped from search results of the query #israel and #palestine during the early months of the 2023 crisis.

Introducing paper: Unveiling Voices: A Co-Hashtag Analysis of TikTok Discourse on the 2023 Israel-Palestine Crisis