Omkar2118/datasets huggingface dataset:
MapReader Data
Full name: in GeoHumanities workshop, SIGSPATIAL 2022

MapReader in GeoHumanities workshop (SIGSPATIAL 2022): Gold standards and outputs

Refer to: 
https://github.com/Living-with-machines/MapReader/wiki/GeoHumanities-workshop-in-SIGSPATIAL-2022

Introducing paper: MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale
PosterLayout

PKU PosterLayout, which consists of 9,974 poster-layout
pairs and 905 images, i.e., non-empty canvases. Each layout is represented by a set of elements labeled with class
and bounding box. We collect data from multiple sources to
guarantee diversity and variety in content, domain, and layout, supporting it as a challenging benchmark expected to
encourage further research. Besides the dataset, we propose
and clearly define new metrics to accompany the old ones,
a total of eight graphic and content-aware metrics. They
evaluate the layouts in terms of utilization, non-occlusion,
and aesthetics. Both quantitative results and visualized results show that the proposed approach outperforms other approaches by generating proper layouts on diverse canvases.

Introducing paper: PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout
SharePriceIncrease
Full name: SharePriceIncrease

The problem here is to predict whether a share price will show an exceptional rise after quarterly announcement of the Earning Per Share based on the price movement of that share price on the proceeding 60 days? The data was formatted by Vlad Pazenuks as part of his third year project. Daily price data on NASDAQ 100 companies was extracted from a Kaggle data set. Reporting dates of these companies were obtained from NASDAQ.com. Each data is the percentage change of the close price from the day before. Each case is a series of 60 day data. The target class is is defined as 0 = price did not increase after company report release by more than 5 percent 1 = price increased after company report release by more than 5 percent There are 1931 cases, 1326 class 0 and 605 class 1.
Urdu News Headlines Dataset

Urdu News Headlines Dataset with VOA and BBC
An Urdu news headlines dataset is a collection of news headlines in the Urdu language, typically scraped from news websites and social media platforms. These datasets can be valuable for researchers and developers working on a variety of tasks, such as:

Machine translation: Training machine translation models to translate between Urdu and other languages.
Text summarization: Developing algorithms for automatically summarizing Urdu news articles.
Natural language processing: Studying the structure and grammar of the Urdu language.
Clustering: Grouping news articles into similar categories based on their headlines.
Benefits of using a dataset with VOA and BBC headlines
There are several benefits to using a dataset that includes news headlines from VOA and BBC:

High quality: VOA and BBC are well-respected news organizations known for their high-quality journalism. This means that the headlines in the dataset are likely to be accurate and unbiased.
Diversity of topics: VOA and BBC cover a wide range of topics, including politics, business, sports, and entertainment. This means that the dataset will be representative of the different types of news that are available in the Urdu language.
Large size: VOA and BBC have been publishing news in Urdu for many years. This means that there is a large amount of data available, which can be used to train machine learning models and to conduct research.
Here are some examples of how a Urdu news headlines dataset with VOA and BBC headlines can be used:

A machine translation model could be trained on the dataset to translate Urdu news headlines into English. This could be used to make Urdu news more accessible to a wider audience.
A text summarization algorithm could be trained on the dataset to automatically generate summaries of Urdu news articles. This could be used to save people time and to help them to stay informed.
A clustering algorithm could be used to group Urdu news articles into similar categories based on their headlines. This could be used to create personalized news feeds for users.
Here are some limitations of using a Urdu news headlines dataset with VOA and BBC headlines:

Limited scope: VOA and BBC are primarily English-language news organizations. This means that the dataset may not be representative of the full range of Urdu news that is available.
Bias: VOA and BBC are both Western news organizations. This means that the dataset may be biased towards Western perspectives.
Limited access: The data may not be readily available to everyone.

Introducing paper: Clustering Urdu News Using Headlines