# ArXiv Dataset
## Overview
This dataset is a comprehensive collection of metadata from the ArXiv repository, a widely-recognized open-access archive offering access to scholarly articles in various fields of science. It covers a broad range of subjects from physics and computer science to mathematics, statistics, electrical engineering, quantitative biology, and economics.
The dataset hosted here is derived from the original ArXiv dataset available on Kaggle, which includes metadata for approximately 1.7 million articles. The metadata encompasses various features such as article titles, authors, categories, abstracts, and full text in PDF format.
This rich repository of scholarly articles provides a valuable resource for data analysis, trend identification, and development of machine learning models. It can facilitate applications like trend analysis, paper recommendation systems, category prediction, co-citation network analysis, knowledge graph construction, and semantic search in…
