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CS Research Dataset

Description

This dataset is a collection of scientific abstracts from the field of computer science, sourced from arXiv. It was created to facilitate research in natural language processing, specifically for tasks such as thematic modeling, trend analysis, and keyword extraction.

Dataset Structure

The dataset is structured as follows:

  • title: Title of the research paper.
  • id: Unique identifier for each abstract.
  • abstract: Abstract of the research paper.
  • categories: Categories associated with the paper, primarily within the field of computer science.
  • doi: Digital Object Identifier for the paper.
  • created: Date when the paper was submitted to arXiv.
  • updated: Date when the paper was last updated.
  • authors: List of authors of the paper.
  • url: URL to the original paper on arXiv.
  • abstract_length: Length of the abstract in characters.
  • id_n: Sequential number assigned to each abstract, starting from 0.

Usage

This dataset can be used for various natural language processing tasks such as thematic modeling, trend analysis, keyword extraction, and more. It is particularly suitable for researchers and practitioners interested in the latest developments in computer science.

How to Load the Dataset

You can load the dataset using the datasets library in Python:

from datasets import load_dataset

dataset = load_dataset("CCRss/arxiv_papers_cs")

Dataset Creation

The dataset was created using the arxivscraper library in Python to scrape abstracts from the arXiv website. Here is an example of how the data was collected:

import arxivscraper

scraper = arxivscraper.Scraper(category='cs', date_from='20200101', date_until='20201231')
output = scraper.scrape()

You can find a detailed example in this Google Colab notebook and test it.

Application

This dataset was used to train a thematic modeling algorithm for analyzing trends in UAV-related research. The trained model is available in our model repository on Hugging Face.

Acknowledgments

We would like to acknowledge Mahdi Sadjadi for creating the arxivscraper library, which was instrumental in collecting data for this dataset. The library is available on Zenodo: arxivscraper (2017).

License

This dataset is provided under the MIT License.