research data . Dataset . 2019

Bibliometric-Enhanced arXiv: A Data Set for Paper-Based and Citation-Based Tasks

Saier, Tarek; Färber, Michael;
Open Access
  • Published: 01 Feb 2019
  • Publisher: Zenodo
Abstract
We propose a new data set based on all publications from all scientific fields available on arXiv.org. Apart from providing the papers' plain text, in-text citations were annotated via global identifiers. As far as possible, cited publications were linked to the Microsoft Academic Graph. Our data set consists of over one million documents and 29.2 million citation contexts. The data set, which is made freely available for research purposes, not only can enhance the future evaluation of researchpaper-based and citation context-based approaches but also serve as a basis for novel ideas to analyze papers. More information can be found in our paper Bibliometric-Enhanced arXiv: A Data Set for Paper-Based and Citation-Based Tasks. See https://github.com/IllDepence/unarXive for the source code which has been used for creating the data set.
Subjects
ACM Computing Classification System: GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
free text keywords: scholarly data, citations, papers, arXiv.org, digital libraries, dataset, scholarly data, citations, arXiv.org, digital libraries, dataset
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Open Access
ZENODO
Dataset . 2019
Providers: ZENODO
Open Access
ZENODO
Dataset . 2019
Providers: ZENODO
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