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unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

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Listed:
  • Tarek Saier

    (Karlsruhe Institute of Technology (KIT))

  • Michael Färber

    (Karlsruhe Institute of Technology (KIT))

Abstract

In recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. In this paper, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers’ plain text, in-text citations were annotated via global identifiers. Furthermore, citing and cited publications were linked to the Microsoft Academic Graph, providing access to rich metadata. 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 research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article.

Suggested Citation

  • Tarek Saier & Michael Färber, 2020. "unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3085-3108, December.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03382-z
    DOI: 10.1007/s11192-020-03382-z
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    References listed on IDEAS

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    1. Reingewertz, Yaniv & Lutmar, Carmela, 2018. "Academic in-group bias: An empirical examination of the link between author and journal affiliation," Journal of Informetrics, Elsevier, vol. 12(1), pages 74-86.
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    Cited by:

    1. Moreno La Quatra & Luca Cagliero & Elena Baralis, 2021. "Leveraging full-text article exploration for citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8275-8293, October.

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