December 9, 2020 Dataset Open Access

Saier, Tarek; Färber, Michael

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  <dc:creator>Saier, Tarek</dc:creator>
  <dc:creator>Färber, Michael</dc:creator>
  <dc:date>2020-12-09</dc:date>
  <dc:description>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.

Here, 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.

This updated version (v3) of our data set is based on all arXiv publications until 2020-07-31 and on the Microsoft Academic Graph as of 2020-08-18. As additional contribution, we included a table with the publication date and the scientific discipline for each paper for easier filtering.

See https://github.com/IllDepence/unarXive for the source code which has been used for creating the data set.

Usage examples for our data set are provided at https://github.com/IllDepence/unarXive#usage-examples.

For citing our data set and for further information we can refer to our journal article

Tarek Saier, Michael Färber: "unarXive: A Large Scholarly Data Set with Publications’ Full-Text, Annotated In-Text Citations, and Links to Metadata", Scientometrics, 2020, http://dx.doi.org/10.1007/s11192-020-03382-z.

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  <dc:identifier>https://zenodo.org/record/4313164</dc:identifier>
  <dc:identifier>10.5281/zenodo.4313164</dc:identifier>
  <dc:identifier>oai:zenodo.org:4313164</dc:identifier>
  <dc:relation>url:https://link.springer.com/article/10.1007%2Fs11192-020-03382-z</dc:relation>
  <dc:relation>doi:10.5281/zenodo.2553522</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/bibliometrics</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/natural-language-processing</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/scholarly-data</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:subject>scholarly data</dc:subject>
  <dc:subject>citations</dc:subject>
  <dc:subject>papers</dc:subject>
  <dc:subject>arXiv.org</dc:subject>
  <dc:subject>digital libraries</dc:subject>
  <dc:subject>dataset</dc:subject>
  <dc:subject>scientometrics</dc:subject>
  <dc:subject>full-text</dc:subject>
  <dc:title>unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>