December 9, 2020 Dataset Open Access

Saier, Tarek; Färber, Michael

DataCite XML Export

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  <identifier identifierType="DOI">10.5281/zenodo.4313164</identifier>
  <creators>
    <creator>
      <creatorName>Saier, Tarek</creatorName>
      <givenName>Tarek</givenName>
      <familyName>Saier</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5028-0109</nameIdentifier>
      <affiliation>University of Freiburg</affiliation>
    </creator>
    <creator>
      <creatorName>Färber, Michael</creatorName>
      <givenName>Michael</givenName>
      <familyName>Färber</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5458-8645</nameIdentifier>
      <affiliation>University of Freiburg</affiliation>
    </creator>
  </creators>
  <titles>
    <title>unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>scholarly data</subject>
    <subject>citations</subject>
    <subject>papers</subject>
    <subject>arXiv.org</subject>
    <subject>digital libraries</subject>
    <subject>dataset</subject>
    <subject>scientometrics</subject>
    <subject>full-text</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-12-09</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4313164</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsDocumentedBy" resourceTypeGeneral="JournalArticle">https://link.springer.com/article/10.1007%2Fs11192-020-03382-z</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2553522</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/bibliometrics</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/natural-language-processing</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/scholarly-data</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;Here, we propose a new &lt;strong&gt;data set based on all publications from all scientific disciplines available on arXiv.org&lt;/strong&gt;. Apart from providing the &lt;strong&gt;papers&amp;#39; plain text&lt;/strong&gt;, &lt;strong&gt;in-text citations were annotated&lt;/strong&gt; via global identifiers. Furthermore, citing and cited publications were linked to the &lt;strong&gt;Microsoft Academic Graph&lt;/strong&gt;, providing access to rich metadata. Our data set consists of &lt;strong&gt;over one million documents and 29.2 million citation contexts&lt;/strong&gt;. 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.&lt;/p&gt;

&lt;p&gt;This &lt;strong&gt;updated version&lt;/strong&gt; (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.&lt;/p&gt;

&lt;p&gt;See &lt;a href="https://github.com/IllDepence/unarXive"&gt;https://github.com/IllDepence/unarXive&lt;/a&gt; for the &lt;strong&gt;source code&lt;/strong&gt; which has been used for creating the data set.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Usage examples&lt;/strong&gt; for our data set are provided at &lt;a href="https://github.com/IllDepence/unarXive#usage-examples"&gt;https://github.com/IllDepence/unarXive#usage-examples&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For &lt;strong&gt;citing&lt;/strong&gt; our data set and for further information we can refer to our journal article&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Tarek Saier, Michael F&amp;auml;rber: &amp;quot;&lt;a href="https://www.aifb.kit.edu/images/f/f9/UnarXive_Scientometrics2020.pdf"&gt;unarXive: A Large Scholarly Data Set with Publications&amp;rsquo; Full-Text, Annotated In-Text Citations, and Links to Metadata&lt;/a&gt;&amp;quot;, Scientometrics, 2020, &lt;a href="http://dx.doi.org/10.1007/s11192-020-03382-z"&gt;http://dx.doi.org/10.1007/s11192-020-03382-z&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
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