December 9, 2020 Dataset Open Access

Saier, Tarek; Färber, Michael

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nmm##2200000uu#4500</leader>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">scholarly data</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">citations</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">papers</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">arXiv.org</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">digital libraries</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">dataset</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">scientometrics</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">full-text</subfield>
  </datafield>
  <controlfield tag="005">20211110155723.0</controlfield>
  <controlfield tag="001">4313164</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">University of Freiburg</subfield>
    <subfield code="0">(orcid)0000-0001-5458-8645</subfield>
    <subfield code="a">Färber, Michael</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">19120394657</subfield>
    <subfield code="z">md5:c66da9551fc3e3b7374d55726003552f</subfield>
    <subfield code="u">https://zenodo.org/record/4313164/files/unarXive-2020.tar.bz2</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-12-09</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire_data</subfield>
    <subfield code="p">user-natural-language-processing</subfield>
    <subfield code="p">user-scholarly-data</subfield>
    <subfield code="p">user-bibliometrics</subfield>
    <subfield code="o">oai:zenodo.org:4313164</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">University of Freiburg</subfield>
    <subfield code="0">(orcid)0000-0001-5028-0109</subfield>
    <subfield code="a">Saier, Tarek</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-bibliometrics</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-natural-language-processing</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-scholarly-data</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="a">Other (Attribution)</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&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;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">url</subfield>
    <subfield code="i">isDocumentedBy</subfield>
    <subfield code="a">https://link.springer.com/article/10.1007%2Fs11192-020-03382-z</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.2553522</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.4313164</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">dataset</subfield>
  </datafield>
</record>