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Dataset Open Access

DOIBoost Dataset Dump

La Bruzzo, Sandro; Manghi, Paolo; Mannocci, Andrea


DataCite XML Export

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<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.1438356</identifier>
  <creators>
    <creator>
      <creatorName>La Bruzzo, Sandro</creatorName>
      <givenName>Sandro</givenName>
      <familyName>La Bruzzo</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2855-1245</nameIdentifier>
      <affiliation>Institute of Information Science and Technology - CNR</affiliation>
    </creator>
    <creator>
      <creatorName>Manghi, Paolo</creatorName>
      <givenName>Paolo</givenName>
      <familyName>Manghi</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7291-3210</nameIdentifier>
      <affiliation>Institute of Information Science and Technology - CNR</affiliation>
    </creator>
    <creator>
      <creatorName>Mannocci, Andrea</creatorName>
      <givenName>Andrea</givenName>
      <familyName>Mannocci</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5193-7851</nameIdentifier>
      <affiliation>Knowledge Media Institute - Open University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>DOIBoost Dataset Dump</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>dataset</subject>
    <subject>CrossRef</subject>
    <subject>Microsoft Academic Graph</subject>
    <subject>Unpaywall</subject>
    <subject>Spark</subject>
    <subject>aggregation</subject>
    <subject>metadata</subject>
    <subject>enrichment</subject>
    <subject>ORCID</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-09-28</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1438356</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="Compiles">10.5281/zenodo.1441058</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementTo">10.5281/zenodo.1441072</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1438355</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/openaire</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/openaire-research-graph</relatedIdentifier>
  </relatedIdentifiers>
  <version>2.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Research in information science and scholarly communication strongly relies on the availability of openly accessible datasets of metadata and, where possible, their relative payloads. To this end, CrossRef plays a pivotal role by providing free access to its entire metadata collection, and allowing other initiatives to link and enrich its information. Therefore, a number of key pieces of information result scattered across diverse datasets and resources freely available online. As a result of this fragmentation, researchers in this domain end up struggling with daily integration problems producing a plethora of ad-hoc datasets, therefore incurring in a waste of time, resources, and infringing open science best practices.&amp;nbsp;DOIBoost is&amp;nbsp;a metadata collection that enriches CrossRef with inputs from Microsoft Academic Graph, ORCID, and Unpaywall for the purpose of supporting high-quality and robust research experiments, saving times to researchers and enabling their comparison.&lt;/p&gt;

&lt;p&gt;This entry consists of two files: &lt;strong&gt;doiBoost.tar.gz&lt;/strong&gt; (which contains a set of part.gz files, each one containing the JSON files realtive to the enriched CrossRef records) and &lt;strong&gt;termsOfUse.doc&amp;nbsp;&lt;/strong&gt;(which contains details on the terms of use of DOIBoost).&lt;/p&gt;

&lt;p&gt;Note that this records comes with two relationships to other results of this experiment:&amp;nbsp;&lt;/p&gt;

&lt;ol&gt;
	&lt;li&gt;link to the data paper: for more information on how the dataset is (and can be) generated;&lt;/li&gt;
	&lt;li&gt;link to the software: to repeat the experiment&amp;nbsp; .&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/777541/">777541</awardNumber>
      <awardTitle>OpenAIRE Advancing Open Scholarship</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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