January 20, 2022 Dataset Open Access

Maaß, Christina H.

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  <identifier identifierType="DOI">10.25592/uhhfdm.9833</identifier>
  <creators>
    <creator>
      <creatorName>Maaß, Christina H.</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6264-6622</nameIdentifier>
      <affiliation>Universität Hamburg</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Shedding light on dark figures: steps towards a methodology for estimating actual numbers of COVID-19 infections in Germany based on Google Trends</title>
  </titles>
  <publisher>Universität Hamburg</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Big Data, Google Trends</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-01-20</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://www.fdr.uni-hamburg.de/record/9833</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPartOf">10.25592/uhhfdm.9831</relatedIdentifier>
  </relatedIdentifiers>
  <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;Data used for research project: Shedding light on dark figures: Steps towards a methodology for estimating actual numbers of COVID-19 infections in Germany based on Google Trends&lt;/p&gt;

&lt;p&gt;Data obtained from Google Trends (trends.google.de) and Robert Koch Institute (https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/nCoV.html).&lt;/p&gt;</description>
  </descriptions>
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