Dataset Open Access

Aurorasaurus Real-Time Citizen Science Aurora Data

Kosar, Burcu C.; MacDonald, Elizabeth A.; Case, Nathan A.; Heavner, Matthew

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  <identifier identifierType="DOI">10.5281/zenodo.1255196</identifier>
      <creatorName>Kosar, Burcu C.</creatorName>
      <givenName>Burcu C.</givenName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0001-6877-6364</nameIdentifier>
      <affiliation>The Catholic University of America, NASA Goddard Space Flight Center, and New Mexico Consortium</affiliation>
      <creatorName>MacDonald, Elizabeth A.</creatorName>
      <givenName>Elizabeth A.</givenName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-3946-297X</nameIdentifier>
      <affiliation>NASA Goddard Space Flight Center, NASA HQ, and New Mexico Consortium</affiliation>
      <creatorName>Case, Nathan A.</creatorName>
      <givenName>Nathan A.</givenName>
      <affiliation>Lancaster University</affiliation>
      <creatorName>Heavner, Matthew</creatorName>
      <affiliation>Los Alamos National Laboratory and New Mexico Consortium</affiliation>
    <title>Aurorasaurus Real-Time Citizen Science Aurora Data</title>
    <subject>citizen science</subject>
    <subject>space weather</subject>
    <subject>northern lights</subject>
    <date dateType="Issued">2018-05-29</date>
  <resourceType resourceTypeGeneral="Dataset"/>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1255195</relatedIdentifier>
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    <rights rightsURI="">Creative Commons Attribution Non Commercial 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Aurorasaurus citizen science data is a collection of auroral sightings submitted to the project via its website ( or apps and mined from social media. It is a robust data set and particularly abundant during strong geomagnetic storms. This data is offered to the scientific community for research use through an open-access database in its raw and scientific formats for the 2015-2016 period, each of which is described in detail in the following technical report:&lt;/p&gt;

&lt;p&gt;Kosar, B. C., MacDonald, E. A., Case, N. A., &amp;amp; Heavner, M. (2018). Aurorasaurus Database of Real‐Time, Crowd‐Sourced Aurora Data for Space Weather Research.&amp;nbsp;&lt;em&gt;Earth and Space Science&lt;/em&gt;,&amp;nbsp;&lt;em&gt;5&lt;/em&gt;(12), 970-980.&lt;/p&gt;

&lt;p&gt;For more information on the project, please contact the project leaders at;/p&gt;

    <description descriptionType="Other">Research is funded by National Science Foundation (NSF) - 1344296 and NASA
INSPIRE Track 1: Aurorasaurus - Citizen Scientists Experiencing Extremes of Space Weather
We acknowledge STFC grant number ST/M001059/1
The Aurorasaurus website ( is hosted by the New Mexico Consortium.
Special permission for Twitter use has been obtained.</description>
    <description descriptionType="Other">{"references": ["MacDonald et al., (2015), Aurorasaurus: A citizen science platform for viewing and reporting the aurora, doi:10.1002/2015SW001214", "Case et al. (2015a), Mapping auroral activity with Twitter, doi:10.1002/2015GL063709", "Case et al. (2015b), Aurorasaurus and the St Patrick's Day storm, doi:10.1093/astrogeo/atv089", "Case et al. (2016a), Using citizen science reports to define the equatorial extent of auroral visibility, doi:10.1002/2015SW001320", "Case et al. (2016b), A real\u2010time hybrid aurora alert system: Combining citizen science reports with an auroral oval model, doi:10.1002/2016EA000167", "Case et al. (2016c), Determining the Accuracy of Crowdsourced Tweet Verification for Auroral Research, doi:10.5334/cstp.52", "MacDonald et al., (2018), New science in plain sight: Citizen scientists lead to the discovery of optical structure in the upper atmosphere, doi:10.1126/sciadv.aaq0030", "Kosar et al., (2018), A case study comparing citizen science aurora data with global auroral boundaries derived from satellite imagery and empirical models, doi:10.1016/j.jastp.2018.05.006"]}</description>
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