<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>3621</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>17</pageFirst>
    <pageLast>31</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>45</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation>Institute of Electrical and Electronics Engineers (IEEE)</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards Collective Storytelling</title>
    <abstract language="eng">This work investigates personal perspectives in visualization annotations as devices for collective data-driven storytelling. Inspired by existing efforts in critical cartography, we show how people share personal memories in a visualization of COVID-19 data and how comments by other visualization readers influence the reading and understanding of visualizations. Analyzing interaction logs, reader surveys, visualization annotations, and interviews, we find that reader annotations help other viewers relate to other people's stories and reflect on their own experiences. Further, we found that annotations embedded directly into the visualization can serve as social traces guiding through a visualization and help readers contextualize their own stories. With that, they supersede the attention paid to data encodings and become the main focal point of the visualization.</abstract>
    <parentTitle language="eng">IEEE Computer Graphics and Applications</parentTitle>
    <subTitle language="eng">Investigating Audience Annotations in Data Visualizations</subTitle>
    <identifier type="issn">0272-1716</identifier>
    <identifier type="issn">1558-1756</identifier>
    <identifier type="doi">10.1109/MCG.2025.3547944</identifier>
    <enrichment key="opus.import.date">2025-03-26T10:00:03+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">hisres</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Tobias Kauer</author>
    <author>Marian Dörk</author>
    <author>Benjamin Bach</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Annotation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>COVID-19</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Data Mining</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kartografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Soziales Netzwerk</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Visualisierung</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Wissen, Systeme</collection>
    <collection role="institutes" number="">FB4 Design</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="institutes" number="">Urban Complexity Lab (UCLAB)</collection>
    <collection role="open_access_fhp" number="">Hybrid Open Access</collection>
    <thesisPublisher>Fachhochschule Potsdam</thesisPublisher>
  </doc>
</export-example>
