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#digitalbehavioraldata

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GESIS<p><a href="https://sciences.social/tags/dataquality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataquality</span></a> <a href="https://sciences.social/tags/Surveydata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Surveydata</span></a> <a href="https://sciences.social/tags/digitalbehavioraldata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>digitalbehavioraldata</span></a> <a href="https://sciences.social/tags/linkeddatasources" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linkeddatasources</span></a><br>Official launch of the <a href="https://sciences.social/tags/KODAQS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>KODAQS</span></a> <a href="https://sciences.social/tags/Toolbox" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Toolbox</span></a> in July 2025 </p><p>The KODAQS Toolbox is a new, open platform for assessing and improving data quality in the social sciences. It supports researchers in systematically reflecting on the quality of their data - along three central data types: Survey data, digital behavioral data (e.g. app or sensor data) and linked data sources (e.g. register and geospatial data).<br><a href="https://kodaqs-toolbox.gesis.org/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">kodaqs-toolbox.gesis.org/</span><span class="invisible"></span></a></p>
GESIS<p><a href="https://sciences.social/tags/GESISGuides" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GESISGuides</span></a> <a href="https://sciences.social/tags/DBD" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DBD</span></a> <a href="https://sciences.social/tags/DigitalBehavioralData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DigitalBehavioralData</span></a><br>Lux, V., &amp; Wieland, M. (2025). How to Set up and Monitor App-based Data Collections (GESIS Guides to Digital Behavioral Data, 22). </p><p><a href="https://doi.org/10.60762/ggdbd25022.1.0" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.60762/ggdbd25022.1.</span><span class="invisible">0</span></a></p><p>This guide addresses that gap by offering practical recommendations to manage the active data collection phase.</p>
GESIS<p><a href="https://sciences.social/tags/GESISGuides" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GESISGuides</span></a> <a href="https://sciences.social/tags/DBD" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DBD</span></a> <a href="https://sciences.social/tags/DigitalBehavioralData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DigitalBehavioralData</span></a><br>Fröhling, L., Birkenmaier, L., Lux, V., &amp; Daikeler, J. (2025). How to Find and Explore Data Quality Frameworks for Digital Behavioral Data (GESIS Guides to Digital Behavioral Data, 26).</p><p><a href="https://doi.org/10.60762/ggdbd25026.1.0" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.60762/ggdbd25026.1.</span><span class="invisible">0</span></a></p>
GESIS<p><a href="https://sciences.social/tags/GESISGuides" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GESISGuides</span></a> <a href="https://sciences.social/tags/DBD" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DBD</span></a> <a href="https://sciences.social/tags/DigitalBehavioralData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DigitalBehavioralData</span></a><br>Bleier, A. (2025). What is Computational Reproducibility? (GESIS Guides to Digital Behavioral Data, 2). </p><p><a href="https://doi.org/10.60762/ggdbd25002.1.0" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.60762/ggdbd25002.1.</span><span class="invisible">0</span></a></p><p>This guide is intended to serve as a starting point for CSS researchers who want to increase the reproducibility of their work and, by doing so, contribute to a culture of openness, accountability, and cumulative scientific progress.</p>
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