#blog #GESISPanel.dbd #Bias #DigitalBehavioralData
The new post on the #GESISblog highlights the use of and attitudes toward #AI in Germany. Julius Kleinbub, Dr. Joachim Piepenburg, and Katharina Kraft show that most people had heard about ChatGPT, while frequent use was scarce, with mixed attitudes toward AI.

https://blog.gesis.org/digital-technologies-and-sampling-comparing-estimates-from-different-recruitment-approaches-within-the-gesis-panel-dbd/

#DataQuality #DigitalBehavioralData #DBD
Special Issue Explores the Quality of Digital Behavioral Data, by Weiß, B., Leitgöb, H., & Wagner, C

Most DBD are not generated for research purposes but are a side product of our daily activities. A new special issue addresses key questions:

#dataquality #Surveydata #digitalbehavioraldata #linkeddatasources
Official launch of the #KODAQS #Toolbox in July 2025

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).
https://kodaqs-toolbox.gesis.org/

#GESISGuides #DBD #DigitalBehavioralData
Lux, V., & Wieland, M. (2025). How to Set up and Monitor App-based Data Collections (GESIS Guides to Digital Behavioral Data, 22).

https://doi.org/10.60762/ggdbd25022.1.0

This guide addresses that gap by offering practical recommendations to manage the active data collection phase.

#GESISGuides #DBD #DigitalBehavioralData
Fröhling, L., Birkenmaier, L., Lux, V., & Daikeler, J. (2025). How to Find and Explore Data Quality Frameworks for Digital Behavioral Data (GESIS Guides to Digital Behavioral Data, 26).

https://doi.org/10.60762/ggdbd25026.1.0

#GESISGuides #DBD #DigitalBehavioralData
Bleier, A. (2025). What is Computational Reproducibility? (GESIS Guides to Digital Behavioral Data, 2).

https://doi.org/10.60762/ggdbd25002.1.0

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.