WHAT’S THIS SESSION ABOUT?
Interactive tools such as the Shiny R package enable researchers to design and share user-friendly, reproducible data applications that directly support the development of research data and software competencies (RDSCs). By lowering barriers to accessibility and promoting transparency, Shiny provides a flexible environment where complex data workflows can be operationalized in a way that is both intuitive and reusable.
This Data Snack explores how Shiny can be used to strengthen training and practice in RDSCs by enhancing analytical workflows, supporting reproducibility, and enabling hands-on learning. A simulation-based Shiny application is presented as an example, demonstrating the development and application of competencies in data visualization, statistical modeling, and software reusability.
The session also considers the broader educational and practical implications of embedding such tools into teaching, research, and professional development programs. Emphasis is placed on how interactive applications not only facilitate technical learning but also foster long-term skills in reproducible research, transparent communication, and collaborative data science practice.
WHERE AND WHEN?
The session will take place from 1:00 to 1:30pm via Zoom. There will be a 15-20 minute presentation followed by an open forum for questions and discussion. The slides will be shared afterwards. We look forward to exciting discussions!
Zoom Link: https://uni-bremen.zoom-x.de/j/61666538039?pwd=JZTcc15FcsrZwedVYuMoVuhp3sPLCF.1
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ABOUT THE SPEAKER
Maryam Movahedifar holds a PhD in Statistics and has extensive experience in Interpretable Machine Learning. With a strong foundation in statistical methods and practical experience in applying these techniques to real-world problems, she is well-equipped to teach complex machine learning concepts. Her expertise includes making advanced models understandable and accessible.
