Choosing the Right Modeling Approach
Evaluating a Bayesian alternative showed that a more complex model is not always necessary when a simpler approach directly addresses the research question.
Research Notes
Research notes documenting methodological decisions, analytical reasoning, and lessons learned throughout my research.
Managing Git Commit Scope
A larger research project showed the importance of keeping Git commits focused and maintaining a clear version history.
Adapting the Repository Structure
A larger research project showed that repository structure should adapt to the size and complexity of the analysis.
Following the Analysis Plan
A final review of the working memory analysis showed the importance of maintaining a detailed analysis plan and carefully verifying each step before publication.
From Practice to Real Data
Building hierarchical Bayesian models with real data revealed computational and methodological challenges that did not appear during training.
Learning the Publication Process
Submitting a first preprint provided practical experience with editorial screening and the realities of scientific publishing.
Understanding Bayesian Thinking
Learning the foundations of Bayesian inference highlighted for me the role of evidence and uncertainty in statistical reasoning.
Managing Python Environment Conflicts
Resolving package conflicts highlighted the importance of stable and reproducible research environments.
Preparing The Preprint
Preparing the first manuscript required organizing analyses into a coherent and publication-ready scientific narrative.
Open Data Challenge
Open dataset required major preprocessing due to structural issues before any analysis could begin.