0.1. RCR in the context of biomedical data science.
0.2. An overview of rigor and reproducibility considerations in biomedical research that employs data science and AI methods.
0.3. The complexity of biomedical data and the need for insight integration.
1.1. What are ethics?
1.2. Informed Consent.
1.3. Privacy.
1.4. Fairness.
2.1. Data management.
2.2. Data representation.
2.3. Metadata.
2.4. Data sharing.
3.1. An introduction to fundamental concepts.
3.2. Case studies representative of modern biomedical studies.
3.3. Summary of the fundamental concepts and their implementation in diverse settings.
4.1. A review of predictive modeling modeling.
4.2. Data preparation. Data cleaning, distributional checks, dimension reduction and their underlying assumptions, and consequences for downstream inference.
4.3. Modeling tools.
4.4. Assessment of bias and fairness within predictive models.
4.5. How to report research with predictive models.
4.6. How to read an ML paper.
5.1. Goals of Reproducible Analyses: reproducible, user friendly, transparent, reusable, version controlled, permanently archived.
5.2. Reproducibility via Code Notebooks.
5.3. Best practices for Reproducible Programming.
5.4. Version Control.
5.5. Containers.
5.6. Putting Everything Together.
6.1. Key concepts in research synthesis.
6.2. Basic adjustment for heterogeneity and miscalibration.
6.3. Assessment of study results heterogeneity.
6.4. Multiple testing and causality.
7.1. Theoretical Foundations.
7.2. Transformers in Biomedical Research.
7.3. Data Management for Transformers.
7.4. Setting Up the Environment.
7.5. Model Training and Fine-Tuning.
7.6. Data Sharing with Transformers.
7.7. Data Representation and Result Interpretation.