Scientific Programming e Reproducible Workflows

This repository contains the complete set of teaching materials for an innovative, hybrid block course on Scientific Programming and Workflow Management, designed for Master's and PhD students in the environmental sciences and related fields.

The central philosophy of this course is "learn-to-learn," addressing the critical gap in traditional curricula where students are often taught what to code but not how to independently acquire and master new technical skills. The curriculum is built upon a project-based, flipped classroom model that combines an intensive in-person leveling week with a supported self-study phase and a final reflective wrap-up week.

A key pedagogical feature is the use of a course-long, gamified narrative ("The Expedition to Isla R-borea") to provide context and motivation for each new concept. Abstract topics are introduced first through kinesthetic "dry-lab" exercises—physical, non-coding role-playing activities that allow students to build an intuitive, tangible understanding of concepts like data structures, version control, and conditional logic before translating them into R code.

The materials cover a complete introductory data analysis workflow, including:

• Fundamental R programming concepts (operators, variables, data types, data structures).

• Core workflow logic (conditionals, loops, functions).

• Essential data management skills (data loading, project organization).

• Collaborative, reproducible workflows using Git, including push/pull, merge conflicts, and pull requests.

• Professional soft skills (project management, giving and receiving feedback).

These materials are shared with the aim of providing other educators with a complete, adaptable framework for teaching foundational programming and data science skills in an engaging, effective, and memorable way.

Keywords:
R Programming, Data Science, Scientific Programming, Pedagogy, Higher Education, Kinesthetic Learning, Gamification, Flipped Classroom, Reproducible Research, Workflow Management, Git, Version Control, Graduate Education, Environmental Science.

Contents of the Upload:
This upload contains the following materials:

• A complete set of PowerPoint slides for all teaching modules (Modules 5-14).

• Printable handouts for all physical "dry-lab" exercises (e.g., Operator Tiles, Mission Briefings, Data Assembly Line Log Sheets).

• The two sample datasets used throughout the course (expedition_sites.csv and sample_data.csv).

• Annotated R scripts for all hands-on coding exercises.

• The final course report, including a summary of the pedagogical approach and student evaluations.