This mini-course is designed to equip graduate students and postdocs with essential skills for ensuring reproducibility in computational research. Through practical exercises and interactive sessions, participants will learn best practices, tools, and techniques for doing open and reproducible research. Topics covered include version control, containerization, data management, workflows, and documentation strategies. This course empowers students to overcome challenges associated with reproducibility, fostering rigorous scientific inquiry, and enhancing the credibility and impact of their computational work, while also exploring the primary causes and consequences of irreproducibility in research. Participants will gain valuable insights and practical experience in achieving computational reproducibility across various domains, including biology. Prerequisites: Basic familiarity with programming (e.g., Python, R); Basic knowledge of Unix/Linux Bash
2 units · Medical Satisfactory/No Credit
This mini-course is designed to equip graduate students and postdocs with essential skills for ensuring reproducibility in computational research. Through practical exercises and interactive sessions, participants will learn best practices, tools, and techniques for doing open and reproducible research. Topics covered include version control, containerization, data management, workflows, and documentation strategies. This course empowers students to overcome challenges associated with reproducibility, fostering rigorous scientific inquiry, and enhancing the credibility and impact of their computational work, while also exploring the primary causes and consequences of irreproducibility in research. Participants will gain valuable insights and practical experience in achieving computational reproducibility across various domains, including biology. Prerequisites: Basic familiarity with programming (e.g., Python, R); Basic knowledge of Unix/Linux Bash
Offered in Winter 2027 at Stanford University.