Rigor and Reproducibility

All MD-PhD students are required to complete formal training in Rigor and Reproducibility during their PhD phase to ensure that their research practices meet the highest standards for experimental design, data integrity, and reproducibility.

Format This course follows an interactive format, with a combination of lectures, case studies, and faculty-led discussions. Students engage actively with course material and complete brief assessments after each session. Attendance is mandatory.

Subject Matter Training is organized into three major areas:

• Rigor and Reproducibility at the Bench: Topics include scientific premise, experimental design, use of controls, hypothesis testing, biological vs. technical replicates, and complementary research approaches.

• Human and Animal Experimental Design and Statistical Analysis: Topics include Institutional Animal Care and Use Committee (IACUC) and Institutional Review Board (IRB) standards, power calculations, variables such as sex and genetics, clinical trial design, and proper statistical testing and reporting.

• Digital and Quantitative Data Management: Topics include best practices for data storage and backup, big data challenges, use of public databases, metadata management, digital image integrity, and strategies to promote reproducibility by others.

Faculty Participation The course is team-taught by faculty with expertise in experimental design, biostatistics, clinical research, bioinformatics, and data management. Faculty deliver lectures, lead interactive discussions, and provide insight into best practices across both basic and translational science.

Duration The course spans eight hours total, offered over four weeks during the spring semester. Attendance is mandatory.

Frequency Classes meet twice weekly.