Course Requirements

BIMS 7100: Research Ethics (prerequisite)

All degree-granting programs require participation in BIMS 7100.  This course provides training in the responsible conduct in research (RCR) and includes sessions that cover the ethical aspects of performing biomedical research: research misconduct, conflict of interest, research involving human subjects and live vertebrate animals, mentor/mentee relationships and responsibilities, collaborations in biomedical research, peer review, management and ownership of data, responsible authorship, etc.

NESC 7010: Foundations in Neuroimmunology (requirement)

This course focuses on current topics in neuroimmunology research. The course is primarily literature-based, exposing students to recent and foundational literature on topics.  Students also complete an NIH-style aims page based on their project and participate in a mock study section. Outside experts that visit UVA as a part of the BIG Neuro Seminar Series meet with NESC 7010 students for one hour twice a semester. Note: this course has prerequisites of MICR 8200 and MICR8202, the introductory graduate-level immunology courses (Directed by Dr. Ewald). Exceptions are made for students that have completed an upper-level undergraduate course in immunology or years 1 and 2 of the medical school curriculum. The course is directed by Dr. Harris.

CELL 8450: Effective Science Writing for Grants and Fellowships (requirement)

The goal of this course is for trainees to learn the principles of grant writing and to prepare a fellowship application for submission.  Ideally, students will complete this course in the fall of their third year, building upon the document they created for their qualifying exam.

BIMS 8380: Basics of Study Design and Practical Statistics (requirement)

This course introduces the R statistical computing environment and packages for manipulating and visualizing data and covers strategies for reproducible research. Specifically, students will be able to (1) calculate and interpret descriptive statistics and plots, (2) choose and create data visualizations in R following modern best practices; (3) interpret concepts in inferential statistics; and (4) conduct parametric and non-parametric hypothesis tests and apply the appropriate test in novel experiments using R statistical software.  Importantly, this course provides training in power analysis, which is essential for proper experimental design.