Instructors

• Please see our instructor repository for general guidelines relevant to all classes. Additional recommendations specific for this course can be found below.

• Administering challenges: this section will vary based on the course content and interface.

Learner profiles

1. Researcher X is a clinical researcher who uses the Genomics Core to collect genomic data, and the Bioinformatics Core to analyze the data. The data generation and collection follow standard methods common in biomedical research. Researcher X views the relationship with SR Cores as transactional, with their lab as a customer of the Cores; they have no interest in deviating from boilerplate methods, or learning to perform any analyses on their own. They periodically ask questions of Core staff that suggests they would benefit from additional insight into how the data are generated and analyzed.

2. Researcher Y is primarily a wet-lab researcher who uses Core facilities to obtain data and perform basic analyses. They have little to no experience with coding. They view the relationship with the Core facilities as collaborative, with everyone contributing intellectually to generating and interpreting results. Researcher Y is interested in learning how the analyses work so they can perform follow-up studies on their own with the same data. They would also like to make their analyses reproducible and share the code for other lab members and collaborators to use.

3. Researcher Z is a computational researcher in a lab that performs both wet and dry lab experiments. They have formal training in bioinformatics and data analysis, but have not performed wet-lab work themselves. Their lab operates by generating data from the Core, but handing off the raw data to Researcher Z to analyze. The lab is moving into developing novel applications of sequencing technology to answer cutting-edge research questions. Researcher Z does not interact with Core facilities; they receive data from other lab members who submit the samples for processing. Because Researcher Z doesn’t have experience with data collection, their progress is hampered by needing to consult frequently with wet-lab researchers, and still missing important assumptions about the data when developing new algorithms. They need to develop a better mental model for how data interacts with software to yield answers to biological questions.