Virtual BUILD Research Collaboratory: A biomedical data science training using innovative pedagogy to address structures of racism and inequitable stress for undergraduates of color.

OBJECTIVE: The unprecedented events of 2020 required a pivot in scientific training to better prepare the biomedical research workforce to address global pandemics, structural racism, and social inequities that devastate human health individually and erode it collectively. Furthermore, this pivot had to be accomplished in the virtual environment given the nation-wide lockdown. METHODS: These needs and context led to leveraging of the San Francisco Building Infrastructure Leading to Diversity (SF BUILD) theories of change to innovate a Virtual BUILD Research Collaboratory (VBRC). The purpose of VBRC was to train Black, Indigenous, and people of color (BIPOC) students to apply their unique perspectives to biomedical research. These training activities were evaluated using a pre-post survey design that included both validated and new psychosocial scales. A new scale was piloted to measure culturally relevant pedagogy. RESULTS: VBRC scholars increased science identity on two items: thinking of myself as a scientist (+1point, p = 0.006) and belonging to a community of scientists (+1point, p = 0.069). Overall, scholars perceived stress also decreased over VBRC (-2.35 points, p = 0.02). Post VBRC, scholars had high agency scores (µ = 11.02, Md = 12, range = 6-12, σ = 1.62) and cultural humility scores (µ = 22.11, Md = 23, range = 12-24, σ = 2.71). No notable race/ethnic differences were found in any measures. CONCLUSIONS: Taken together, our innovative approach to data science training for BIPOC in unprecedented times shows promise for better preparing the workforce critically needed to address the fundamental gaps in knowledge at the intersection of public health, structural racism, and biomedical sciences.

Decreased accuracy of forensic DNA mixture analysis for groups with lower genetic diversity.

Forensic investigation of DNA samples from multiple contributors has become commonplace. These complex analyses use statistical frameworks accounting for multiple levels of uncertainty in allelic contributions from different individuals, particularly for samples containing few molecules of DNA. These methods have been thoroughly tested along some axes of variation, but less attention has been paid to accuracy across human genetic variation. Here, we quantify the accuracy of DNA mixture analysis over 244 human groups. We find higher false inclusion rates for mixtures with more contributors, and for groups with lower genetic diversity. Even for two-contributor mixtures where one contributor is known and the reference group is correctly specified, false inclusion rates are 1e-5 or higher for 56 out of 244 groups. This means that, depending on multiple testing, some false inclusions may be expected. These false positives could be lessened with more selective and conservative use of DNA mixture analysis.

SCIP: a self-paced summer coding program creates community and increases coding confidence.

In 2020, many students lost summer opportunities due to the COVID-19 pandemic. We wanted to offer students an opportunity to learn computational skills and be part of a community while stuck at home. Because the pandemic created an unexpected research and academic situation, it was unclear how to best support students to learn and build community online. We used lessons learned from literature and our own experience to design, run and test an online program for students called the Science Coding Immersion Program (SCIP). In our program, students worked in teams for 8 hours a week, with one participant as the team leader and Zoom host. Teams worked on an online R or Python class at their own pace with support on Slack from the organizing team. For motivation and career advice, we hosted a weekly webinar with guest speakers. We used pre- and post-program surveys to determine how different aspects of the program impacted students. We were able to recruit a large and diverse group of participants who were happy with the program, found community in their team, and improved their coding confidence. We hope that our work will inspire others to start their own version of SCIP.