Undergraduate (UG) Undergraduate (UG) research is a high-impact practice that has been shown to benefit students' development and has the potential to contribute to faculty research output. However, UG research does not attain the impact that it could have on student development or faculty research productivity. Faculty doing UG research face two main challenges: continuity and efficiency. Continuity is important for faculty to achieve research goals. However, onboarding, training, and mentoring UG researchers can be an inefficient, time-consuming process due to a lack of formal mentorship processes. From a student perspective, UG research has two main challenges: lack of autonomy and understanding of relevance relative to other co-curricular activities. In some research groups, UG students may not feel that they have ownership of their work, which results in the decay of intrinsic motivation. We are developing an Entrepreneurial Mindset (EM)-focused framework to enhance the research experience both for faculty and students. This framework provides faculty with access to resources and collaborators to support their efforts in engaging students in UG research and developing continuity in their laboratories. Students, on the other hand, will benefit from training opportunities designed to spark their curiosity, help them establish connections between their learnings, and enhance the value they create through their work. EM principles can transform UG research experiences by improving students' understanding of the relevance of their research and how it creates value for society. Our goal is to infuse EM into students' understanding of the UG research process, with the objective of broadening their participation, addressing institutional challenges, and enhancing research productivity to benefit society. We seek to understand what benefits students perceive they will gain through the UG research experience so that we can understand how entrepreneurially-minded learning (EML) connects to those motivations. To answer this question, we survey students immediately after their UR experience, utilizing questions from well-developed UG research outcome surveys, such as the Undergraduate Research Student Self-Assessment (URSSA), coupled with questions focused on EML. Baseline survey data indicates that students could benefit from workshops that help them identify stakeholders and set goals in research, both of which align well with the goals of EML. First and second-year students involved in a pilot survey endorsed perceived gains in their understanding of research and desire to participate in research after exposure to the ideas of EML in research. This paper also describes the modules we created that utilize the EM to reduce inefficiencies in training, mentoring, and recruiting UG researchers. These can be shared across large and small universities and with scaling organizations such as the Vertically Integrated Projects (VIP) consortium and the Council on Undergraduate Research. The workshops would be broadly available for faculty to implement in courses, seminars, or research programs.

SUMMARY: GSEL is a computational framework for calculating the enrichment of signatures of diverse evolutionary forces in a set of genomic regions. GSEL can flexibly integrate any sequence-based evolutionary metric and analyze sets of human genomic regions identified by genome-wide assays (e.g. GWAS, eQTL, *-seq). The core of GSEL's approach is the generation of empirical null distributions tailored to the allele frequency and linkage disequilibrium structure of the regions of interest. We illustrate the application of GSEL to variants identified from a GWAS of body mass index, a highly polygenic trait. AVAILABILITY AND IMPLEMENTATION: GSEL is implemented as a fast, flexible and user-friendly python package. It is available with demonstration data at https://github.com/abraham-abin13/gsel_vec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.