NRT-HDR: using a data analytics framework to merge STEM and entrepreneurial training

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In the past decade, businesses that utilize “omics” information (genomics, transcriptomics, and proteomics) to answer major societal issues in medicine, environmental science, and other fields have flourished. These successes highlight the power of bridging academic discoveries and their commercial applications. However, they also identify a gap in current STEM graduate student training: curricula are designed to focus narrowly on the academic processes of discoveries with minimal exploration of their application to and development in the private sector, which is the purview of business graduates. Similarly, business graduates are well trained in their field but lack the scientific background to make informed decisions about the research direction of a potential company. This National Science Foundation Research Traineeship (NRT) award will address this gap by developing a fully-integrated omics-business curriculum that will develop scientific knowledge alongside entrepreneurial skills. The convergent nature of this program will be based in interdisciplinary groups of faculty mentors and student teams that will work together from data collection and analysis to business design and development. The program anticipates the participation of 18 funded trainees and an additional 30 graduate students (Master and PhD). Undergraduate students will also be encouraged to participate in some of the activities that will be developed. Overall, approximately 100 students are expected to benefit from this NRT program.The research theme of this Data Analytics in Multi-Omics Science (DAMOS) NRT program is investigating phenotypic variation using advanced genomics, transcriptomics, and proteomics methodologies. “Phenotype” is any modification, whether macroscopic, microscopic, or molecular, that is correlated with changes in organismal fitness and survival. Genomics, transcriptomics, and proteomics variability drives phenotypic alterations. Using the common language of data science and analytics, students in this program will design and develop projects that explore the full breadth of an “omics” pipeline, thus contributing to mapping genome to phenome relations. At the same time, they will capitalize on this knowledge to develop a business plan, practice business pitches, and network/intern with local industry partners to learn how to transform academic discoveries into solutions for some of society’s grand challenges. The focus on data science and data analytics will effectively create a new training paradigm that transcends the boundaries of individual disciplines to create a new generation of scientists who are equally comfortable diving into the details of their research as they are at exploring its potential business and societal impacts. Key elements of the DAMOS program include (i) a flexible curriculum that draws from three different disciplines (biology, mathematics and statistics, and business), (ii) modular workshops and experiential learning opportunities that integrate and expand on each student’s scientific and business background, and (iii) professional and career development opportunities to explore academic and industry career paths. All these elements are fully integrated within current graduate curricula at Oakland University and will provide a sustainable and scalable blueprint for this and future transdisciplinary programs.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.