The Computational Science for Undergraduate Research Experiences (CSURE) is an NSF funded Research Experiences for Undergraduates (REU) program organized by the Joint Institute for Computational Sciences (JICS), www.jics.utk.edu/csure-reu. The main goal of the CSURE project is to direct a group of ten undergraduate students to explore the emergent computational science models and techniques proven to work on the supercomputers at the National Institute for Computational Sciences (NICS). In addition, a number of summer interns from Hong Kong also participated in the program. The CSURE program focuses on five different scientific domains: chemistry and material sciences, systems biology, engineering mechanics, atmospheric sciences, and parallel solvers on emergent platforms. The program also enjoys the joint relationship with researchers at the Oak Ridge National Laboratory. Because of these diverse topics of research area and backgrounds of participant, we will in this paper entail the challenges and resolutions in managing and coordinating the program, delivering cohesive tutorial materials, directing mentorship of individual projects, and a few good lessons learned in the duration of the program since it started in 2013.
PURPOSE: We present work in assisting Bioinformatics efforts at minority institutions in the USA funded through an NIH grant over the last 15 years. The primary aim was to create a program for assisting minority institutions in building multidisciplinary bioinformatics training programs. DESIGN: The program involves four components for immediate and long-term increases in research opportunities at minority institutions. Component 1: A two-week Summer Institute in Bioinformatics introducing the breadth of bioinformatics while discussing open research problems. Component 2: Strengthening or establishing bioinformatics programs at minority serving campuses by teaching bioinformatics in collaboration with local faculty. Component 3: A five to eight-week research internship at the PSC for students that completed bioinformatics courses on their campuses. Component 4: Development of a model curriculum for a concentration in bioinformatics in biology, computer science, or mathematics. In this paper we will report on the results of the internship program. METHODS: In compliance with federal regulations (45 CFR 46) concerning Human Subjects Research, the survey materials and procedures used and discussed in this paper were approved by Carnegie Mellon University’s Institutional Review Board (IRB No. HS-13-099 on 3/15/13, IRB No HS-14-141 on 3/18/14, and IRB No HS-15-178 on 3/12/15). Under these approved procedures, we began to conduct voluntary pre and post surveys of interns and summer institute participants. These surveys were completed at the very beginning of their summer experience at the PSC (pre-survey) and again at the end of their summer experience at the PSC (post-survey). This paper reports on a subset of the questions asked in the pre and post surveys, mainly on the demographic and skill sets of the participants that are most relevant to computation and high performance computing. DEMOGRAPHICS: 21 minority institutions have benefited the grant. Of the thirty-six student surveys completed, 36% were completed by undergraduate students, 36% by master’s students and 25% by doctoral students. 96% of total participants identified that they were attending a minority serving institution (MSI) with 47% indicating that they were attending a Hispanic serving institution, 44% indicating that they were attending a historically black college/university while 5% indicated that they were attending an “other” minority serving institution. 82% of participants self-identified as belonging to racial and ethnic groups that have been shown by the National Science Foundation to be underrepresented in health-related sciences on a national basis, which includes African Americans, Hispanic Americans, Native Americans, Alaskan Natives, Hawaiian Natives, and Natives of the U.S. Pacific Islands. NEEDS ASSESSMENT: The MARC pre-survey also included questions asking the participant to identify their prior bioinformatics knowledge. Few student participants self-identified as having intermediate bioinformatics knowledge. The majority of participants in the pre-surveys identified themselves as being able to run bioinformatics programs, but being uncomfortable changing the program parameters. One-third have not done basic bioinformatics analysis (such as database search and multiple alignment). About one-half had not done more advanced bioinformatics analysis and greater than one-half had not worked with structural data. About three-quarters or greater of the participants had not been exposed to common NGS analyses. The number of participants that reported Basic or Advanced skills with programming, databases, or the UNIX operating system was 30% or less. When asked through an open text unstructured question to list the basic steps and tools needed for these analyses, before the workshop the answers to this type of questions was typically “I do not know”. In the post-survey, the majority of the participants expressed that they could run basic bioinformatics analyses at an intermediate or advanced level. For NGS tasks done during the training less than one-third expressed that they could perform these analyses at an advanced level. The participants reported improvements in their programming, databases, or UNIX skills but 30-50% indicated low-level skills in these areas. LESSONS LEARNED: A strong team effort at the teaching level is needed to help improve the skill set of interns. Follow-up is key in order to help student maintain or improve the skills gained and carry out research successfully. It is very difficult to add courses or degrees in many state Minority Serving Institutions. Variability in traditional Biology Curricula make adapting the courses and modules required for broader improvement of computational skills a challenge. Math requirements at the bachelor’s degree level and “introductory computing” courses can be substantial barriers to success for biology students. Finally, better bioinformatics and computational textbooks for biologists at the undergraduate level are needed. CONCLUSIONS: This program has been a highly successful outreach effort and a very sound and cost-effective use of the MARC funding program from NIH. Important lessons have been learned about bioinformatics education that should be implemented at the policy level in order to ensure that educators, students and researchers at minority serving institutions can address science problems using state-of-the-art computational methods, computational genomics and Big Data.
GRANT SUPPORT: This work was supported by National Institutes of Health Minority Access to Research Careers (MARC) grant T36-GM-095335 to the Pittsburgh Supercomputing Center. It also used the BioU computing cluster, which was made available by National Institutes of Health grant T36-GM-008789 to the Pittsburgh Supercomputing Center. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant OCI-1053575. Specifically, it used the Blacklight supercomputer system at the Pittsburgh Supercomputing Center (PSC).
Opportunities to solve real-world problems - collaboratively or individually – can create significant impact on the education and career goals of students. By providing such opportunities to students, research groups and businesses can also benefit significantly. However, several factors and strategies play a role in developing a mutually beneficial synergy between the students and research groups (or businesses). Adopting a disciplined agile approach for student engagement and retention is one such strategy. In this paper we discuss our experiences in engaging students in research and development through the aforementioned approach. Some lessons learnt and recommendations are also included in the paper.
