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Program Timeline

2019 OUR Summer Conference

Students who have completed their sophomore through senior years of their undergraduate degree at Virginia Tech or another institution are eligible to apply. While students in biology, nutrition, or animal-related disciplines usually have the best fit and interest with NIDDK-related research, there is no departmental stipulation to apply; previous Scholars have included students from statistics and computer engineering. On the application, students will need to articulate their interest in NIDDK-related biomedical research and how their current training and education has prepared them to be a TOUR Scholar.

Complete the application by January 25, 2021 to be considered as a Summer 2021 TOUR-Scholar.  


Program Timeline

Fall

  1. Call for applications
  2. Summer research information sessions held (September - October)
  3. Due date for applications
  4. Review of applications
  5. Notification of acceptance and mentor matching
  6. First meeting with mentor (November – December)
  7. Sign up for Independent Study course

Winter/Spring

  1. Prepare for summer project with background reading and meetings with mentors and research groups (January - May)
  2. Library database workshop: effective library research (February)
  3. Read Ignorance: how it drives science by Stuart Firestein
  4. Research Compliance (IACUC, IRB, COI) completed during semester
  5. Meet with program directors (in person or WebEx)
  6. Develop timeline for summer project goals
  7. Proposal based on summer project (April)
  8. Advisory Board reviews TOUR Scholars grant proposals (May)
  9. Pre-program data collection

Summer Program

  1. Week One: Boot Camp Week (orientation and team-building exercises)
  2. Weeks Two - Ten:
    • Weekly meetings
    • Research (up to eight hours a day)
    • Poster and presentation preparation
    • Office of Undergraduate Research Summer Research Conference
    • Post-survey data collection

Post Program

  1. Travel grants awarded to those who apply
  2. Evaulation
  3. Data Analysis