The GEP: Crowd-Sourcing Big Data Analysis with Undergraduates

Area of Science:

• Genomics
• Bioinformatics
• Science Education

Background:

• The increasing volume of biological data presents challenges and opportunities for research.
• Traditional research models may struggle to fully utilize abundant datasets.
• Engaging students in authentic research can enhance learning and scientific output.

Purpose of the Study:

• To explore the potential of student-scientist partnerships in the era of big data.
• To demonstrate how coordinated undergraduate efforts can generate high-quality genomic data.
• To highlight the dual benefits of scientific discovery and student research training.

Main Methods:

• Coordinating undergraduate student participation in large-scale genomics projects.
• Implementing standardized protocols for data generation and annotation.
• Facilitating collaborative analysis of genomic datasets.

Main Results:

• Production of high-quality, annotated genomic datasets through collective student effort.
• Generation of scientific analyses previously unachievable through individual or small-group efforts.
• Successful scientific publications resulting from the collaborative research.

Conclusions:

• Student-scientist partnerships are a viable model for addressing big data challenges in genomics.
• Undergraduate research participation can significantly contribute to scientific advancement.
• This collaborative approach provides invaluable research experience for students.