Teaching Data Literacy Through Active Curation: The Grad Student's Data Survival Guide

Research data management (RDM) presents significant challenges for graduate students who must collect, process, analyze, and store research data throughout their academic careers. These challenges intensify when students join existing projects mid-stream or leave before completion, requiring them to rapidly understand unfamiliar datasets or prepare their work for seamless handoff to successors. Recognizing that data curation processes are specifically designed to align data with FAIR principles—ensuring materials are well-documented, logically organized, and accessible—we developed an innovative approach that uses data curation as the foundation for active data management training. Our solution is the Grad Student's Data Survival Guide, a comprehensive resource that teaches graduate researchers data literacy by guiding them in curating their own project materials. This approach leverages students' existing investment in their research projects to teach essential skills. By learning and applying curation best practices to their own work, graduate students develop competencies in evaluating data for future research use, designing projects that accommodate personnel transitions, and effectively managing and describing project materials from inception through completion. This poster presents a comprehensive overview of the Grad Student's Data Survival Guide, detailing individual lessons and demonstrating how our curriculum aligns with established data curation frameworks, particularly the Data Curation Network's CURATE(D) model. We will provide interactive access to the Guide via tablet, enabling attendees to explore the content firsthand and offer feedback on both specific materials and overall pedagogical structure. We welcome input from curation experts on potential gaps in our coverage and opportunities for improvement.