Prerequisites:
None. No prior AI, coding, or data science experience is required.
Overview:
Artificial Intelligence can dramatically accelerate data analysis, but researchers often struggle with steep learning curves and privacy concerns. This workshop introduces approachable, code-free AI workflows using tools available through Rice's Google Workspace for Education license.
Participants will learn how to use Google Gemini and Gemini Notebook (formerly NotebookLM) as grounded, secure research partners. From literature reviews and qualitative coding to natural-language quantitative analysis, this session demonstrates how to interrogate datasets, uncover patterns, and streamline research while using AI responsibly and keeping data protected.
Topics Include:
- Institutional Security & Privacy: Understanding how institutional education licenses protect research data from being used for public AI model training.
- Source-Grounded Literature Reviews: Uploading research papers into Gemini Notebook to generate comparative synthesis tables and query documents with exact, clickable citations.
- Qualitative Data Extraction: Processing interview transcripts, field notes, and survey responses to extract thematic trends and representative quotes.
- Code-Free Quantitative Analysis: Using Gemini's native data capabilities to summarize tabular files (CSVs and Google Sheets), run descriptive metrics, and plot charts using conversational prompts.
- Academic Prompt Engineering: Crafting structured prompts, setting analytical constraints, and implementing verification techniques to prevent AI hallucinations.
Contact information:
Please email researchdata@rice.edu if you have questions about the Data@Rice workshop series.