Workshop agenda – Thursday, Feb. 20, 2025
View agenda for Tuesday, Feb. 18.
Agenda details for Wednesday, Feb. 19 pending.
Presented by Elena Azadbakht, associate professor and health sciences librarian, Teresa Schultz, associate professor and scholarly communication and social sciences librarian and Challen Wright, assistant professor, metadata librarian, ÍƼöÐÓ°ÉÔ´´.
This workshop will provide hands-on learning for participants through the process of preparing and sharing datasets in data repositories to comply with grant funder mandates. Participants will explore various data repositories, including the university’s data repository Dryad, as well as what they will need to do to prepare their data to ensure the data is reusable and understandable, and how they can get credit for sharing their data.
Curious about AWS cloud computing services or eager to explore their new and exciting tools, like Amazon SageMaker Unified Studio? Join the AWS team for an interactive demo showcasing this powerful tool and its capabilities for accelerating machine learning workflows.
Stay afterward for a relaxed coffee-and-cookie open discussion, where you can ask questions, share challenges from past projects, explore training opportunities, or learn how to begin your cloud computing journey. Everyone is welcome!
Presented by Paul Hurtado, Ph.D., associate professor, mathematics and statistics, ÍƼöÐÓ°ÉÔ´´ in collaboration with Theresa McKim, Ph.D., teaching assistant professor, biology, ÍƼöÐÓ°ÉÔ´´
Learn to turn your data analysis into polished, professional documents! This hands-on workshop covers using R Markdown, Jupyter Notebooks and LaTeX to create dynamic reports, integrate code and visuals, and produce publication-ready outputs.
- Create dynamic reports using R Markdown and Jupyter Notebooks
- Format equations and customize outputs with LaTeX
- Combine code, visualizations, and text for seamless workflows
- Embed R or Python scripts directly into LaTeX documents
Perfect for researchers and students looking to streamline their workflows and enhance the quality of their reports.
Presented by Carlos Ramirez Reyes, Ph.D., assistant professor, research data services, University Libraries
This workshop will guide participants through practical techniques for handling messy datasets. Topics include managing missing values, removing duplicates, tidying data and reformatting variables using R. Ideal for researchers and analysts seeking to efficiently prepare their data for analysis. Basic knowledge of R and R studio is required.
Presented by Jeremy Tiedt, assistant dean, undergraduate student success, College of Business, ÍƼöÐÓ°ÉÔ´´
Presented by Kanishka Manna, Ph.D., postdoctoral scholar, Nevada Bioinformatics Center
This hands-on workshop introduces the powerful workflow management system Nextflow for creating automated and reproducible computational pipelines. Participants will gain insight into workflow management systems, explore Nextflow’s advantages, and learn pipeline development through a practical example (not domain specific). This workshop is designed to equip researchers with the skills needed to automate and scale their data analyses efficiently.
- Understand the fundamentals of reproducible analysis pipelines and workflow management systems (WMS)
- Compare Snakemake vs Nextflow and identify factors to choose the right WMS
- Set up the environment and tools required for Nextflow
- Build and execute a beginner-friendly Nextflow workflow (e.g., "Tacos with Nextflow")
- Develop pipelines by defining parameters, adding processes and creating workflows
- Modify pipelines for scalability and reproducibility, including sharing via GitHub
- Test pipelines with nf-test and integrate open-source tools like conda
This workshop will emphasize practical learning through hands-on exercises, including a creative and accessible example pipeline to demystify pipeline development.
Presented by Alex Knudson, software engineer, Light & Wonder in collaboration with the Nevada Bioinformatics Center
Dive deeper into R with this advanced session focusing on data wrangling and visualization using dplyr, tidyr, and ggplot2. Participants will learn practical applications for real-world scenarios.
- Data wrangling with dplyr and tidyr (filtering, grouping, summarizing, reshaping)
- Creating visualizations with ggplot2 (scatterplots, bar charts, customizing themes)
- Applying Tidyverse and ggplot2 in real-world scenarios with guided examples
Presented by Cassandra Hui, Ph.D., Bioinformatician, Nevada Bioinformatics Center
Whether you're new to version control or looking to enhance your project management skills, this hands-on workshop will provide the training you need to leverage GitHub effectively for collaboration and personal use.
Join us for an interactive learning experience where you’ll explore:
- Setting up and navigating GitHub
- Creating and managing repositories for academic projects
- Collaborating on shared repositories
You’ll leave with practical skills to manage research projects and collaborate seamlessly with others – all while gaining experience with GitHub’s powerful interface. This training will use GitHub online and GitHub Desktop and requires no coding.
Presented by James Ragsdale, doctoral student and instructor, sociology, ÍƼöÐÓ°ÉÔ´´
This interactive workshop examines how researcher decisions — known as researcher degrees of freedom — can unintentionally bias results or inflate type I error rates. It covers cognitive heuristics that may unconsciously lead to questionable research practices. Through group discussions, case studies, and data visualization, participants will explore flexibility in study design, analysis, and reporting while learning strategies to reduce bias and strengthen research integrity.
- Identify key stages in the research process where choices influence outcomes
- Understand the impact of p-hacking, selective reporting, motivated reasoning and HARKing
- Learn strategies to avoid questionable research practices
- Enhance research reliability and credibility through methodological rigor
This workshop is essential for anyone aiming to improve the quality and reproducibility of their research.