Fall 2026 Workshops

LATIS offers a series of workshops that are free and open to all faculty, graduate students, and staff. Join our LATIS Research Workshops Google Group to be the first to learn about workshops. You can view recordings from our past workshops on our LATIS Mediaspace Channel.

NEW: Subscribe to the LATIS Workshop Calendar to see all events 

Workshops are also offered on even more topics from partner departments:

• Research Data Services (UMN Libraries)
• MSI (MN Supercomputing Institute)

See workshops.umn.edu for a list of current Research and Computing Workshops across the University. 

Fall 2026 LATIS Workshops 

Register here! 

 Workshops will be offered on Wednesdays this fall. Online workshops will be recorded and distributed after the workshop. 

 Register today! 

Asynchronous Workshops

We also offer asynchronous workshops in canvas that you can take at your own pace. Please contact us [email protected] with any questions or trouble enrolling. Click on the links below for a detailed description of each workshop.

Workshop Descriptions

Qualtrics 101: Qualtrics Designing Complex Surveys & Experiments 

Date(s): Wednesday September 16, 10:00am - noon 

Venue: Online (zoom)

Instructor(s): Sasha Zarins, Thomas Lindsay

Description

Surveys are often used by researchers in a wide range of academic disciplines, but it’s not always easy to figure out the ins and outs of how to actually create a survey. Qualtrics is a powerful tool offered by the university to build complex survey designs and experimental tasks. This workshop will help you get started with some of Qualtrics’ more sophisticated aspects. We will also touch on how and when to transition your surveys to Qualtrics’ New Survey Taking Experience (also known as the Simple Layout).

This workshop will cover how to:

• Create and edit different types of questions in a survey;
• Insert piped text;
• Add embedded data fields;
• Edit the survey flow;
• Work with skip, display, and branching logic;
• Insert or change randomization at the response level, question level, and block level;
• Create workflows; 
• And more.

To be successful, you should have

• Basic experience with Qualtrics-- either through your own research or by taking LATIS's Introduction to Qualtrics Canvas Module (~1 hour).

Introduction to NVivo 

Date(s):, September 23, 10am-noon 

Venue: Appleby 302

Instructor(s): Michael Beckstrand, Tessa Cicak

NVivo is a qualitative data management, coding and markup tool, that facilitates powerful querying and exploration of source materials for both mixed methods and qualitative analysis. It integrates well with tools that assist in data collection and can handle a wide variety of source materials. This workshop introduces the basic functions of NVivo, with no prior experience necessary. Licensing is provided for faculty and graduate students of the College of Liberal Arts and the College of Education and Human Development; others can run the software in trial-mode for two weeks or can be given temporary access to the software for this workshop. 

This workshop will cover

• Adding your source materials (text, images, audio/video, survey/spreadsheets)
• Working with concepts (or codes/tags) and their definitions
• Making annotations and analytical memos
• Using text queries to speed up coding
• Finding patterns in the concepts identified in the source materials
• Importing data from other tools including Qualtrics, OneNote, and Zotero
• Exporting excerpts and making backups
• Working in teams

To be successful, you should

• Be familiar with source materials used in qualitative research (interviews, focus groups, field notes, archival documents, etc.)
• Be familiar with the types of questions asked in qualitative research
• Download and install NVivo from z.umn.edu/getNVivo prior to the workshop

Making Publication Worthy Maps

Date(s): Wednesday October 7, 10:00am - noon 

Venue: Geocommons (Blegen Hall)

Instructor(s): Michelle Andrews, Tessa Cicak

Description

Have you ever needed to make a map for a presentation, publication, or paper? Have you been relying on points and boundaries roughly drawn on a screenshot? Are you ready to learn a better way? This workshop will walk you through the steps using ArcGIS Online. We will help you find an appropriate basemap. We will teach you how to incorporate point data and shape data using an example dataset and export a map for publication. Participants will also learn about open source map data resources. 

This workshop will cover how to:

• Make a publication worthy map using ArcGIS Online
• Find an appropriate basemap
• Incorporate point and shape data
• Export map data in an appropriate format for publication

To be successful, you should have

• A computer that can run ArcGIS Online in an internet browser
• A active UMN internet ID
• Data to make your own map (optional)

Swedish death cleaning for your research: Data management habits for more efficient, reproducible, and enjoyable work

Date(s): Wednesday October 28, 10:00am - noon 

Venue: Zoom

Instructor(s): Tessa Cicak, Alicia Hofelich Mohr

Description

Take a look at your computer’s desktop, downloads, and documents folder. If you needed to find a file in the next 30 minutes, could you? What about 10 years from now?  How do you decide what’s worth keeping and what can be thrown away? What do you do with the stuff you decide should be kept?

This workshop will help answer those questions and more. Here, we will take a future-centered approach to data management, loosely based on Swedish Death Cleaning. The goal is to provide workflows and strategies that you can implement for all the different types of files and research you accumulate each day. 

This workshop will cover how to:

• Review and intentionally keep files associated with research projects 
• Determine when files are no longer needed 
• Store files securely and efficiently
• Develop effective workflows for research projects 
• Strategies for managing files during transitions in work or roles

To be successful, you should have

• A research or scholarly project in mind
• Files that need to be organized

Qualitative Research and AI 

Date(s): Wednesday Nov 4, 10:00am - noon 

Venue: Online (zoom)

Instructor(s): Michael Beckstrand, David Olsen

Description

Generative AI tools and Large Language Models (LLMs) offer powerful capabilities across all stages of the research process—from planning and literature discovery to automated transcription, coding, and thematic analysis. However, using these tools effectively requires understanding how to balance technological automation with research ethics, methodological rigor, analytical principles, reflexivity, and data privacy.  

This workshop introduces practical strategies and frameworks for incorporating Generative AI into qualitative workflows. We will examine UMN-approved general-purpose tools, built-in AI capabilities within popular Qualitative Data Analysis (QDA) software, structured AI-assisted qualitative analysis frameworks, and open-source automated transcription tools. 

This workshop will cover how to:

• Use UMN-approved AI tools (Gemini, NotebookLM) securely for research
• Apply effective prompt strategies to summarize and analyze qualitative data
• Leverage built-in AI features in software like NVivo and ATLAS.ti
• Explore structured AI-first frameworks like Guided AI Thematic Analysis (GAITA) 
• Use open-source tools (Whisper, aTrain) for private, local transcription

To be successful, you should have

• Basic familiarity with qualitative research concepts (e.g., interviews, coding)
• An active UMN Internet ID to explore UMN-supported AI applications. No prior experience with programming or advanced AI tools is required.

When your laptop isn't enough: Introduction to cluster computing

Date(s): Wednesday November 11, 10:00am - noon 

Venue: Online (zoom)

Instructor(s): Michael Beckstrand, David Olsen, Pernu Menheer

Summary

Your laptop is not always the right place to run a long or resource-intensive analysis. Using a small example program, this workshop will show you how to move that work to an HPC cluster, submit it through Slurm, and determine what happened when the job finishes or fails.

Description

Has your computer ever run out of memory, spent hours or days processing a dataset, or become unusable while an analysis was running? Perhaps you need to repeat the same computation hundreds of times, use a GPU, run work that you want to continue after closing your laptop, or simply be able to do something else while a task is running. These are common signs that it may be time to move part of your research workflow to a high-performance computing (HPC) cluster.

In this workshop, we’ll begin with a small program that could ordinarily be run from the command line and walk through running the same program on compute.cla (or other clusters at MSI or on national infrastructure) as well as web-based interactive compute environments such as notebooks.latis or Open OnDemand (OOD). We'll talk about its resource requirements, how to translate them into a job submission script, and then observe what happens after the scheduler takes over. We will also work through a failed run, using its logs to determine what went wrong, then correct it and resubmit. By the end of the workshop, participants will have taken a program from local execution to a completed cluster job and will have a practical model for approaching their own research workflows.

This workshop will cover how to:

• Recognize when a workload may benefit from HPC resources
• Understand the roles of login nodes, compute nodes, the scheduler, and the resources a cluster provides
• Estimate and adjust processor count, peak memory usage, and wall time to right-size a job
• Convert an existing command into a basic Slurm job script
• Submit, monitor, inspect, and cancel a job on a Slurm cluster
• Locate logs and other information useful for debugging a failed job
• Diagnose and correct common job failures

To be successful, you should have

• Basic familiarity with Linux environments, including navigating directories and running commands. You may register for LATIS’ Linux for Research Computing workshop to learn or refresh these skills prior to this workshop.
• Basic familiarity with at least one programming or scripting language, such as Python, R, Julia, or MATLAB

Managing Data When you Graduate (Canvas Modules)

Research and creative work doesn't end with degree completion; however, access to many of the data storage tools and software that have supported that work changes when students become alumni. This asynchronous workshop will help graduate students navigate questions about whether they can take their data and materials with them when they leave the university, and if so, how to do it. This workshop is co-organized by the University Libraries. 

The workshop will cover:

• The University policies that guide ownership of data
• Access changes to storage, software, and services that happen upon graduation
• Strategies and tips for ensuring data are accessible and understandable long after graduation

Schedule a consultation to discuss:

• How to make a plan to ensure a smooth transition for your data and materials between graduate school and your next endeavor
• Specific advice and troubleshooting for your own research and situation. 

To be successful, you should:

• Be a graduate student at the University of Minnesota at least a year into your program (it never hurts to plan early!), or who is nearing the end of your program.
• Have a research project (part of a dissertation or thesis) that has generated data or materials that you want to keep track of after you leave. This can include collaborative projects that will continue at UMN after graduation.

Introduction to Survey Sampling (Canvas Modules)

This is an interactive, self-paced Canvas course, designed for those who are either 1) completely new to surveying or 2) have never had formal instruction in survey/sampling design. By the end of course, you should be able to: 

1. Differentiate between a census and a sample
2. Describe features and limitations of common sampling methods
3. Recognize different sources of survey error/bias
4. Describe how different sources of survey error/bias affects the conclusions you can draw with your survey

This brief, introductory course to sampling is designed to take around 1-3 hours to complete, depending on the material you choose to engage with.

Qualtrics Tutorials (Canvas Modules)

We have three asynchronous Canvas courses available for you to take: 

1. Introduction to Qualtrics: Are you brand new to using Qualtrics? Or has it been a really long time since you used Qualtrics? Start here to learn the ropes. [Expected time: 1 hour]
    
2. Qualtrics Data Integrity & Management: No matter if you are new to Qualtrics or a long-time user, this module is a must for any Qualtrics user who is interested in 1) how to make Qualtrics data more readable and suitable to their needs, 2) best practices for conducting reproducible research within Qualtrics (e.g., sharing and archiving survey information, how to export data reproducibly, etc.). [Expected time: 35-45 minutes]
    
3. Designing Experiments & Complex Surveys in Qualtrics: Sometimes figuring out the right bells and whistles for more complex research designs in Qualtrics can be daunting. If you’re looking to build complex surveys or experimental tasks within Qualtrics, this tutorial is for you! We cover how to use some more complex functionality within Qualtrics, such as the using the survey flow, branching logic, embedded data, embedded media, piped text, “loop & merge”, integration with MTurk/Prolific, and more! In this module, you will watch a video walkthrough from our Fall 2021 workshop. [Expected time: 10-20 minutes for Canvas content; 2 hours of video content]

Working with Data in R - Tutorials (Canvas Modules)

R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. R is designed for reproducible research and can be used for many parts of the research process besides statistical analysis. This asynchronous course includes introductory readings, videos, and activities to build on and advance your data skills in R. 

Topics include

1. Foundations in R: Just starting in R? Welcome! This module will walk you through the basics of R and set the foundation for the more advanced modules below.
2. Publication worthy graphs with ggplot2: Learn how to adjust colors, axises, legends, and themes, as well as how to reproducibility save graphs for publication.
3. Create a table using dplyr: Learn how to aggregate data and create summaries for tables for publication.
4. Reshaping data: Data are not always in the right format for analysis or visualization. Learn how to transform data from wide to long format and back again.
5. R Markdown: Combine code, output, and text into readable documents with R Markdown. Learn how to create a basic R markdown document for research.
6. Working with Qualtrics data in R: Qualtrics is a popular tool for survey research, but the resulting data often require cleaning before analyzing in R. Learn how to efficiently clean Qualtrics data for use in R, including how to reproducibly remove the multiple headers, save labels, and combine multi-response columns. 

Linux for Research Computing (Canvas Modules)

This asynchronous course is a gentle introduction to command line programming using Linux. It is designed for CLA researchers and students who need to use high performance computing resources for their work (for example, to run fMRI analyses, parallel computing, or large scale analyses), but have little to no experience with Linux. 

This course guides participants through:

1. Connecting to the CLA compute cluster
2. Navigating directory and file structure using the Linux command-line terminal
3. Creating, modifying, and moving files using the Linux command-line terminal
4. Submitting an interactive and a batch computing job and understanding when it is beneficial to use one or the other

NEW Fall 2025: Part 2 guides participants who are familiar with Linux and CLA systems through connecting to computing resources offered by the Minnesota Supercomputing Institute (MSI).