Spring 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 the videos, slides, and materials from past workshops at the LATIS Workshop Materials website.

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. 

Spring 2026 LATIS Workshop Series 

Register here for one or more workshops! 

 Please note that workshops days, times, and locations vary.  

 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

Making Publication Worthy Maps

Instructor(s): Michelle Andrews, Samantha Porter, 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)

Data Management for Reproducible Research in R

Instructor(s): Alicia Hofelich Mohr, Sasha Zarins 

Summary

You may be using R for your statistical analyses, but have you thought about whether others (or future you) would be able to understand and run your code later on? This workshop will introduce strategies for managing your code, including R projects and version control with GitHub, as well as tips for managing and documenting your packages. 

This workshop will cover:

• How to set up an R project to organize your research. 
• Benefits of using GitHub and how to integrate git and GitHub into an R project. 
• Good practices for code documentation and package management, including how to share and save package versions. 

To be successful, you should have:

• Familiarity with R and RStudio
• A research project you are working on or will be working on that uses R for data processing, visualization, or analysis. 

Introduction to Text as Data

Instructor(s): Michael Beckstrand, David Olsen 

Summary

An introduction to processing and analyzing text documents using Python.

Description

Scholars in humanities and social science fields are using computational tools to explore large collections of digital texts. This hands-on workshop will introduce common machine learning methods such as topic modeling and sentiment analysis, as well as fundamental cleaning and processing tasks for a text analysis workflow in Python.

This workshop will cover how to:

• Read and write text files in Python
• Manipulate ‘strings’ of text
• Pre-process and clean text for analysis
• Count word frequencies
• Build topic models and conduct sentiment analysis

To be successful, you should

• Be familiar with Python. Have previous experience with an introductory Python workshop, for example.
• Have a computer that can run JupyterLab in an internet browser

Web APIs in Python

Instructor(s): Michael Beckstrand, David Hahn

Summary

Web APIs (Application Programming Interfaces) provide a way for scholars to efficiently and legally access and download data from web platforms and publications such as the New York Times, or to access bulk data like citations with Scopus or OpenAlex. In this workshop we’ll use Python to query and download data from an API to get a handle on the full process, from gaining access credentials all the way to preparing data for analysis.

This workshop will cover how to:

• Use Python 3 in a JupyterLab computing environment
• Read API documentation to build successful API queries
• Use the Requests and JSON Python libraries to download data
• Use built-in Python functions such as type, len, and dir to explore API data
• Explore API data in Python using dictionaries

To be successful, you should have:

• A computer you can use during the workshop, with ability to log into notebooks.latis.umn.edu
• A free NYTimes developer account 
• No prior experience with these tools is necessary, and participants do not need to have any coding skills. 
• The Introduction to Python materials on Canvas are not required, but strongly recommended.

Beyond the ChatBot - AI Tools for Research

Instructor(s): Michael Beckstrand, David Hahn, Pernu Menheer, Alicia Hofelich Mohr, David Olsen 

Summary

Curious about how AI can be a helpful tool for research without getting in the way of the science or scholarship? Have you heard about ChatBot tools such as ChatGPT and Gemini, but aren't sure what else is out there? Join us for an overview presentation of AI tools for research! This workshop will provide quick overviews and demonstrations of four AI tools and how they can help you with specific research tasks. 

This workshop will cover:

• Using Atrain for secure transcription of audio files
• What is a Gem and how can I use it for research?
• How to use HuggingFace models for classification
• Using NotebookLM as your literature review assistant

To be successful, you should have:

• A curiosity about AI tools in research
• A laptop with a UMN sign on and internet connection 

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).