Spring 2025 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.

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 2025 LATIS Workshop Schedule

Workshops will be a mix of in-person and online formats. Click on the links below for a detailed description of each workshop.  Registrants will be notified of any changes to the workshop locations once rooms are confirmed. 

Register here for one or more workshops! 

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

Introduction to NVivo 

Date(s): January 27, 2025; 1:00 - 3:00pm

Venue: In Person, Humphrey 25 

Instructor(s): Michael Beckstrand, Tessa Cicak

Format: In Person 

Level: Beginner

Audience: Graduate students

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

Love your data: Managing Code and packages in R 

Date(s): February 10, 2025; 1:00 - 3:00pm

Venue: Online via Zoom

Instructor(s): Alicia Hofelich Mohr, David Olsen, David Hahn

Format: Online

Level: Beginner

Audience: Graduate students

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. 

Troubleshooting code with AI 

Date(s): March 3, 2025; 1:00pm - 3:00pm

Venue: Humphrey 25 

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

Format: In Person

Level: Beginner

Audience: Graduate students

Summary

One of the trickiest parts of getting comfortable with any statistical scripting language (R, STATA, Python) or other coding for research is finding a research-relevant resource to help troubleshoot and problem-solve as you try to script something. You could always Google it? Maybe you’ve heard of a website called StackOverflow? Generative AI provides another solution with its ability to generate and interactively develop code to your parameters. This workshop is designed to introduce you to chatting with Microsoft’s Copilot about research coding tasks and using it to troubleshoot errors in code, suggest possible solutions to problems and use your current scripting knowledge to go further.

This workshop will cover:

• Using GenAI to outline a data processing workflow and provide suggestions
• Asking good questions and writing good prompts for code troubleshooting
• Working through examples of bugged code and code with room for improvement
• Creating code documentation, comments and reminders
• Common pitfalls or things to avoid

To be successful, you should have:

• A general interest in the application of generative artificial intelligence in research
• A base knowledge of some research-relevant scripting language: R, Python, STATA, Julia, JavaScript, etc
• Reviewed UMN’s Guidance on using Copilot, and on artificial intelligence usage

From Research to Recognition: Building Digital Research Portfolios and Websites

Date(s): March 17, 2025; 1:00pm - 3:00pm

Venue: Anderson 110

Instructor(s): Tessa Cicak, Samantha Porter

Format: In Person

Level: Beginner

Audience: Graduate students

Summary

Interested in making an academic website for yourself or for your research but not sure where to start? This workshop will take you through the steps of planning a website and choosing a website host. 

This workshop will cover:

• Comparisons of different website hosting options
• Creating a website strategy
• Best practices for website design and accessibility
• Tips for translating academic content into engaging website content
• Intro to using different website hosting services

To be successful, you should have:

• We will provide a document with prompts prior to the workshop, participants will use this as a guide for creating website content.
• Any files, photos, or data you would want to have on a website.
• Please bring a laptop

Introduction to Research Computing in CLA

Date(s): March 31, 2025; 1:00pm - 3:00pm

Venue: Humphrey 25 (To be confirmed)

Instructor(s): David Olsen, David Hahn, Phil Burton

Format: In Person

Level: Beginner

Audience: Graduate students

Tags: research computing, data management, essential skills

Summary

This workshop introduces essential concepts and tools for researchers who want to move beyond desktop computing for their research workflows. Participants will learn how to leverage the research computing infrastructure available in CLA, including compute.cla and the forthcoming interactive computational environments. 

Description

Modern research increasingly requires computational resources that exceed the capabilities of a typical PC—whether for tasks such as analyzing large datasets, running complex simulations, or processing resource-intensive fMRI workflows. Yet, despite the benefits high-performance computing can offer researchers, knowing where and how to begin tends to be a barrier to entry. This workshop addresses that issue by introducing participants to the fundamentals of accessing CLA's research computing infrastructure followed by a demonstration of some basic skills for effectively utilizing those resources.

In particular, we will examine both interactive computing interfaces, which offer a familiar web-based environment ideal for beginners, and traditional command-line access through SSH for more advanced workflows. Additionally, participants will learn data management strategies, including how to effectively transfer, organize, and stage research data using tools such as scp for quick file transfers or Globus for managing larger datasets. We will demonstrate how to manage research software through the module system, which will help you access the wide range of research applications LATIS curates on our systems. Finally, participants will be introduced to the basics of the job scheduler, learning how to submit and monitor computational tasks on compute.cla, enabling them to utilize these resources for their own research needs.

This workshop will cover:

• How to connect to CLA's research computing infrastructure through both interactive computing interfaces and SSH
• How to stage your research data using your file manager or tools like scp and Globus
• How to navigate the computing environments and load research software using modules
• How to submit and manage computing jobs through the job scheduler
• Where to find support for your research computing needs

To be successful, you should have:

• A computer you can use during the workshop, with a SSH client and NoMachine installed (directions to follow).
• This is an introductory workshop. No prior experience with these tools is necessary, though knowledge of the basics of working within a Linux environment and basic coding skills will be useful. 

Introduction to Web Scraping 

Date(s): April 7, 2025; 1:00pm - 3:00pm

Venue: Online via Zoom

Instructor(s): Michael Beckstrand, David Olsen

Format: Online

Level: Beginner

Audience: Graduate students

Summary

The internet is full of information waiting for exploration - from social media, to newspaper comments, to digitized archives. How do you begin gathering this kind of data? This workshop will introduce participants to browser-based tools for web scraping as well as reproducible web scraping methods using Python. We will cover essential legal literacies to ensure you can make informed decisions about when and how to web scrape following legal and ethical best practices.

This workshop will cover how to:

• View and explore the HTML tree underlying every webpage you see
• Use a browser extension (Scraper) to systematically copy sections of matching HTML from a single webpage
• Use Python 3 in a JupyterLab computing environment
• Use the Requests and BeautifulSoup Python libraries to access HTML data from the web
• Create variables, lists and loops to work with web data in Python
• Store and view HTML data in Pandas dataframe format

To be successful, you should have:

• A computer you can use during the workshop, with 
  • ability to log into notebooks.latis.umn.edu
  • Chrome browser with the Scraper Chrome extension
• 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.

Web APIs in Python

Date(s): April 14, 2025; 1:00pm - 3:00pm

Venue: Online via Zoom

Instructor(s): Michael Beckstrand, David Hahn

Format: Online

Level: Beginner

Audience: Graduate students

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.

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