"Reproducibility is a core principle of scientific progress. Scientific claims should not gain credence because of the status or authority of their originator but by the replicability of their supporting evidence." - Open Science Collaboration in Science

Landmark articles


Learn more about reproducibilitiy in the context of workflows in the Land of 10,000 Workflows series

Services for reproducible research


Data Curation and Sharing

Data Management

Systematic Review support

Help finding reporting standards (e.g. PRISMA)

Help Finding sites for submitting pre-registrations (e.g. Clinicaltrials.gov, Open Science Framework, etc.)


Liberal Arts Technologies and Innovation Services (LATIS), housed in the College of Liberal Arts (CLA), supports researchers in the liberal arts and social sciences. We provide training, consultation, and direct support for a variety of research methodologies and tools. Our goal is to help faculty and graduate students take steps towards more reproducible workflows and better managed data wherever they are in research life cycle.

We offer a workshop series on reproducible research tools that are free and open to graduate students and faculty.

More information about our services is available at z.umn.edu/latisresearch.

Some specific services relevant for reproducibility:

  • Documentation: Tips and techniques to document and organize your workflow no matter what tool you are using. Some examples of tools we can help with  Markdown/LaTeX, Jupyter Notebooks, Qualitative Analysis Tools (NVivo, Atlas.TI), Quantitative Analysis Tools (R, SPSS, Stata)

  • Open Science: Prepare your data and code for sharing. We can help: 1. Review datasets for potential privacy/identification issues 2. Extract metadata (variable labels, value labels) from survey tools or statistical packages to create codebooks. 3. Work with the libraries to determine the best ways to make your data/materials available.

  • Version Control: Integrate version control systems (such as github) into your workflows.

  • Efficiency: Our research computing resources can help you run large data analyses efficiently using CLA's new cluster computing and scripting techniques for parallelization.

  • Automation: Learn tools to automate portions of your workflow, from data collection to data "wrangling" using tools such as R and Python.

Contact us at surveys@umn.edu or our Online Service Request Form.


Minnesota Supercomputing Institute

  • Jupyter is an interactive electronic notebook for any computations in R, Python or other languages.  See MSI beta for access to MSI’s installation.

  • Stratus is a local research compute cloud environment available as part of MSI beta that will enable you to create exact cloned images of your production workflows.

  • MSI hosts their own enterprise version of Galaxy, that enables extensive point-and-click genomics analyses within your browser, where all histories of data analysis are carefully logged, tracked, and easily reproduced from start to finish.