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An Introduction to Reproducible Research Practices

[Online] The importance of reproducibility, replication, and transparency in the research endeavor is increasingly discussed in academia. This workshop will introduce the concept of “reproducibility” and foundational strategies that can increase the reproducibility of your work particularly related to organization, documentation, literate coding techniques, version control, and archiving data and code for future access and use. We will also present a protocol, the TIER protocol, as a tool that graduate students or others can use that are first approaching reproducibility. This workshop will primarily be tool agnostic and instead focusing on the high level practices that can be applied across disciplines and workflows with representative examples. A follow-up workshop “Designing a Reproducible Workflow with R and Git” will present a specific workflow in practice.

This workshop is eligible for 2 hours of Graduate School RCR Credit.

This event is offered virtually. A zoom link will be sent via email to registered participants to join the workshop.

The content of the workshop may be recorded. If you are uncomfortable with a recording being published, please contact the instructor at any time prior to the conclusion of the workshop.

Data Science, Data Management

Date:
Thursday, September 29, 2022
Time:
10:00am - 12:00pm
Campus:
n/a
Categories:
Data and Visualization  
Registration has closed.

Event Organizer

Profile photo of John Little
John Little
Profile photo of Sophia Lafferty-Hess
Sophia Lafferty-Hess
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Center for Data and Visualization Sciences