We are still offering consultation services during the COVID-19 move to online instruction. Please feel free to reach out for virtual appointments! For more information on library services and resources, please click here.
This following menu lists our most commonly requested and delivered workshops. Any of these workshops can be customized to your discipline and/or course assignment.
Please note that workshops requiring extensive customization (e.g. use of specific datasets, creation of new learning objects) should be requested as early as possible, and no later than 2 weeks before the date of the session, to ensure the best outcome.
Thumbnails in the boxes below indicate the availability of prerecorded versions. Please feel free to use and share these resources!
Getting your digital house in order is like writing a love letter to your future self. We’ll share a few quick file organization, naming, and documentation tips that can help you – and your collaborators – remember what you did and find your stuff 6 months from now.
If you’ll be working on a grant proposal, chances are you’ll have to write a data management plan (DMP). This session gives insight into what funders are looking for in DMPs, and walks (well, sprints) through the typical components of a DMP, including some examples from successfully funded proposals.
Data management plans workshop
This session will begin with an overview of the components of typical data management plans (DMPs). Then you'll get the opportunity to evaluate some sample DMPs, spurred on by a little friendly competition.
In this hands-on session, attendees will learn some basic Python while working in Jupyter notebooks, an interactive web tool for running and writing about code. Next, we'll use Python and Jupyter to run a simple text analysis on a custom dataset built with the Digital Scholar Workbench, a text mining platform for building and analyzing textual datasets from JSTOR, Portico, Chronicling America, DocSouth, and CORD-19. We will close by discussing opportunities to further expand attendees' coding and text analysis skills after the session. No prior experience with Python, JSTOR, or Jupyter is necessary, and no programming skills are needed or assumed for this session.