CFP: ACRL Data Literacy Cookbook

Kelly Getz (Eastern Michigan University) and Meryl Brodsky (University of Texas, Austin) are editing an ACRL Data Literacy Cookbook, and are seeking proposals from academic library practitioners for chapters that describe “practiced lesson plans, curriculum map development, activities, and events designed to promote and educate data literacy through library instruction and outreach.” In particular, they are hoping for tried and tested examples, which can range from simple and quick door-opener activities, to lessons, courses, and curriculum maps. Audiences of these lessons are not limited to academia, and they may also include community-based collaborations and outreach events. From the call, examples of potential topics include:

  • Data visualization and infographics (creation and interpretation)
  • Finding and using secondary data
  • Data management, formatting, archiving, and preservation
  • Interpreting statistics, surveys, polls
  • Data and statistics in the news, and in the disciplines
  • Data literacy outreach and engagement
  • Data literacy among the disciplines
  • Data carpentry
  • Data citation
  • Personal archiving, naming conventions
  • Research data services

Proposals should be ~500 words and include: Title, Audience description, Learning objectives (ties to ACRL Framework for Information Literacy), Length of activity/activities in minutes (if applicable), General description of the activity, lesson, event, or curriculum map, and assessment.

Submit proposals by September 30th, 2020, via this Google form: https://forms.gle/wFZTr4qAhW21esuv7

Contributors will be notified of their proposal’s status by December 30, 2020. The deadline to submit the first draft of accepted chapters for revision is April 1, 2021.

dh+lib Review

This post was produced through a cooperation between Caitlin Christian-Lamb, Nickoal Eichmann-Kalwara, Linsey Ford, Ian Goodale, and Pamella Lach (dh+lib Review Editors).

Leave a Reply

  

  

  

This site uses Akismet to reduce spam. Learn how your comment data is processed.