Data Carpentry lesson from Ecology curriculum to learn how to analyse and visualise ecological data in R. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R. This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R.

This lesson introduces undergraduates to personal digital archiving (PDA) as an instructional bridge to research data management.

PDA is the study of how people organize, maintain, use and share personal digital information in their daily lives. PDA skills closely parallel research data management skills, with the added benefit of being directly relevant to undergraduate students, most of whom manage complex personal digital content on a daily basis.

By teaching PDA, librarians encourage authentic learning experiences that immediately resonate with students' day-to-day activities. Teaching PDA builds a foundation of knowledge that not only helps students manage their personal digital materials, but can be translated into research data management skills that will enhance students' academic and professional careers.

Peer-reviewed articles in this special issue:

- “Responsible AI Practice in Libraries and Archives: A Review of the Literature” by Sara Mannheimer, Natalie Bond, Scott W. H. Young, Hannah Scates Kettler, Addison Marcus, Sally K. Slipher, Jason A. Clark, Yasmeen Shorish, Doralyn Rossmann, and Bonnie Sheehey. The authors explore the existing literature to identify and summarize trends in how libraries have (or have not) considered AI’s ethical implications.
- “It Takes a Village: A Distributed Training Model for AI-based Chatbots” by Beth Twomey, Annie Johnson, and Colleen Estes, discusses the steps taken at their institution to develop and implement a library chatbot powered by a large language model, as well as lessons learned.
- “‘Gimme Some Truth’ AI Music and Implications for Copyright and Cataloging” by Adam Eric Berkowitz, details modern developments in AI-assisted music creation, and the resultant challenges that these surface regarding copyright and cataloging work.
- “Adapting Machine Translation Engines to the Needs of Cultural Heritage Metadata” by Konstantinos Chatzitheodorou, Eirini Kaldeli, Antoine Isaac, Paolo Scalia, Carmen Grau Lacal, and Mª Ángeles García Escrivá provides an overview of the process used to hone general-use machine translation engines to improve their outputs when translating cultural heritage metadata in the Europeana repository from one language to another.
- “Exploring the Impact of Generative Artificial Intelligence on Higher Education Students' Utilization of Library Resources: A Critical Examination” by Lynsey Meakin applies the Technological Acceptance Model to higher education students’ perceptions and adoption of tools using generative AI models.

Recurring content:
- Public Libraries Leading the Way: “Activating Our Intelligence: A Common-Sense Approach to Artificial Intelligence” by Dorothy Stoltz

- ITAL &: “The Jack in the Black Box: Teaching College Students to Use ChatGPT Critically” by Shu Wan