CNI Spring 2025 Membership Meeting

ARL Executive Director Andrew K. Pace will begin the opening plenary with a 20–25-minute update, including a Q&A portion, on the CNI leadership transition.

The 2025 Paul Evan Peters Award recipients will look back over their two decades with the LOCKSS Program. Reich will focus on the Program’s initial goals and how they evolved as the landscape of academic communication changed, and Rosenthal will discuss the Program’s technology, how it developed, and how this history reveals a set of seductive, persistent but impractical ideas.

Presentation Slides & Notes: https://blog.dshr.org/2025/04/paul-evan-peters-award-lecture.html

Authors Alliance and the Northeastern University Library (NUL) are working with a diverse set of stakeholders to develop an actionable startup plan for a public interest training corpus for artificial intelligence. "Developing The Public Interest Corpus" is advanced through a series of iterative planning workshops and other related meetings with librarians, disciplinary researchers, publishers, technologists, authors, and more. This presentation provides an update on the effort to date, synthesizing takeaways from the first project workshop held at NUL in February. The workshop included discussion of the project’s: (1) principles and goals, (2) target audiences, training data needs, and potential partners (3) legal and policy challenges, and (4) business model, sustainability, and governance.

https://publicinterestcorpus.org/

Flickr is huge; it contains billions of images. It's a unique visual history collection, and the Flickr Foundation believes it is time to figure out how to preserve it for future viewers to enjoy. But, it is simply too big for any contemporary archive to take on, so the Foundation developed Data Lifeboat, a simple packaging service to bundle up slivers of Flickr for preservation elsewhere. A Data Lifeboat is designed to contain images and metadata and to jettison the more standard web archive for a format that is content- and context-focussed.  

https://www.flickr.org/programs/content-mobility/data-lifeboat/

Neural network artificial intelligence (AI) technologies capable of working with both images and text offer promising tools for improving access to library collections at scale. In particular, libraries increasingly must address the obligation to generate succinct "alt-text" descriptions of digital images, which often entails remediation tasks in the tens of thousands of items. AI approaches are appealing given their ability to automate complex tasks involving natural language, but there are plentiful reasons to look beyond simply pasting library materials into ChatGPT. Stanford University's experiments have found that both fine-tuning of locally hosted models and "conditioning" of the captions by incorporating available metadata into the model's instructions ("prompt engineering") show promise for producing useful descriptive text for images. They've also found that tailoring approaches to specific collections and keeping human reviewers in the loop are keys to making the alt-text as accurate as possible while gaining efficiency at scale. Beyond accessibility compliance, vision language models can also enable free-text "evocative" search in multiple languages, object detection, and other tools for improving discovery within image collections.

https://web.stanford.edu/~pleonard/cni2025/

This session will not be recorded; it is designed to provide attendees with a platform to openly engage in discussions about pressing issues affecting the field. All topics, whether widespread or specific concerns, can be addressed freely by participants. The session will begin with a focus on data access and infrastructure in the current US environment.