CNI Spring 2025 Membership Meeting

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/

Artificial intelligence (AI) literacy has evolved from a theoretical discussion to an essential academic competency. In 2025, many academic institutions and libraries no longer debate whether to invest in AI skills, but rather how to do so effectively and responsibly. This panel will feature two library leaders highlighting how their institutions advance AI literacy initiatives. The discussion will focus on practical strategies for embedding AI literacy into library services; equipping librarians with essential AI skills; and fostering collaboration among faculty, IT, and students.

Why do we even care about data? What value do data bring us and what value do humans bring to the data landscape? As artificial intelligence (AI) becomes the default in data conversations, data curators are thinking critically about what this means. Intentional efforts from professionals such as curators invested in making data FAIR (findable, accessible, interopable, and reusable) are key to preserving the meaning of data. There is unique value and importance in people's labor and experiences when documenting and preserving data. It is the people who dig into the context and elevate what is missing to help make the data more FAIR. The session will address a project from the Data Curation Network (DCN) funded by Institute of Museum and Library Services grant [re-252343-ols-22] to support the development of a specialized curriculum for data curation training for information professionals and data stewards. Key to this work was drawing together data curators and information professionals to create in-depth training resources for four specialized data types: geospatial data, scientific images, code, and simulations. Using a cohort model with a guiding mentor, the project relied upon co-developing strategies to effectively and equitably work with volunteers to generate community-based resources. Community building, co-designing, bringing people from different types of disciplines, data backgrounds, and experiences are equally as important as developing a curriculum and sharing it. In fact, the curriculum benefits from and is made stronger when various intersectional voices are present. In this project briefing, the mentors will provide an overview of the community building that went into forming cohorts that developed, piloted, and refined specialized data curation curriculum, reflect on what worked well and where there are opportunities, and they will describe directions for future DCN work as well as how others can adapt this work for their own needs. The value that data curators in community bring to institutions, researchers, and data reuse will enable greater usability of data into the future.

https://datacurationnetwork.org/expanding-curation-training/

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/

Libraries have a rich tradition of interconnection, resource sharing, and cooperative metadata work, but not of open metadata. Our work with interoperability projects and research information services including IIIF (International Image Interoperability Framework), Blue Core, POD (Platform for Open Data), and RIALTO bring questions about metadata reuse to the fore. This session will describe the uncertainties and impediments created by lack of explicit licensing and argue that open licenses or declarations are the right solutions that allow the library community to innovate. We draw connections with the practices of the burgeoning open science and open data movements, as well as emerging requirements for outputs from federally funded research.

IIIF: http://iiif.io
Blue Core: https://bluecore.info/
POD: https://www.cni.org/topics/digital-libraries/pod-building-library-data-lakes-to-reduce-friction-and-enable-innovation
RIALTO: https://library.stanford.edu/rialto-research-intelligence

*The presenters will also host a breakfast discussion table on this topic (Tuesday, April 8, 7:45–9:00 am)

This briefing shares 18-month outcomes from a collaborative initiative among research libraries and iSchools at Virginia Polytechnic Institute and State University, the University of North Texas, the University of Colorado Boulder, and Los Alamos National Laboratory. This initiative aimed to strengthen academic libraries' role as research partners and promote library and information science research collaborations. The program combines open-access training, seed-funded collaborative projects, and structured fellowships. Outcomes include institutional policy reforms that recognize research contributions as a vital element of library staff career advancement and cross-departmental partnerships addressing digital scholarship, data curation, and other key priorities in library settings. The session highlights strategies for aligning workforce development with organizational priorities, including adaptable funding frameworks and mentorship models for early-career professionals. Presenters will discuss lessons learned from coordinating implementations across distinct academic libraries and sustaining a cultural shift toward research-intensive collaboration.

https://jointpdi.github.io/
https://prolearning.unt.edu/unt/course/course.aspx?catId=130
https://lib.vt.edu/center-for-digital-research-and-scholarship/program/grant-page.html
https://library.unt.edu/deans-innovation-grant/

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/

Washington University (WashU) established the Digital Intelligence and Innovation Accelerator (DI2) to catalyze digital transformation and support researchers in the development and implementation of software and related technologies to support. From the beginning, WashU Libraries have actively partnered with DI2, taking advantage of the library's central position on campus to serve as a 'front door' to digital transformation. This presentation discusses the partnership and presents case studies of three projects that have shaped both organizations' approaches to services. First, the presenters will look at chatbots for discovery. DI2 has an objective to build a state-of-the-art chatbot tool that is fully HIPAA (Health Insurance Portability and Accountability Act) and FERPA (Family Educational Rights and Privacy Act) compliant. The ChatBot Creator project is designed to be user-friendly and highly customizable, and the first beta use case is being developed in partnership with the university library. It is a Digital Resource Hub chatbot that supports the campus community in identifying resources to support their research and learning needs. The second case study will look at a DI2-funded project, based in the libraries, to expand the use of, and access to, open educational resources for the campus. Finally, the presenters will review ongoing efforts to implement a campus-wide research profiling system. Each of these partnerships has been different, but all have resulted in improved services for WashU faculty and students.

OER Project: https://library.wustl.edu/news/new-award-to-transform-learning-through-open-educational-resources/
DI2 overview: https://di2accelerator.wustl.edu/
WashU Libraries: https://library.wustl.edu/

The HBS Knowledge platform is a dynamic knowledge graph designed to enhance the discovery and integration of research, people, and ideas across Harvard Business School (HBS). First launched in June 2021 and rebooted in March 2025, the platform connects structured and unstructured data from heterogeneous sources across HBS, creating a rich network of insights. In the session, the presenters will explore how the graph was built, the ongoing data workflows that keep it up to date, current use cases, and their vision for further development. They will describe the technology stack and main challenges and learnings as they continue to iterate on the platform. With plans to leverage its structure for advanced semantic search and as a foundation for artificial intelligence-driven applications, HBS Knowledge represents a crucial step toward more intelligent and connected academic discovery and resources for administrative departments on campus.