CNI Spring 2025 Membership Meeting

Optional orientation session for new attendees (representatives of new member organizations and new representatives or alternate delegates from existing member organizations); guests and presenters are also welcome.

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/

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/

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.