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Vision
Transforming Revolutionize how researchers, students, and the global community access and explore Harvard's extensive collections, while making making all kinds of information easily discoverable and accessible.
Project Goals
- Enhance user experience
- Improve discovery and accessibility of special and archival collections and all types of digital collections including but not limited to image, text, audio, video, born digital, immersive (3d, XR, VR, MR), GIS, etc.
- Integrate distinct digital collections discovery platforms, including developing a new one
- Investigate and use AI-powered tools to enhance user experience and metadata
Problem and Value Statements
Problem Statement
Since its founding, Harvard Library has been a guardian of the University’s memory and a gateway to the world's knowledge. We currently host an array of discovery systems that use different design approaches, organizational priorities, and technology standards. Scholars and the public expect to be able to find trustworthy information and discover resources easily regardless of the system that is managing and providing access to it.
Solution Business Value
By enabling rich cross-collection search, this project will offer end users intuitive, contextual discovery of special collections, archives and digital collections, through a mix of conversational interfaces, browsing that emphasizes the visual nature of materials when appropriate, and recommendations for similar or related resources, all informed by ongoing user research.
Alignment with Harvard Library Multi-Year Goals and Objectives
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- Diversify and expand access to knowledge
Maximize the breadth of tangible and digital collections across Harvard and peer institutions, for the benefit of all partners
Increase our focus on acquiring, accessing, and creating digital content that is accessible to all, as open as possible, and permits creative uses of collections as data
Invest in open access infrastructure and services that support equitable, sustainable models for scholarly communication and open knowledge
Vision
Revolutionize how researchers, students, and the global community access and explore Harvard's extensive collections, making all kinds of information easily discoverable and accessible.
In Scope
- Replace HOLLIS for Images and Harvard Digital Collections, extending their use cases to meet project goals
- Reimagine metadata pipeline
Out of Scope
- Discovery and access to licensed resources (articles, databases), traditional library resources (monographs, etc.)
Deliverables and Work Products
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Sprints | Outcome | Responsible Parties |
|---|---|---|
| Sprint 1 | Gained foundational understanding of back end, and established collaboration practices with each other and other HUIT and LTS colleagues. | Technical Project Team |
| Sprint 2 | Investigated front end frameworks and decided on React, diagramed a draft front end architecture, and "made real" step 3 (semantic retrieval) in order to help begin the front end work. See recording of demo here. | Technical Project Team |
Sprint 3 | Initialize front end development (big win: to work with fastapi for semantic retrieval), finish deploy of semantic retrieval, and experiment with one LLM generative feature and finish indexing the Finding Aids. | Technical Project Team |
Definition of Done
Discovery platform, including access to digital assets, is released on production environment and in use by Harvard constituents and the public.
Stakeholders
Executive Stakeholders | Title |
|---|---|
| Martha Whitehead | VP for Harvard Library and University Librarian |
| Stu Snydman | AUL & Managing Director for Library Technology Services |
| Open | AUL for Discovery and Access |
| Tom Hyry | AUL for Archives and Special Collections |
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Project Constraints
- Scope - Flexible (all types of digital collections depends on unknowns)
- Time - Fixed 3 year project to Reimagine Discoveryproject
- Cost - Fixed 3 year budget
Project Dependencies
- ArcLight implementation project
- Media Presentation Service upgrade
- LibraryCloud
- DRS Futures project
- Rapidly changing LLM industry
Project Risks
Description | Plan | Impact | Owner |
|---|---|---|---|
| Rapidly changing Generative AI space | Build system to be flexible, swap out models easily | Cost, trust | Technical Project Team |
| Library metadata quality is varied and semantic retrieval works with unstructured data | See if metadata fields can help the quality of embeddings | Quality of retrieval | Metadata creators and Technical Project Team |
Acceptance
Accepted by:
Prepared by: Carolyn Caizzi
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