Table of Contents
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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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- Cross collection search for special and archival collections, including digital content using focusing on the end user experience and making clear the relationships between archival objects/items and larger collections
- Incorporate AI/ML technologies to offer natural language search, and generative AI features like summarization, while retaining baseline search and ability to browse Replace functions
- Access to digital content, and act as a replacement HOLLIS for Images and Harvard Digital Collections, extending their use cases to meet project goals: full text searching, born digital, GIS, A/V
- Reimagine metadata pipeline using new technologies from AI/ML
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- Discovery and access to licensed resources (articles, databases) , traditional library resources (monographs, etc.)and general collections
Deliverables and Work Products
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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 |
| Unexpected changes to other library systems like Aeon, JSTOR Forum | Account for and expect changes from external systems in design of data pipeline | Timeline delays | Technical Project Team |
| Staff capacity to support work of the project | Meeting weekly with stakeholders to ensure there is enough time to plan for bouts of work that include time from broader staff | Overall project success | Library Stakeholders |
Acceptance
Accepted by: Library Stakeholders August 8 2024
Prepared by: Carolyn Caizzi
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