Finalizing adding finding aids, designing for how to display heirarchical items from Finding Aids and CURIOSity. Add in CURIOSity items to index, add in document type to eyebrow on front end. Confirm LLM selection and system prompts through team testing. Finalize front end in preparation for release to QA in Sprint 7.
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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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Team Member | Title | Project Role(s) |
|---|---|---|
| Enrique Diaz | Manager of Library Software Engineering | Product Owner (LTS) |
| Doug Simon | Senior Digital Library Software Engineer | Developer (LTS) |
| JJ Chen | Digital Library Data Engineer | Developer (LTS) |
| Maura Meagher | Associate UX Developer | Developer (LTS) |
| Carolyn Caizzi | Senior IT Project Manager | Project Manager/ Scrum Lead (LTS) |
| Meg McMahon | UX Researcher | UX Researcher/Designer (HL) |
Estimated Schedule
Note: Project is managed by using the Scrum framework and these phases/milestones will be adjusted. Below is a tentative schedule.
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a high level schedule. See more detailed view of project tasks here.
Phase | Phase Start | Phase End | Completion Milestone |
|---|---|---|---|
| 1 | July 2024 | September 2024 | Natural language discovery platform with generative AI features for discovering digitized, special and archival collections is built and released to QA for testing. |
| 2 | October 2024 | December 2024 | Platform is tested by end users and improvements are recommended. Research into scaling platform for production is completed. Data pipeline is scoped and work begins. Design process for digitized collections (images) component is completed. |
| 3 | January 2025 | March 2025 | Data pipeline and digitized collections components begin to be built. Decision to soft launch discovery platform is made depending on data pipeline. |
| 4 | April 2025 | June 2025 | Cont. building data pipeline and digitized collections components. Platform is monitored for costs and analytics are gathered and reviewed to plan for full launch September 2025. |
| 5-12 | Years 2-3 will build out full text search integration, more types of digital collection discovery, and access, as well as continuously improve the platform. Investigation into and possible rollout of workflows for using AI to improve quality of metadata. |
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