GREI Data Repository AI Taxonomy
John Chodacki (California Digital Library), Mark Hanhel (figshare),, Stefano Iacus (Dataverse), Ryan Scherle (Dryad), Eric Olson (Center for Open, Science), Nici Pfeiffer (Center for Open Science), Kristi Holmes (Zenodo),, Mohammad Hosseini (Zenodo)
TL;DR
This paper introduces a comprehensive AI taxonomy for data repositories, aiming to standardize AI integration across various repository management roles to improve workflow efficiency and consistency.
Contribution
It presents a novel structured AI taxonomy specifically designed for data repository roles, facilitating better AI adoption and management in repository ecosystems.
Findings
Developed a detailed AI taxonomy for repository roles
Provides a structured framework for AI integration in repositories
Aims to guide AI implementation across repository workflows
Abstract
The Generalist Repository Ecosystem Initiative (GREI), funded by the NIH, developed an AI taxonomy tailored to data repository roles to guide AI integration across repository management. It categorizes the roles into stages, including acquisition, validation, organization, enhancement, analysis, sharing, and user support, providing a structured framework for implementing AI in repository workflows.
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Taxonomy
TopicsData Mining Algorithms and Applications