GREI Data Repository AI Taxonomy
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | |
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| _version_ | 1866915016523907072 |
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| author | Chodacki, John Hanhel, Mark Iacus, Stefano Scherle, Ryan Olson, Eric Pfeiffer, Nici Holmes, Kristi Hosseini, Mohammad |
| author_facet | Chodacki, John Hanhel, Mark Iacus, Stefano Scherle, Ryan Olson, Eric Pfeiffer, Nici Holmes, Kristi Hosseini, Mohammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08054 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GREI Data Repository AI Taxonomy Chodacki, John Hanhel, Mark Iacus, Stefano Scherle, Ryan Olson, Eric Pfeiffer, Nici Holmes, Kristi Hosseini, Mohammad Digital Libraries Artificial Intelligence Machine Learning 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. |
| title | GREI Data Repository AI Taxonomy |
| topic | Digital Libraries Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.08054 |