Case Study 1 — First Verified Demonstration of Machine-Readable Data Publication for LLM Retrieval (2025)
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2025
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| _version_ | 1866901745408409600 |
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| author | NorthsteadAware |
| author_facet | NorthsteadAware |
| contents | <p>This case study documents the first publicly verified demonstration that research data can be structured, published, and permanently timestamped in a form that large-language models (LLMs) can recognise and cite. The verification shows that open repositories and standard metadata—without proprietary infrastructure—are sufficient to achieve machine readability, discoverability, and provenance integrity at practical scale. The work pioneers post-institutional data publishing, establishing that individuals and small teams can meet the same technical standards historically associated with large institutions, while remaining fully transparent and reproducible.</p> <p>**Version 2 (November 2025):** Updated to include final Figshare and GitHub DOIs in the *Verification Details* section. <br>All other content remains identical to Version 1.</p> <p>**Website:** [https://northsteadaware.com](https://northsteadaware.com)</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17575090 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Case Study 1 — First Verified Demonstration of Machine-Readable Data Publication for LLM Retrieval (2025) NorthsteadAware llm citation machine-readable data post-institutional publishing cross-repository verification data provenance AI research NorthsteadAware <p>This case study documents the first publicly verified demonstration that research data can be structured, published, and permanently timestamped in a form that large-language models (LLMs) can recognise and cite. The verification shows that open repositories and standard metadata—without proprietary infrastructure—are sufficient to achieve machine readability, discoverability, and provenance integrity at practical scale. The work pioneers post-institutional data publishing, establishing that individuals and small teams can meet the same technical standards historically associated with large institutions, while remaining fully transparent and reproducible.</p> <p>**Version 2 (November 2025):** Updated to include final Figshare and GitHub DOIs in the *Verification Details* section. <br>All other content remains identical to Version 1.</p> <p>**Website:** [https://northsteadaware.com](https://northsteadaware.com)</p> |
| title | Case Study 1 — First Verified Demonstration of Machine-Readable Data Publication for LLM Retrieval (2025) |
| topic | llm citation machine-readable data post-institutional publishing cross-repository verification data provenance AI research NorthsteadAware |
| url | https://doi.org/10.5281/zenodo.17575090 |