Author

Daniel Ebanks

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#small language model Preprint Sep 2026

Geospatial Metadata Improves Discoverability by Connecting Datasets Across Scientific Disciplines

It is found that datasets with more missing metadata receive fewer downstream citations and have fewer resolvable connections to other datasets, suggesting that geographic metadata is a more reliable basis for cross-disciplinary interoperability than keyword vocabularies, which often remain discipline-specific.

Daniel Ebanks, Devika Jain · 0 citations

#artificial intelligence Preprint Sep 2026

Geospatial AI, Dataverse Metadata, and the Study of Place-Based Government

Harvard Dataverse hosts over 150,000 research datasets, but the geographic information those datasets carry is entered as free text by depositors and has never been assembled into a searchable structure. We construct a knowledge graph from the repository's public data and metadata, organizing 102,650 datasets within a...

Daniel Ebanks, Devika Jain · 0 citations