Papers for

government data teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Linking german parliament speakers to wikidata reveals metadata gaps

Linking Speakers of the German Parliament to Wikidata: Scope and Coverage of Metadata

Abstract: This paper links all individuals who spoke in the German Bundestag between 1949 and 2021 to Wikidata, creating a longitudinal dataset that connects parliamentary speech transcripts with structured biographical metadata. We evaluate the coverage, composition, and potential biases of the retrieved properties and statements, with particular attention to gender, professional background, historical legacies, and transnational dimensions such as place of birth, languages, and foreign awards. The results demonstrate both the analytical potential of combining GermaParl with Wikidata and the importance of critically assessing uneven metadata coverage in open knowledge graphs.

Wed 16 SeptDatabasesSocial and Information Networks
The gist
This paper connects all people who spoke in the German parliament from 1949 to 2021 with detailed information on Wikidata, like their background and awards. The authors look at how complete and balanced this information is, focusing on areas like gender and birthplace. They show that combining these datasets can help analyze parliamentary history but also point out that some information is uneven or missing. This warns users to be careful when using open data for research or analysis.
Open → 2609.18289v1

Knowledge graph links geospatial data to study place-based government

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

Abstract: 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 215,985-node network of 528,003 edges linking datasets to keywords, publications, subjects, journals, and locations. Of those datasets, 43,991 (42.9 percent) carry at least one geospatial field, geographic coverage, geographic unit, or a bounding box and 96.9 percent of all nodes sit in a single connected component, so datasets remain reachable from one another even when their geospatial metadata share nothing in common. A conservative keyword search identifies 7,654 geospatially tagged datasets (17.4 percent) as directly policy-relevant, with elections and legislatures the largest cluster, followed by government administration, health policy, transportation, and education. Five datasets illustrate how this metadata behaves across policy domains and spatial scales, and an extended use case shows how community language models, stance detection with geographic aggregation, and partisan language bridging tools can attach discourse to place. The central obstacle is place resolution: the same location appears as many disconnected nodes. We argue that the graph provides a concrete setting for developing AI-driven metadata enrichment and entity resolution, and we document its coverage skew toward American, city-level data.

Thu 10 SeptArtificial Intelligence
The gist
Many research datasets at Harvard Dataverse include location information, but this data is often just typed in without a clear structure, making it hard to search by place. The authors built a big network that connects these datasets with topics, places, and publications to make them easier to explore together. They found that most datasets are connected in one large group, even if their location details differ. The study also shows challenges like different names for the same place and points out that most data focus on U.S. cities. This work sets the stage for using AI to improve location data and link discussions to specific places.
Open → 2609.11674v1