Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modeling.

Saved in:
Bibliographic Details
Title: Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modeling.
Authors: Haddock B; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Pletcher A; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Blair-Stahn N; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Keyes O; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Kappel M; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Bachmeier S; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Lutze S; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Albright J; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Bowman A; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Kinuthia C; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Burke-Conte Z; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Mudambi R; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA., Flaxman A; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Source: Gates open research [Gates Open Res] 2024 Oct 18; Vol. 8, pp. 36. Date of Electronic Publication: 2024 Oct 18 (Print Publication: 2024).
Publication Type: Dataset; Journal Article
Journal Info: Publisher: Bill and Melinda Gates Foundation Country of Publication: United States NLM ID: 101717821 Publication Model: eCollection Cited Medium: Internet ISSN: 2572-4754 (Electronic) Linking ISSN: 25724754 NLM ISO Abbreviation: Gates Open Res Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2572-4754
DOI:10.12688/gatesopenres.15418.2