View in EDS

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
FullText Text:
  Availability: 0
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 39474508
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modeling.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Haddock+B%22">Haddock B</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Pletcher+A%22">Pletcher A</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Blair-Stahn+N%22">Blair-Stahn N</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Keyes+O%22">Keyes O</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Kappel+M%22">Kappel M</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Bachmeier+S%22">Bachmeier S</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Lutze+S%22">Lutze S</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Albright+J%22">Albright J</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Bowman+A%22">Bowman A</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Kinuthia+C%22">Kinuthia C</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Burke-Conte+Z%22">Burke-Conte Z</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Mudambi+R%22">Mudambi R</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.<br /><searchLink fieldCode="AU" term="%22Flaxman+A%22">Flaxman A</searchLink>; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101717821%22">Gates open research</searchLink> [Gates Open Res] 2024 Oct 18; Vol. 8, pp. 36. <i>Date of Electronic Publication: </i>2024 Oct 18 (<i>Print Publication: </i>2024).
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Dataset; Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Bill+and+Melinda+Gates+Foundation%22">Bill and Melinda Gates Foundation </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101717821 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>2572-4754 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2225724754%22">25724754 </searchLink><i>NLM ISO Abbreviation: </i>Gates Open Res <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=39474508
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.12688/gatesopenres.15418.2
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 36
    Titles:
      – TitleFull: Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modeling.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Haddock B
      – PersonEntity:
          Name:
            NameFull: Pletcher A
      – PersonEntity:
          Name:
            NameFull: Blair-Stahn N
      – PersonEntity:
          Name:
            NameFull: Keyes O
      – PersonEntity:
          Name:
            NameFull: Kappel M
      – PersonEntity:
          Name:
            NameFull: Bachmeier S
      – PersonEntity:
          Name:
            NameFull: Lutze S
      – PersonEntity:
          Name:
            NameFull: Albright J
      – PersonEntity:
          Name:
            NameFull: Bowman A
      – PersonEntity:
          Name:
            NameFull: Kinuthia C
      – PersonEntity:
          Name:
            NameFull: Burke-Conte Z
      – PersonEntity:
          Name:
            NameFull: Mudambi R
      – PersonEntity:
          Name:
            NameFull: Flaxman A
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 18
              M: 10
              Text: 2024 Oct 18
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 2572-4754
          Numbering:
            – Type: volume
              Value: 8
          Titles:
            – TitleFull: Gates open research
              Type: main
ResultId 1