Evidence map›Paper›PMID 39474508›Full record

ArticleGates open research2024

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

Beatrix Haddock, Alix Pletcher, Nathaniel Blair-Stahn, Os Keyes, Matt Kappel, Steve Bachmeier, Syl Lutze, James Albright, Alison Bowman, Caroline Kinuthia and 3 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Gates open research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Beatrix HaddockInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Alix PletcherInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.ORCID https://orcid.org/0009-0009-1585-4766
Nathaniel Blair-StahnInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Os KeyesInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Matt KappelInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.ORCID https://orcid.org/0000-0003-2430-5661
Steve BachmeierInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Syl LutzeInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.ORCID https://orcid.org/0009-0005-7858-0222
James AlbrightInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Alison BowmanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Caroline KinuthiaInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Zeb Burke-ConteInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Rajan MudambiInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Abraham FlaxmanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.ORCID https://orcid.org/0000-0001-6033-4713

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Entity resolution (ER) is the process of identifying and linking records that refer to the same real-world entity. ER is a fundamental challenge in data science, and a common barrier to ER research and development is that the data fields used for this fuzzy matching are personally identifiable information, such as name, address, and date of birth. The necessary restrictions on accessing and sharing these authentic data have slowed the work in developing, testing, and adopting new methods and software for ER. We recently released Methods: We created the simulated population data available for noising with pseudopeople using our Vivarium simulation platform. Our model simulates individuals and their families, households, and employment dynamics over time, which we observe through simulated censuses, surveys, and administrative data collection systems. Results: Our simulation process produced over 900 gigabytes of simulated censuses, surveys, and administrative data for pseudopeople, representing hundreds of millions of simulants. A sample simulated population of thousands of simulants is now openly available to all users of the pseudopeople package, and large-scale simulated populations of millions and hundreds of millions of simulants are also available by online request through GitHub. These simulated population data are structured for use by the pseudopeople package, which includes additional affordances to add various kinds of noise to the data to provide realistic, sharable challenges for ER researchers.

Indexed as

CensusesComputer SimulationPrivacyAlgorithmsConfidentialityDatasets as TopicFuzzy LogicHumansInternetProgramming LanguagesSoftwareUnited StatesEntity resolution (ER)microsimulation

Identifiers

PMID39474508
PMCPMC11518969

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.