ArticleScientific data2023
Race and ethnicity data for first, middle, and surnames.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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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.
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Who cites it
8 citing papers in PubMed.
- Computed tomography as a solution to skeletal collection limitations: exploring cranial variation in the South African population.International journal of legal medicine · 2026Article
- Reduced age-wise disparity in estimated cervical cancer screening participation rates after applying hysterectomy correction: a population-based cross-sectional study.BMC women's health · 2026Article
- Intersectional biases in narratives produced by open-ended prompting of generative language models.Nature communications · 2026Article
- Enabling disaggregation of Asian American subgroups: a dataset of Wikidata names for disparity estimation.Scientific data · 2025Article
- Inferring Unknown Race in Central Cancer Registries.Journal of registry management · 2025Article
- Contextualized race and ethnicity annotations for clinical text from MIMIC-III.Scientific data · 2024Article
- Loss to Follow-Up in Patients With Proliferative Diabetic Retinopathy or Diabetic Macular Edema.JAMA network open · 2024Article
- Race and ethnicity data for first, middle, and surnames.Scientific data · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We provide the largest compiled publicly available dictionaries of first, middle, and surnames for the purpose of imputing race and ethnicity using, for example, Bayesian Improved Surname Geocoding (BISG). The dictionaries are based on the voter files of six U.S. Southern States that collect self-reported racial data upon voter registration. Our data cover the racial make-up of a larger set of names than any comparable dataset, containing 136 thousand first names, 125 thousand middle names, and 338 thousand surnames. Individuals are categorized into five mutually exclusive racial and ethnic groups - White, Black, Hispanic, Asian, and Other - and racial/ethnic probabilities by name are provided for every name in each dictionary. We provide both probabilities of the form ℙ(race|name) and ℙ(name|race), and conditions under which they can be assumed to be representative of a given target population. These conditional probabilities can then be deployed for imputation in a data analytic task for which self-reported racial and ethnic data is not available.
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Registered trials
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.