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MicroData Instrument Inc 2019 public use microdata sample (pums) data
2019 Public Use Microdata Sample (Pums) Data, supplied by MicroData Instrument Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Derivative Assay:

Article Title: CMap: a database for mapping job titles, sector specialization, and promotions across 24 sectors.
Article Snippet: Finally, an additional examples/ directory provides examples of integrating CMap with: (a) American Community Survey (ACS) Public Use Microdata Sample (PUMS) data44 (b) LinkedIn job postings45 For a better understanding of the distribution of our data, please refer to Fig. 3a for distributions of title specialization and Fig. 3b for promotion distributions by sector and region. technical Validation In this section, we outline the validation techniques employed to assess and ensure the quality of both parts of our dataset: (i) the job titles, and (ii) the identified promotions. title Cleaning Validation.

Article Title: Advancing extreme heat risk assessments to better capture individually-experienced temperatures: A new approach to describe individual and subgroup vulnerabilities
Article Snippet: We derived indoor and outdoor occupations from 2010 U.S. Census Public Use Microdata Sample (PUMS) data for each city, which contains only a sample of each population and includes age and gender.

Article Title: Changes in Adult Obesity Trends in the US
Article Snippet: These weights were designed to target the joint distribution of these characteristics as reported in the 2020 U.S. Census Public Use Microdata Sample (PUMS) data.

Article Title: Understanding the relationship between health and place: A systematic review of methods to disaggregate data to small areas.
Article Snippet: Background: A systematic review was conducted to examine the extent, range and nature of published research evidence using methods to attribute data reported at aggregate levels to small areas, taken from both the health sciences and geography literatures.. Methods: Four electronic bibliographic health databases were searched (MEDLINE, Embase, PsychINFO and CINAHL) and one human geography database (GEOBASE).. We reviewed titles, abstracts and then full-text articles for their relevance to this review, based on pre-determined exclusion and inclusion criteria.

Article Title: Moving opportunistically: Interstate migration after medicaid expansion.
Article Snippet: The large-scale expansion of Medicaid eligibility following the Affordable Care Act provides an ideal setting to explore whether changes to state-provided health insurance benefits predict migration across state lines.. Using the American Community Survey’s person-level data from 2009 to 2019 about moving during the prior 12 months, we examine whether Medicaid’s selective expansion after the Affordable Care Act prompted inmigration to states that greatly expanded Medicaid eligibility for the populations most targeted by the expansion.. We find that states expanding Medicaid by the largest margin was related to higher in-migration, equating to 0.23 percentage points greater annual population growth in the target population, 0.42 percent for the disabled subpopulation, and 0.25 percent for the parent subpopulation.

Article Title: Inequitable exposure to tobacco product litter among adolescents in California, USA
Article Snippet: Weights developed from the 2021 American Community Survey 5-Year Public Use Microdata Sample files increased generalizability to the geographical, gender, and race/ethnicity distribution of California adolescents aged 12–17 years.

Article Title: CMap: a database for mapping job titles, sector specialization, and promotions across 24 sectors
Article Snippet: Finally, an additional examples/ directory provides examples of integrating CMap with: American Community Survey (ACS) Public Use Microdata Sample (PUMS) data LinkedIn job postings For a better understanding of the distribution of our data, please refer to Fig. for distributions of title specialization and Fig. for promotion distributions by sector and region.

Article Title: A visitor-enriched census in the U.S. cities using large-scale mobile positioning data
Article Snippet: Over the past several decades, both raw census datasets, exemplified by the Public Use Microdata Sample (PUMS) and harmonized datasets, such as the Integrated Public Use Microdata Series (IPUMS) , have been made publicly accessible in electronic format.

Modification:

Article Title: CMap: a database for mapping job titles, sector specialization, and promotions across 24 sectors.
Article Snippet: Finally, an additional examples/ directory provides examples of integrating CMap with: (a) American Community Survey (ACS) Public Use Microdata Sample (PUMS) data44 (b) LinkedIn job postings45 For a better understanding of the distribution of our data, please refer to Fig. 3a for distributions of title specialization and Fig. 3b for promotion distributions by sector and region. technical Validation In this section, we outline the validation techniques employed to assess and ensure the quality of both parts of our dataset: (i) the job titles, and (ii) the identified promotions. title Cleaning Validation.

Article Title: Advancing extreme heat risk assessments to better capture individually-experienced temperatures: A new approach to describe individual and subgroup vulnerabilities
Article Snippet: We derived indoor and outdoor occupations from 2010 U.S. Census Public Use Microdata Sample (PUMS) data for each city, which contains only a sample of each population and includes age and gender.

Article Title: Changes in Adult Obesity Trends in the US
Article Snippet: These weights were designed to target the joint distribution of these characteristics as reported in the 2020 U.S. Census Public Use Microdata Sample (PUMS) data.

Article Title: Understanding the relationship between health and place: A systematic review of methods to disaggregate data to small areas.
Article Snippet: Background: A systematic review was conducted to examine the extent, range and nature of published research evidence using methods to attribute data reported at aggregate levels to small areas, taken from both the health sciences and geography literatures.. Methods: Four electronic bibliographic health databases were searched (MEDLINE, Embase, PsychINFO and CINAHL) and one human geography database (GEOBASE).. We reviewed titles, abstracts and then full-text articles for their relevance to this review, based on pre-determined exclusion and inclusion criteria.

Article Title: Moving opportunistically: Interstate migration after medicaid expansion.
Article Snippet: The large-scale expansion of Medicaid eligibility following the Affordable Care Act provides an ideal setting to explore whether changes to state-provided health insurance benefits predict migration across state lines.. Using the American Community Survey’s person-level data from 2009 to 2019 about moving during the prior 12 months, we examine whether Medicaid’s selective expansion after the Affordable Care Act prompted inmigration to states that greatly expanded Medicaid eligibility for the populations most targeted by the expansion.. We find that states expanding Medicaid by the largest margin was related to higher in-migration, equating to 0.23 percentage points greater annual population growth in the target population, 0.42 percent for the disabled subpopulation, and 0.25 percent for the parent subpopulation.

Article Title: Inequitable exposure to tobacco product litter among adolescents in California, USA
Article Snippet: Weights developed from the 2021 American Community Survey 5-Year Public Use Microdata Sample files increased generalizability to the geographical, gender, and race/ethnicity distribution of California adolescents aged 12–17 years.

Article Title: CMap: a database for mapping job titles, sector specialization, and promotions across 24 sectors
Article Snippet: Finally, an additional examples/ directory provides examples of integrating CMap with: American Community Survey (ACS) Public Use Microdata Sample (PUMS) data LinkedIn job postings For a better understanding of the distribution of our data, please refer to Fig. for distributions of title specialization and Fig. for promotion distributions by sector and region.

Article Title: A visitor-enriched census in the U.S. cities using large-scale mobile positioning data
Article Snippet: Over the past several decades, both raw census datasets, exemplified by the Public Use Microdata Sample (PUMS) and harmonized datasets, such as the Integrated Public Use Microdata Series (IPUMS) , have been made publicly accessible in electronic format.



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The Distribution of Cosine Similarity between Synthetic Population and the American Community Survey Public Use Microdata Sample (ACS PUMS) for the 2462 Public Use Microdata Areas (PUMA).

Journal: Scientific Data

Article Title: A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States

doi: 10.1038/s41597-024-03970-1

Figure Lengend Snippet: The Distribution of Cosine Similarity between Synthetic Population and the American Community Survey Public Use Microdata Sample (ACS PUMS) for the 2462 Public Use Microdata Areas (PUMA).

Article Snippet: In this process, we collected 2022 ACS PUMS data , because this is the only data that uses the latest 2020 Public Use Microdata Areas (PUMA) as the geographical boundaries.

Techniques:

List of primary datasets used for constructing the residential demand models.

Journal: Scientific Data

Article Title: High resolution synthetic residential energy use profiles for the United States

doi: 10.1038/s41597-022-01914-1

Figure Lengend Snippet: List of primary datasets used for constructing the residential demand models.

Article Snippet: Synthetic Populations and Ecosystems of the World (SPEW) , SPEW , is a framework that produces synthetic populations for various countries. We used the open-sourced version of the synthetic population available for the U.S. constructed for the year 2013. The sampled base population is the byproduct of American Community Survey (ACS) Public Use Microdata Sample (PUMS) data. Statistical methods such as Simple Random Sampling (SRS) and Iterative Proportional Fitting (IPF) , are used to estimate joint distributions of population characteristics given their marginal distributions at a small geographic level (e.g. PUMA-level for the U.S.). Data records are available at household level for all of U.S. Descriptors are available for mapping records from PUMS data onto the base synthetic population. .

Techniques: Blocking Assay