Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

A shift in scientific publishing from paper-based to knowledge-based practices promotes reproducibility, machine actionability and knowledge discovery. This is important for disciplines like social science, as study indicators are often social constructs such as race or education; hypothesis tests are challenging to compare in demographic research due to their limited temporal and spatial coverage; and natural language in research papers is often imprecise and ambiguous. Therefore, we present the MIRA-KG, consisting of: (1) an ontology for capturing social demography research, which links hypotheses and findings to evidence, (2) annotations of papers on health inequality in terms of the ontology, gathered by (i) prompting a Large Language Model to annotate paper abstracts using the ontology, (ii) mapping concepts to terms from NCBO BioPortal ontologies and GeoNames, and (iii) refining the final graph by a set of SHACL constraints, developed according to data quality criteria. The utility of the resource lies in its use for formally representing social demography research hypotheses, discovering research biases, discovery of knowledge, and the derivation of novel questions.This dataset was generated using the code available on Github at https://w3id.org/mira/ at version v1.0. It uses the following ontology: https://w3id.org/mira/ontology/.

Data for the PeerJ manuscript, A simple, sufficient, and consistent method to score the status of threats and demography of imperiled species. Preprint at https://peerj.com/preprints/1860/.Consists of four tab-separated files written directly from the R data.frames used in the analysis:FWS_change_spp.tsv: Threat and demography scores, as well as FWS status metrics, for 52 species in Analysis 1 of the paper. These species were either proposed for status changes by FWS (37 spp.) or were controls (15 spp.).LDA_data.tsv: A reshaped dataset based on FWS_change_spp.tsv, for linear discriminant analysis in Analysis 1.all_FL_spp.tsv: Threat and demography scores for 54 ESA-listed species from Florida, non-plant and FWS with primary jurisdiction.FL_multiperson.tsv: Threat and demography scores for ten randomly selected species in all_FL_spp.tsv, with scores from five different people.

This data set contains the results of a survey about researchers’ experience with and attitudes toward preprinting. The survey respondents are authors of journal articles published in 2021 and 2022 and indexed in the Web of Science database. The questions in the survey are grouped into three sections: experience with preprinting, opinions on preprinting, and demographic questions.

The Word document contains the survey form.

The Excel spreadsheet contains the raw survey data. Free-text responses are not included in the spreadsheet because they may reveal sensitive information.

For more information about this data set, please see the paper "To Preprint or Not to Preprint: Experience and Attitudes of Researchers Worldwide" by Rong Ni and Ludo Waltman. This paper will be published soon.

No description was included in this Dataset collected from the OSF

Datasets used in analysis presented in the pre-print: " "Includes the contigs, anvi’o protein calls, and anvi’o database profiles for the MAGs used in this analysis

We used this dataset to assess the strength of isolation due to geographic and macroclimatic distance across island and mainland systems, comparing published measurements of phenotypic traits and neutral genetic diversity for populations of plants and animals worldwide. The dataset includes 112 studies of 108 species (72 animals and 36 plants) in 868 island populations and 760 mainland populations, with population-level taxonomic and biogeographic information, totalling 7438 records. Methods Description of methods used for collection/generation of data: We searched the ISI Web of Science in March 2017 for comparative studies that included data on phenotypic traits and/or neutral genetic diversity of populations on true islands and on mainland sites in any taxonomic group. Search terms were 'island' and ('mainland' or 'continental') and 'population*' and ('demograph*' or 'fitness' or 'survival' or 'growth' or 'reproduc*' or 'density' or 'abundance' or 'size' or 'genetic diversity' or 'genetic structure' or 'population genetics') and ('plant*' or 'tree*' or 'shrub*or 'animal*' or 'bird*' or 'amphibian*' or 'mammal*' or 'reptile*' or 'lizard*' or 'snake*' or 'fish'), subsequently refined to the Web of Science categories 'Ecology' or 'Evolutionary Biology' or 'Zoology' or 'Genetics Heredity' or 'Biodiversity Conservation' or 'Marine Freshwater Biology' or 'Plant Sciences' or 'Geography Physical' or 'Ornithology' or 'Biochemistry Molecular Biology' or 'Multidisciplinary Sciences' or 'Environmental Sciences' or 'Fisheries' or 'Oceanography' or 'Biology' or 'Forestry' or 'Reproductive Biology' or 'Behavioral Sciences'. The search included the whole text including abstract and title, but only abstracts and titles were searchable for older papers depending on the journal. The search returned 1237 papers which were distributed among coauthors for further scrutiny. First paper filter To be useful, the papers must have met the following criteria: Overall study design criteria: Include at least two separate islands and two mainland populations; Eliminate studies comparing populations on several islands where there were no clear mainland vs. island comparisons; Present primary research data (e.g., meta-analyses were discarded); Include a field study (e.g., experimental studies and ex situ populations were discarded); Can include data from sub-populations pooled within an island or within a mainland population (but not between islands or between mainland sites); Island criteria: Island populations situated on separate islands (papers where all information on island populations originated from a single island were discarded); Can include multiple populations recorded on the same island, if there is more than one island in the study; While we accepted the authors' judgement about island vs. mainland status, in 19 papers we made our own judgement based on the relative size of the island or position relative to the mainland (e.g. Honshu Island of Japan, sized 227 960 km² was interpreted as mainland relative to islands less than 91 km²); Include islands surrounded by sea water but not islands in a lake or big river; Include islands regardless of origin (continental shelf, volcanic); Taxonomic criteria: Include any taxonomic group; The paper must compare populations within a single species; Do not include marine species (including coastline organisms); Databases used to check species delimitation: Handbook of Birds of the World (www.hbw.com/); International Plant Names Index (https://www.ipni.org/); Plants of the World Online(https://powo.science.kew.org/); Handbook of the Mammals of the World; Global Biodiversity Information Facility (https://www.gbif.org/); Biogeographic criteria: Include all continents, as well as studies on multiple continents; Do not include papers regarding migratory species; Only include old / historical invasions to islands (>50 yrs); do not include recent invasions; Response criteria: Do not include studies which report community-level responses such as species richness; Include genetic diversity measures and/or individual and population-level phenotypic trait responses; The first paper filter resulted in 235 papers which were randomly reassigned for a second round of filtering. Second paper filter In the second filter, we excluded papers that did not provide population geographic coordinates and population-level quantitative data, unless data were provided upon contacting the authors or could be obtained from figures using DataThief (Tummers 2006). We visually inspected maps plotted for each study separately and we made minor adjustments to the GPS coordinates when the coordinates placed the focal population off the island or mainland. For this study, we included only responses measured at the individual level, therefore we removed papers referring to demographic performance and traits such as immunity, behaviour and diet that are heavily reliant on ecosystem context. We extracted data on population-level mean for two broad categories of response: i) broad phenotypic measures, which included traits (size, weight and morphology of entire body or body parts), metabolism products, physiology, vital rates (growth, survival, reproduction) and mean age of sampled mature individuals; and ii) genetic diversity, which included heterozygosity,allelic richness, number of alleles per locus etc. The final dataset includes 112 studies and 108 species. Methods for processing the data: We made minor adjustments to the GPS location of some populations upon visual inspection on Google Maps of the correct overlay of the data point with the indicated island body or mainland. For each population we extracted four climate variables reflecting mean and variation in temperature and precipitation available in CliMond V1.2 (Kritikos et al. 2012) at 10 minutes resolution: mean annual temperature (Bio1), annual precipitation (Bio12), temperature seasonality (CV) (Bio4) and precipitation seasonality (CV) (Bio15) using the "prcomp function" in the stats package in R. For populations where climate variables were not available on the global climate maps mostly due to small island size not captured in CliMond, we extracted data from the geographically closest grid cell with available climate values, which was available within 3.5 km away from the focal grid cell for all localities. We normalised the four climate variables using the "normalizer" package in R (Vilela 2020), and we performed a Principal Component Analysis (PCA) using the psych package in R (Revelle 2018). We saved the loadings of the axes for further analyses. References:

Bruno Vilela (2020). normalizer: Making data normal again.. R package version 0.1.0. Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., et al.(2012). CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol., 3, 53--64. Revelle, W. (2018) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.8.12. Tummers, B. (2006). DataThief III. https://datathief.org/

All apart from two rows of these data were originally collated and reported by: García VB, Lucifora LO, Myers RA. 2008. The importance of habitat and life history to extinction risk in sharks, skates, rays and chimaeras. Proceedings of the Royal Society of London, B 275: 83-89. We have added the basking shark Cetorhinus maximus data from: Pauly D. 2002. Growth and mortality of the Basking Shark Cetorhinus maximus and their implications for management of Whale Sharks Rhincodon typus. In Proceedings of the international seminar and workshop, Sabah, Malaysia, July 1997, Fowler SL, Reed TM, Dipper FA (eds). IUCN Species Survival Commission Shark Specialist Group: Gland, Switzerland and Cambridge, UK; 199-208. And data on a genric manta ray (Manta spp.) as reported originally as: Dulvy NK, Pardo SA, Simpfendorfer CA, Carlson JK. 2013. Diagnosing the dangerous demography of manta rays using life history theory. PeerJ PrePrints 1: e162v161. This prepreint has since been published as a paper at PeerJ: Dulvy NK, Pardo SA, Simpfendorfer CA, Carlson JK. 2014. Diagnosing the dangerous demography of manta rays using life history theory. PeerJ 2: e400; doi:10.7717/peerj.400 We urge the readers to read the original papers especialy the Garcia et al. paper to understand how these data were put together.

Files necessary to run the Bayesian multi-state capture-recapture model to analyse the demography of northern royal albatross at Taiaroa Head, New Zealand. The model is used for the article at https://peerj.com/preprints/712/, submitted for peer-reviewed publication. The files consist of: - model.bug - the Bayesian multi-state capture-recapture model, written in the BUG language, - data.txt - Data, in R raw format (readable in R using the source function), - inits.txt - Initialisation values for the model, - jags.cmd - JAGS command file to run the model, containing the names of the parameters to monitor, and to specify the length of the burnin period and of the total number of iterations. To run the model, assuming that JAGS is installed, simply run "jags jags.cmd".

The data consist of 11 objects: - age: matrix of 355 x 23 values being the age in years for all 355 individuals and 23 years. - atcol: matrix of 355 x 23 values, with 1 indicating that an individual was at the colony, 0 otherwise, in each of the 23 years. - bsucc: matrix of 355 x 23 values, with 2 if an individual produced a fledgling, 1 otherwise, for each of the 23 years. - firstcap: vector of 355 values, representing the first year each individual was seen during the studied period, with 1 being the start of the period (1988-89). - k.R: minimum recruitment age, single value. - k.B: minimum age at first reproduction, single value. - N: number of individuals, single value. - sex: vector of 355 values, containing the gender of each individual: 1 for female, 2 for male, NA for unknown. - state: matrix of 355 x 23 values, being the state of each individual each year: 1: breeding adult, 2: non-breeding adult, 3: pre-breeder, 4: juvenile, 5: dead, NA: unknown. - T: number of years considered in the dataset, single value. - Tstar: maximum individual age in the dataset, single value.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in The Economic Benefits of Latino Immigration: How the Migrant Hispanic Population’s Demographic Characteristics Contribute to US Growth, PIIE Working Paper 19-3.

If you use the data, please cite as: Huertas, Gonzalo, and Jacob Funk Kirkegaard. (2019). The Economic Benefits of Latino Immigration: How the Migrant Hispanic Population’s Demographic Characteristics Contribute to US Growth. PIIE Working Paper 19-3. Peterson Institute for International Economics.

Roads affects ecological processes at a range of spatial scales, and studies document both negative and positive effects on natural capital and ecological processes. Still there are too large knowledge gaps and geographic and taxonomic biases to draw conclusions and guide road authorities. We used a systematic review approach to synthesise current knowledge and identify knowledge gaps. We used a broad search string and limited our search to the period 2008 -2018. This gave a total of about 2000 unique papers. Title and abstract screening reduced this to 473 papers where we included studies that estimate, model or review effects of roads on population processes, demography, distribution, occurrence, abundance, biodiversity, behaviour and landscape connectivity. Papers that passed the initial screening criteria were screened on full-text, grouped into themes and explored in more detail to detect patterns related to geography, road types, habitat types, management and responses recorded. This grouping was based on whether papers addressed invasive species; population genetics; edge effects, landscape perspectives; population processes; biodiversity; dispersal and connectivity; roadside construction and management; verges as habitats and resource; ecological traps; roadkill; pollution and ecotoxicology; verges as refugees and conservation approaches; noise; urbanisation; or ecosystem services. These groups of papers were then reviewed using a narrative approach. Biodiversity was addressed in 175 papers as the largest category of papers, mainly with a focus on taxonomic diversity and often within a narrow phylogenetic focus. 130 studies addressed different landscape perspectives, some in combination with details on fragmentation, population genetics, dispersal and connectivity. The majority of papers on movement and dispersal, however, addressed processes at smaller spatial scales. A good number of papers (124) explored the role of road verges as habitats or providers of resources. In a subset of the papers, this was linked to the role of roadsides as refuges or their role in conservation. There were over 100 papers addressing demography and population processes involving a broad range of measured or estimated effects and only a few in combination with population genetics. Edge effects were also rather well described for a wide range of organisms (108 papers) sometimes linked to abiotic explanatory variables. We found that, despite strong negative effects of processes and factors such as noise, barrier effects, vehicle collisions and landscape fragmentation, HTI can contain considerable biological diversity and species richness, contribute to structural and resource heterogeneity in the landscape, and function as corridors for a diverse set of organisms. However, evidence of these contributions is fragmented, with high species-specificity in responses (especially in animals), but also a strong impact of the landscape configuration and resources. A shift in focus from species occurrence to processes and functions at population and community level; addressing the importance of connectivity towards and away from the roads, integrating habitats along roads in a larger landscape; and approaches to identify and prioritise critical components and trade-offs during road construction and maintenance are among the major knowledge gaps to be addressed in future research.

This dataset contains the results from studies of the Elephant Seal (Mirounga leonina) at Macquarie Island. Results from branding surveys and photographs from 1985 onwards are reported. Numbers, life stage, sex, moult stage and migration patterns have been reported. Currently some 2000 pups a year are branded and the dataset includes birth dates, weights at birth and weaning and at 6, 12 and 18 months.

This work was completed as part of ASAC (AAS) project 2265 (ASAC_2265).

Objectives:

1. To prepare research papers, from the extensive southern elephant seal dataset, that deal with key demographic parameters of the population such as size, age specific survivorship, fecundity, recruitment into the breeding population, age specific growth rates, and intrinsic rate of change of the population. In addition, later papers will investigate interannual variability in these parameters, how these relate to changing environmental conditions, and the effects of this on long term population fluctuations.

2. To analyse and compare stable isotope ratios in the facial vibrissae of the seals and the hard parts of their prey to determine the geographical positions of the major foraging grounds of the seals. The isotope values will also allow the food webs, that support the seals, to be better defined.

3. To measure the growth rates of elephant seal vibrissae so that changing isotope values, related to the prey and foraging areas, can be referred to particular foraging periods. Elephant seals characteristically have two separate periods of foraging: one in summer and one in winter. The positions of these episodes on a vibrissa can be identified once the growth rates of vibrissae are known.

Taken from the progress report for the 2009-2010 season:

Progress against objectives: 1. One paper published from the elephant seal dataset. Two papers also published during 2009/10 using data collected opportunistically during the life of this project.

1. PhD student Andrea Walters continues to analyse the results of the whisker analyses. She has presented some of her results at the AMSA 2009 marine connectivity conference in Adelaide.

An honours student has been engaged (start date March 2010) to analyse the squid component of the seals' diet.

1. John van den Hoff spent the early summer at Macquarie Island finalising the collection of the demographic data. 2154 tag/brand resights were recorded. Collection of the data has continued on the island by Chris Oosthuizen, Ben Arthur and Iain Field since John returned to Australia. When those field workers return data collection will cease.

To evaluate the evidence linking perceived risk from humans and associated phenotypic responses to downstream ecological consequences, we comprehensively reviewed the literature on human-induced NCEs and TMIEs. Papers evaluated in our comprehensive review were identified from three sources: 1) two Web of Science searches; 2) papers citing Frid and Dill (2002; https://doi.org/10.5751/ES-00404-060111*), 3) relevant papers found within review papers identified from (1) and (2). The specifics of the Web of Science searches are provided below. We initially scanned all papers for three criteria in a progressive manner; to advance, each paper had to be empirical, examine an effect of anthropogenic disturbance, and reference a topic related to risk. Papers meeting all three criteria were further filtered to those that evaluated a human-induced risk effect and tested for an effect beyond a phenotypic response (i.e. a change in fitness, fecundity, survival, density, abundance, or population...

About

Dataset provided for NeuroLibre preprint.

❤️ Living preprint: https://preprint.neurolibre.org/10.55458/neurolibre.00035

Author repo: https://github.com/evaalonsoortiz/QC-imaging-demographics
NeuroLibre fork: https://github.com/roboneurolibre/qc-imaging-demographics

For details, please visit the corresponding NeuroLibre technical screening.

https://neurolibre.org

✉️ info@neurolibre.org

This dataset is made up of images containing handwritten 3-digit occupation codes from the Norwegian population census of 1950. The occupation codes were added to the census sheets by Statistics Norway after the census was concluded for the purpose of creating aggregated occupational statistics for the entire population. The coding standard used in the 1950 census is, according to Statistics Norway’s official publications (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1950, booklet 4, page 81), very similar to the standards used in the census for 1920. Cf. the 13th booklet published for the 1920 census (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1920, note that this booklet is only available in Norwegian). In short, an occupation code is a 3-digit number that corresponds to a given occupation or type of occupation. According to the official list of occupation codes provided by Statistics Norway there are 339 unique codes. These are not all necessarily sequential or hierarchical in general, but some subgroupings are. This list can be found under Files. It is also worth noting that these images were extracted from the original census sheet images algorithmically. This process was not flawless and lead to additional images being extracted, these can contain written occupation titles or be left entirely blank. The dataset consists of 90,000 unique images, and 9,000 images that were randomly selected and copied from the unique images. These were all used for a research project (link to preprint article: https://doi.org/10.48550/arXiv.2306.16126) where we (author list can be found in preprint) tried to find a more efficient way of reviewing and correcting classification results from a Machine Learning model, where the results did not pass a pre-set confidence threshold. This was a follow-up to our previous article where we describe the initial project and creating of our model in more detail, if it is of interest (“Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes”, https://doi.org/10.51964/hlcs11331).

SNP variant counts by mutation rate, described in "Recurrent mutation in the ancestry of a rare variant" by John Wakeley, Wai-Tong (Louis) Fan, Evan Koch and Shamil Sunyaev (preprint available at https://doi.org/10.1101/2022.08.18.504427) and generated using the Roulette method in "A mutation rate model at the basepair resolution identifies the mutagenic effect of Polymerase III transcription" by Seplyarskiy et al (preprint available at https://doi.org/10.1101/2022.08.20.504670). "mu" is the mutation rate inferred using Roulette, and "n" is the number of sites with variant count "AC_nfe" in gnomAD v2.1.1 data for ~57K non-Finnish European samples.

Description of the terms under methods extracted from the reviewed papers reported in Fig 2.

Time periods include Old (1990–2000) and New (2010–2012), with dates of each time period indicated (number of papers identified using search terms and number we reviewed in parenthesis). Search terms for topics are in addition to the search terms for dispersal (see text).

Associations between demographic variables and conference papers among BSSR scientists employed in tenure-track or tenured positions.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Workers include members of the Armed Forces and civilians who were at work last week..Industry titles and their 4-digit codes are based on the 2017 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..Several means of transportation to work categories were updated in 2019. For more information, see: Change to Means of Transportation..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..Beginning in data year 2019, respondents to the Weeks Worked question provided an integer value for the number of wee...