Austria Exports of automatic data processing machines, magnetic or optical readers to Laos was US$17.9 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Austria Exports of automatic data processing machines, magnetic or optical readers to Laos - data, historical chart and statistics - was last updated on June of 2025.

The continuous growth of the global human population results in increased use and change of landscapes, with infrastructures like transportation or energy facilities, being a particular risk to large carnivores. Environmental Impact Assessments were established to identify the probable environmental consequences of any new proposed project, find ways to reduce impacts, and provide evidence to inform decision making and mitigation. Portugal has a wolf population of around 300 individuals, designated as an endangered species with full legal protection. They occupy the northern mountainous areas of the country which has also been the focus of new human infrastructures over the last 20 years. Consequently, dozens of wolf monitoring programs have been established to evaluate wolf population status, to identify impacts, and to inform appropriate mitigation or compensation measures. We reviewed Portuguese wolf monitoring programs to answer four key questions: do wolf programs examine adequate biological parameters to meet monitoring objectives? is the study design suitable for measuring impacts? are data collection methods and effort sufficient for the stated inference objectives? and do statistical analyses of the data lead to robust conclusions? Overall, we found a mismatch between the stated aims of wolf monitoring and the results reported, and often neither aligns with the existing national wolf monitoring guidelines. Despite the vast effort expended and the diversity of methods used, data analysis makes almost exclusive use of relative indices or summary statistics, with little consideration of the potential biases that arise through the (imperfect) observational process. This makes comparisons of impacts across space and time difficult and is therefore unlikely to contribute to a general understanding of wolf responses to infrastructure-related disturbance. We recommend the development of standardized monitoring protocols and advocate for the use of statistical methods that account for imperfect detection to guarantee accuracy, reproducibility, and efficacy of the programs. Methods We reviewed all major wolf monitoring programs developed for environmental impact assessments in Portugal since 2002 (Table S1, Supplementary material). Given that the focus here is on the adequacy of targeted wolf monitoring for delivering conclusions about the effects of infrastructure development, we reviewed only monitoring programs that were specifically designed for wolves and not those concerned with general mammalian assessment. The starting point was a compilation from the 2019-2021 National Wolf Census (Pimenta et al., 2023), where every wolf monitoring program that occurred between 2014 and 2019 in Portugal was identified. The list was completed with projects that started before 2014 or after 2019 based on personal knowledge, inquires to principal scientific teams, governmental agencies, and EIA consultants. Depending on duration, wolf monitoring programs can produce several, usually annual, reports that are not peer-reviewed and do not appear on standard search engines (e.g., Web of Science or Google Schoolar) but are publicly available from the Portuguese Environmental Agency (APA – www.apambiente.pt). We conducted an online search on APA´s search engine (https://siaia.apambiente.pt/) and identified a total of 30 projects. For each of these projects, we were interested in the first and the last report to identify any methodological changes. If the last report was not present, we reviewed the most recent one. If no report was present, we requested it from the team responsible. Our investigation centred on characterizing and quantifying four components of wolf monitoring programs that are interlinked and that should be ideally determined by the initial objectives: (1) biological parameters, i.e., what wolf parameters were studied to assess impacts; (2) study design, i.e., what sampling schemes were followed to collect and analyse data; (3) data collection, i.e., which sampling methodology and how much effort was used to collect data; and (4) data analysis, i.e., how data were analysed to estimate relevant parameters and assess impact. Biological parameters were identified and classified under two categories: occurrence and demography, which broadly correspond to the necessary inputs to assess impacts like exclusion effect and changes in reproductive patterns. Occurrence-related parameters refer to variables used to measure the presence or absence of wolves, whereas demographic parameters refer to variables that intend to measure population-level effects such as abundance, density, survival, or reproduction. We also recorded whether any effort was made to quantify prey population distribution or abundance as recommended in the guidelines. For study design, we reviewed the sampling design of the project, with specific focus on the spatial and temporal aspect of the study such as total area surveyed, the definition of a sampling site within this region (i.e., resolution), the duration of the study and the number of sampling seasons. The goal here was to determine whether the sampling scheme used was appropriate for assessing infrastructure impacts on wolf distribution or demography, depending on what the focus was. For data collection, we identified the main data collection methodologies used and the corresponding sampling effort. By far the most frequent method used is sign surveys, and specifically scat surveys, and for these studies we recorded whether genetic identification of species or individuals based on faecal DNA was attempted. We compare how sampling effort varies by the various inference objectives and, as above, assess which, if any, project or data collection approach is most likely to produce evidence of impact. We divided the Analysis component into two groups: single-year and multi-year analyses. For single-year analysis we identified how monitoring projects used data to make inferences about the state biological parameters of interest and discuss the associated strengths and weaknesses. For multi-year analyses, we recorded how differences or trends were quantified and associated with infrastructure impacts, commenting on the statistical robustness of the analyses used across the projects.

Key Table Information.Table Title.Selected Sectors: Sales, Value of Shipments, or Revenue Size of Firms for the U.S.: 2022.Table ID.ECNSIZE2022.EC2200SIZEREVFIRM.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Economic Census: Establishment and Firm Size Statistics for the U.S..Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2025-04-24.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesOperating expenses ($1,000)Total inventories, beginning of year ($1,000)Total inventories, end of year ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. levels only. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for all economic census sectors except Agriculture (11) and Management of Companies and Enterprises (55). For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector00/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction ar...

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Ethnicity for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB00MYNESD01B.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Ethnicity Hispanic Equally Hispanic/non-Hispanic Non-Hispanic Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the...

United States PPI: Mfg: CE: SO: OE: Secondary Products data was reported at 112.611 Dec2003=100 in Apr 2025. This records an increase from the previous number of 112.610 Dec2003=100 for Mar 2025. United States PPI: Mfg: CE: SO: OE: Secondary Products data is updated monthly, averaging 92.100 Dec2003=100 from Dec 2003 (Median) to Apr 2025, with 230 observations. The data reached an all-time high of 112.611 Dec2003=100 in Apr 2025 and a record low of 90.700 Dec2003=100 in Nov 2016. United States PPI: Mfg: CE: SO: OE: Secondary Products data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Manufacturing: Computer and Electronic Products.

United States PPI: Mfg: TE: OE: BP: Primary Products (PP) data was reported at 172.487 Dec1984=100 in Apr 2025. This records an increase from the previous number of 172.440 Dec1984=100 for Mar 2025. United States PPI: Mfg: TE: OE: BP: Primary Products (PP) data is updated monthly, averaging 137.600 Dec1984=100 from Dec 1984 (Median) to Apr 2025, with 485 observations. The data reached an all-time high of 172.487 Dec1984=100 in Apr 2025 and a record low of 98.700 Dec1984=100 in Jul 1985. United States PPI: Mfg: TE: OE: BP: Primary Products (PP) data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Manufacturing: Transportation Equipment.

Graph and download economic data for Civilian Labor Force in Baker County, OR (LAUCN410010000000006A) from 1990 to 2024 about Baker County, OR; OR; civilian; labor force; labor; household survey; and USA.

Graph and download economic data for Unemployed Persons in Jefferson County, OR (LAUCN410310000000004) from Jan 1990 to May 2025 about Jefferson County, OR; OR; household survey; unemployment; persons; and USA.

Chile IPI: Weights: Mfg: OE: Building of Ships & Boats data was reported at 0.620 % in 2024. This stayed constant from the previous number of 0.620 % for 2023. Chile IPI: Weights: Mfg: OE: Building of Ships & Boats data is updated yearly, averaging 0.620 % from Dec 2018 (Median) to 2024, with 7 observations. The data reached an all-time high of 0.620 % in 2024 and a record low of 0.620 % in 2024. Chile IPI: Weights: Mfg: OE: Building of Ships & Boats data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.B003: Industrial Production Index: 2018=100: Weights: National Institute of Statistics.

How did the data collection work?
The goal of the study is to compare the performance on data analysis tasks when using the data analysis tool eyenalyzer or others. Hence, participants are randomly assigned to one of two groups (i.e., with and without eyenalyzer). All participants work on the same six data analysis tasks - according to their group assignment either with the tool eyenalyzer or with any other tool apart from it. After a maximum of two hours, the participants hand in their solutions to the tasks as well as a self-report of the time taken to complete each task (in minutes), the tooling used for each task (as list), and the estimated difficulty of each task (on a 7-point Likert scale). Finally, they fill out a questionnaire about their demographic data (i.e. age, gender, and previous experience). The submitted solutions are later graded in terms of correctness. Further details on the procedure can be found in this conference paper.

What data is provided?
In this repository you can find both the data obtained from 20 participants (i.e., file data.xlsx) and a german replication package (i.e., folder material.zip).

1. The data file data.xlsx comprises 13 columns and 120 rows. Each row represents one combination of participant and task. The columns are:
   • participant the current participant as a unique identifier from {P01, ..., P20}
   • independent_variable the group assignment of the participant as one of {eyenalyzer, others}
   • task the current task as one of {T1, ..., T6}
   • score the grading of the current task for the current participant as one of {not completed, wrong, borderline, acceptable, correct}
   • time time used by the current participant for the current task in minutes
   • difficulty difficulty estimate of the current participant for the current task as one of {very difficult, difficult, rather difficult, indecisive, rather easy, easy, very easy}
   • tools_used listing of tooling used for the current task if the current participant belongs to the group without eyenalyzer
   • age age of the current participant in years
   • gender gender of the current participant as one of {male, female}
   • experience_programming previous experience of the current participant in programming as one of {none, little, indecisive, some, much}
   • experience_data_science previous experience of the current participant in data science as one of {none, little, indecisive, some, much}
   • experience_experimental_research previous experience of the current participant in experimental research as one of {none, little, indecisive, some, much}
   • experience_statistics previous experience of the current participant in statistics as one of {none, little, indecisive, some, much}
2. The replication folder material.zip comprises three subfolders with the following material:
   • group_with_eyenalyzer/primer.pdf information material on statistics provided to the group with eyenalyzer
   • group_with_eyenalyzer/tasks.pdf the instructions provided to the group with eyenalyzer
   • group_without_eyenalyzer/primer.pdf information material on statistics provided to the group without eyenalyzer
   • group_without_eyenalyzer/tasks.pdf the instructions provided to the group without eyenalyzer
   • both_groups/data/ a directory containing the 4 data files needed to complete the tasks (based on the data from this repository)
   • both_groups/questionnaire.pdf a transcription of the demographic questionnaire

Do you have further questions?
For more information, please feel free to contact

Austria Exports of automatic data processing machines, magnetic or optical readers to Aruba was US$1.87 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Austria Exports of automatic data processing machines, magnetic or optical readers to Aruba - data, historical chart and statistics - was last updated on July of 2025.

Graph and download economic data for Civilian Labor Force in Union County, OR (ORUNIO1LFN) from Jan 1990 to Apr 2025 about Union County, OR; OR; civilian; labor force; labor; and USA.

About this dataset

Context

The dataset tabulates the population of Gold Beach by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Gold Beach across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 50.75% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gold Beach is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Gold Beach total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gold Beach Population by Race & Ethnicity. You can refer the same here

How did the data collection work?
The goal of the study is to get insights to the usability of the data analysis tool eyenalyzer. Hence the participants complete 6 data analysis tasks using eyenalyzer, while their eye movements are recorded by eye tracking. Note that the tasks are the same as in this conference paper and the corresponding data repository, but are broken down into 13 sub-tasks; each task targets one action page in eyenalyzer. After each task, the participants rate the difficulty of the task (on a 7-point Likert scale); after completing all tasks, they fill out a questionnaire on their demographic data as well as the two validated questionnaires System Usability Scale (SUS) and User Experience Questionnaire (UEQ). The submitted solutions are later checked for correctness; the answers to SUS and UEQ are transformed into scores. The manual rewatching of the eye tracking recordings gives the navgiation methods applied for switchting between the tasks or action pages of eyenalyzer as well as the handling of dialog boxes and info fields (i.e. to what extent they are read and whether or not the trigger a positive reaction or not). Further details on the procedure will be found in a soon to be published conference paper.

What data is provided?
In this repository you can find both the data obtained from 41 participants (i.e., folder data.zip) and a german replication package (i.e., folder material.zip).

1. The data file data/performance.xlsx comprises 9 columns and 533 rows. Each row represents one combination of participant and sub-task. The columns are:
   • participant the current subject as a unique identifier from {P01, ..., P41}
   • task the current sub-task as one of {1A, 1B, 1C, 2A, 2B, 3A, 3B, 3C, 4A, 4B, 5A, 5B, 6}
   • score the grading of the current task for the current participant as one of {not completed, wrong, borderline, acceptable, correct}
   • time time used by the current participant for the current task in milliseconds
   • clicks the number of mouse clicks used by the current participant for the current task
   • keys the number of keys pressed on the keyboard by the current participant for the current task
   • difficulty difficulty estimate of the current participant for the current task as one of {very difficult, difficult, rather difficult, indecisive, rather easy, easy, very easy}
2. The data file data/questionnaire.xlsx comprises 18 columns and 41 rows. Each row belongs to one participant. The columns are:
   
   • participant the current subject as a unique identifier from {P01, ..., P41}
   • age age of the current participant in years
   • gender gender of the current participant as one of {male, female}
   • experience_programming previous experience of the current participant in programming as one of {none, little, indecisive, some, much}
   • experience_data_science previous experience of the current participant in data science as one of {none, little, indecisive, some, much}
   • experience_experimental_research previous experience of the current participant in experimental research as one of {none, little, indecisive, some, much}
   • experience_statistics previous experience of the current participant in statistics as one of {none, little, indecisive, some, much}
   • background the occupation of the current participant as one of {bachelor student, master student, phd student, job}
   • field the field of study or work of the current participant as one of {social sciences, computer science and mathematics, technology and engineering, economy and law, art and design and music, other}
   • SUS the SUS-score obtained by the current participant
   • attractiveness the score on the UEQ-scale attractiveness obtained by the current participant
   • perspicuity the score on the UEQ-scale perspicuity obtained by the current participant
   • novelty the score on the UEQ-scale novelty obtained by the current participant
   • stimulation the score on the UEQ-scale stimulation obtained by the current participant
   • dependability the score on the UEQ-scale dependability obtained by the current participant
   • efficiency the score on the UEQ-scale efficiency obtained by the current participant
   • pragmatic_quality the mean score of the pragmatic UEQ-scales for the current participant (i.e. perspicuity, dependability, and efficiency)
   • hedonic_quality the mean score of the pragmatic UEQ-scales for the current participant (i.e. stimulation and novelty)
3. The data file data/navigation_method.xlsx comprises 3 columns and 246 rows. Each row represents one combination of participant and task. The columns are:
   • participant the current participant as a unique identifier from {P01, ..., P41}
   • task the current task as one of {T1, T2, T3, T4, T5, T6}
   • method the navigation method used by the current participant to switch to the proper action page for the current task as one of {navigation bar, landing page link}
4. The data file data/dialog_boxes.xlsx comprises 5 columns and 546 rows. Each row belongs to one dialog box triggering. The columns are:
   • participant the current participant as a unique identifier from {P01, ..., P41}
   • task the current sub-task as one of {1A, 1B, 1C, 2A, 2B, 3A, 3B, 3C, 4A, 4B, 5A, 5B, 6}
   • id the triggered dialog box as a unique identifier from {1, ..., 20}
   • reading the degree of reading of the current dialog box by the current participant as one of {yes, partial, no}
   • reaction the reaction on the current dialog box by the current participant as one of {good, bad, ignored, consecutive}
5. The data file data/info_fields.xlsx comprises 5 columns and 166 rows. Each row belongs to one info field triggering. The columns are:
   • participant the current participant as a unique identifier from {P01, ..., P41}
   • task the current sub-task as one of {1A, 1B, 1C, 2A, 2B, 3A, 3B, 3C, 4A, 4B, 5A, 5B, 6}
   • id the triggered dialog box as a unique identifier from {1, ..., 48}
   • reading the degree of reading of the current info field by the current participant as one of {yes, partial, no}
   • reaction the reaction on the current info field by the current participant as one of {good, bad, irrelevant}
6. The replication folder material.zip comprises three files and one subfolder with the following material:
   
   • primer.pdf information material on statistics provided to the participants
   • stimuli.pdf the stimuli used in the eye tracking set up (calibration and the work with eyenalyzer is denoted by dummy slides)
   • data/ a directory containing the 4 data files needed to complete the tasks (based on the data from this repository)
   • questionnaire.pdf a transcription of the demographic questionnaire

Do you have further questions?
For more information, please feel free to contact

China RE: Computer, Software & Appliance: OE Distribution data was reported at 0.012 RMB bn in 2001. This records an increase from the previous number of 0.008 RMB bn for 2000. China RE: Computer, Software & Appliance: OE Distribution data is updated yearly, averaging 0.009 RMB bn from Dec 1999 (Median) to 2001, with 3 observations. The data reached an all-time high of 0.012 RMB bn in 2001 and a record low of 0.008 RMB bn in 2000. China RE: Computer, Software & Appliance: OE Distribution data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJD: Retail Enterprise: Computer, Software and Assistant Appliance Retail.

About this dataset

Context

The dataset tabulates the population of Hubbard by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Hubbard. The dataset can be utilized to understand the population distribution of Hubbard by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Hubbard. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Hubbard.

Key observations

Largest age group (population): Male # 10-14 years (209) | Female # 40-44 years (248). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Hubbard population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Hubbard is shown in the following column.
• Population (Female): The female population in the Hubbard is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Hubbard for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hubbard Population by Gender. You can refer the same here

Japan JP: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Female data was reported at 11.563 % in 2010. Japan JP: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Female data is updated yearly, averaging 11.563 % from Dec 2010 (Median) to 2010, with 1 observations. Japan JP: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Education Statistics. The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.; ; UNESCO Institute for Statistics (http://uis.unesco.org/); ;

Key Table Information.Table Title.Construction: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2223BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesConstruction workers annual wages($1,000)Construction workers for pay period including March 12Construction workers for pay period including June 12Construction workers for pay period including September 12Construction workers for pay period including December 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees for pay period including March 12Other employees for pay period including June 12Other employees for pay period including September 12Other employees for pay period including December 12Total fringe benefits ($1,000)Employers cost for legally required fringe benefits ($1,000)Employers cost for voluntarily provided fringe benefits ($1,000)Total selected costs ($1,000) Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of construction work subcontracted out to others ($1,000)Cost of purchased land ($1,000)Total cost of selected power, fuels, and lubricants ($1,000)Cost of gasoline and diesel fuel ($1,000)Cost of natural gas and manufactured gas ($1,000)Cost of on-highway use of gasoline and diesel fuel ($1,000)Cost of off-highway use of gasoline and diesel fuel ($1,000)Cost of all other fuels and lubricants ($1,000)Cost of purchased electricity ($1,000)Value of construction work ($1,000)Value of construction work on government owned projects ($1,000)Value of construction work on federally owned projects ($1,000)Value of construction work on state and locally owned projects ($1,000)Value of construction work on privately owned projects ($1,000)Value of other business done ($1,000)Value of construction work subcontracted in from others ($1,000)Net value of construction work ($1,000)Value added ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Gross value of depreciable assets (acquisition costs), beginning of year ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Total retirements ($1,000)Gross value of depreciable assets (acquisition costs), end of year ($1,000)Total depreciation during year ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous waste) services ($1,000)Advertising and promotional services ($1,000)Purchased professional and technical services ($1,000) Taxes and license fees ($1,000)All other operating expenses ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical locati...

Oman Imports of automatic data processing machines, magnetic or optical readers from Chad was US$13 during 2022, according to the United Nations COMTRADE database on international trade. Oman Imports of automatic data processing machines, magnetic or optical readers from Chad - data, historical chart and statistics - was last updated on June of 2025.

Samoa WS: Firms Visited or Required Meetings with Tax Officials: % of Firms data was reported at 52.100 % in 2009. Samoa WS: Firms Visited or Required Meetings with Tax Officials: % of Firms data is updated yearly, averaging 52.100 % from Dec 2009 (Median) to 2009, with 1 observations. Samoa WS: Firms Visited or Required Meetings with Tax Officials: % of Firms data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Samoa – Table WS.World Bank: Company Statistics. Percent of firms that were visited or required to meet with tax officials.; ; World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/).; Unweighted average;