The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. Overall 30 researchers conduct and promote research on the causes, consequences and nature of Good Governance and the Quality of Government - that is, trustworthy, reliable, impartial, uncorrupted and competent government institutions. The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained. A second objective is to study the effects of Quality of Government on a number of policy areas, such as health, the environment, social policy, and poverty. QoG Standard Dataset is our largest data set consisting of more than 2,000 variables from sources related to the Quality of Government. In the QoG Standard CS dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data is available for a country for 2018, data for 2019 is included. If no data exists for 2019, data for 2017 is included, and so on up to a maximum of +/- 3 years. In the QoG Standard TS dataset, data from 1946 to 2021 is included and the unit of analysis is country-year (e.g., Sweden-1946, Sweden-1947, etc.). QoG-institutet är ett oberoende forskningsinstitut som tillhör Statsvetenskapliga institutionen vid Göteborgs universitet. Sammanlagt är det ungefär 30 forskare som bedriver internationell forskning om orsaker till och konsekvenserna av korruption och samhällsstyrningens kvalitet. Forskningen fokuserar på det teoretiska och empiriska problemet hur politiska institutioner av hög kvalitet kan skapas och upprätthållas, samt studerar effekterna av samhällsstyrningens kvalitet på ett antal olika politikområden, som exempelvis hälsa, miljö, socialpolitik och fattigdom. QoG Standard Dataset är vår största datauppsättning som består av mer än 2 000 variabler från källor relaterade till konceptet Quality of Government. I QoG Standard CS dataset ingår data från omkring 2018. Data från 2018 är prioriterat, men där inga uppgifter finns tillgängliga för 2018 för ett specifikt land så ingår data för 2019. Om inga uppgifter finns tillgängliga för 2019 så ingår data för 2017 och så vidare upp till max +/- 3 år. I QoG Standard TS dataset ingår data från 1946 till 2021 och analysenheten är land-år (t.ex. Sverige-1946, Sverige-1947, etc.). In the QoG Standard CS dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data is available for a country for 2018, data for 2019 is included. If no data exists for 2019, data for 2017 is included, and so on up to a maximum of +/- 3 years. Time-series dataset: 194 countries which are members of the United Nations well as previous members of the UN provided that their de facto sovereignty has not changed substantially since they were members. Plus an addition of 17 historical countries. A total of 211 nations. Cross-sectional dataset: 194 countries which are members of the United Nations well as previous members of the UN provided that their de facto sovereignty has not changed substantially since they were members. Tidsseriedataset: 194 länder som är medlemmar i FN eller som tidigare varit medlemmar och vars suveränitet inte förändrats sedan medlemskapet. Samt 17 nationer som upphört att existera. Totalt 211 nationer. Tvärsnittsdataset: 194 länder som är medlemmar i FN eller som tidigare varit medlemmar och vars suveränitet inte förändrats sedan medlemskapet.

This is the current version of Oregon's Open Data Technical Standards Manual. The Technical Standards Manual provides guidelines for release of publishable data on the web portal at data.oregon.gov, and requirements for agencies publishing open spatial data in compliance with the State’s Open Data Standard.

Individuals can report more than one race category. This table maps the individual recipient's ID (from the Individual Recipients - HMIS Data Standards dataset) to HMIS Race ID (from the Race - HMIS Data Standards dataset).-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=61082

This is a searchable historical collection of standards referenced in regulations - Voluntary consensus standards, government-unique standards, industry standards, and international standards referenced in the Code of Federal Regulations (CFR).

The City of Rochester and its staff use data about individuals in our community to inform decisions related to policies and programs we design, fund, and carry out. City staff must understand and be accountable to best practices and standards to guide the appropriate use of this information in an ethical and accurate manner that furthers the public good. With these disaggregated data standards, the City seeks to establish useful, uniform standards that guide City staff in their collection, stewardship, analysis, and reporting of information about individuals and their demographic characteristics.This internal guide provides recommended standards and practices to City of Rochester staff for the collection, analysis, and reporting of data related to following characteristics of an individual: Race & Ethnicity; Nativity & Citizenship Status; Language Spoken at Home & English Proficiency; Age; Sex, Gender, & Sexual Orientation; Marital Status; Disability; Address / Geography; Household Income & Size; Housing Tenure; Computer & Internet Use; Employment Status; Veteran Status; and Education Level. This kind of data that describes the characteristics of individuals in our community is disaggregated data. When we summarize data about these individuals and report the data at the group level, it becomes aggregated data. These disaggregated data standards can help City staff in different roles understand how to ask individuals about various demographic traits that may describe them, the collection of which may be useful to inform the City’s programs and policies. Note that this standards document does not mandate the collection of every one of these demographic factors for all analyses or program data intake designs – instead, it prompts City staff to intentionally design surveys and other data intake tools/applications to collect the right level of data to inform the City’s decision-making while also respecting the privacy of the individuals whose information the City seeks to gather. When a City team does choose to collect any of the above-mentioned demographic information about individuals in our community, we advise that they adhere to these standards.

Demographic dataset of individual recipients served by City of Portland Rescue Plan projects using Homeless Management Information System (HMIS) to collect and manage data. Demographic data follows the US Department of Housing and Development (HUD) HMIS data standards.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=60951

net traffic

Race categories for US Department of Housing and Development (HUD) data standards. These standards apply for projects using the Homeless Management Information System (HMIS) for data collection and management. HMIS is a local information technology system used to collect client-level data and data on the provision of housing and services to individuals and families experiencing or at risk of houselessness. The Federal HUD HMIS standards preempt the City of Portland Rescue Plan Data Standards.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=60946

This data package contains three templates that can be used for creating README files and Issue Templates, written in the markdown language, that support community-led data reporting formats. We created these templates based on the results of a systematic review (see related references) that explored how groups developing data standard documentation use the Version Control platform GitHub, to collaborate on supporting documents. Based on our review of 32 GitHub repositories, we make recommendations for the content of README Files (e.g., provide a user license, indicate how users can contribute) and so 'README_template.md' includes headings for each section. The two issue templates we include ('issue_template_for_all_other_changes.md' and 'issue_template_for_documentation_change.md') can be used in a GitHub repository to help structure user-submitted issues, or can be modified to suit the needs of data standard developers. We used these templates when establishing ESS-DIVE's community space on GitHub (https://github.com/ess-dive-community) that includes documentation for community-led data reporting formats. We also include file-level metadata 'flmd.csv' that describes the contents of each file within this data package. Lastly, the temporal range that we indicate in our metadata is the time range during which we searched for data standards documented on GitHub.

Of the 14 nations included in the original study, these data cover the following ten: Brazil, Cuba, Dominican Republic, India, Israel, Nigeria, Panama, United States, West Germany, and Yugoslavia. (The data for Egypt, Japan, the Philippines, and Poland are not available through ICPSR.) In India and Israel the interviews were conducted in two waves, with different samples. Besides ascertaining the usual personal information, the study employed a "Self-Anchoring Striving Scale," an open-ended scale asking the respondent to define hopes and fears for self and the nation, to determine the two extremes of a self-defined spectrum on each of several variables. After these subjective ratings were obtained, the respondents indicated their perceptions of where they and their nations stood on a hypothetical ladder at three different points in time. Demographic variables include the respondents' age, gender, marital status, and level of education. For more information on the samples, coding, and the means of measurement, see the related publication listed below.

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Action Aims Groups Types Data Standard Controlled List. It specifies a classification system for categorising high level environmental aims and associated activities undertaken to meet those aims. It currently covers Water Land and Biodiversity aims, and in particular those activities to achieve river basin outcomes of preventing deterioration, achieving protected area objectives or achieving water body objectives.

By 2015, 90% of patients presenting to a public hospital emergency department will be seen, treated, and either discharged or admitted to hospital within four hours.

About this dataset

Context

The dataset tabulates the Standard population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Standard. The dataset can be utilized to understand the population distribution of Standard by age. For example, using this dataset, we can identify the largest age group in Standard.

Key observations

The largest age group in Standard, IL was for the group of age 85+ years with a population of 27 (9.68%), according to the 2021 American Community Survey. At the same time, the smallest age group in Standard, IL was the 70-74 years with a population of 1 (0.36%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Standard is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Standard 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 Standard Population by Age. You can refer the same here

Gender categories for US Department of Housing and Development (HUD) data standards. These standards apply for projects using the Homeless Management Information System (HMIS) for data collection and management. HMIS is a local information technology system used to collect client-level data and data on the provision of housing and services to individuals and families experiencing or at risk of houselessness. The Federal HUD HMIS standards preempt the City of Portland Rescue Plan Data Standards.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=60944

This is an export of the data archived from the 2022 National Incident Feature Service.Sensitive fields and features have been removed.Each edit to a feature is captured in the Archive. The GDB_FROM and GDB_TO fields show the date range that the feature existed in the National Incident Feature Service.The National Incident Feature Service is based on the National Wildfire Coordinating Group (NWCG) data standard for Wildland Fire Event. The Wildland Fire Event data standard defines the minimum attributes necessary for collection, storage and dissemination of incident based data on wildland fires (wildfires and prescribed fires). The standard is not intended for long term data storage, rather a standard to assist in the creation of incident based data management tools, minimum standards for data exchange, and to assist users in meeting the NWCG Standards for Geospatial Operations (PMS 936).

Current food Inspection dataset published using LIVES data standard.

This reference data provides a standard list of values for all Canadian provinces and territories. The list reflects Canada’s 13 major political units. There are many coding systems for Canadian provinces and territories. The data standard shows the relationships among the recommended code and other common codes. Purpose This list is intended to standardize the way Canadian provinces and territories are described in datasets to enable data interoperability and improve data quality. Not included in this standard are previous names, abbreviations and codes for provinces and territories. When changes occur in the future, version history will be maintained. Applicability Use of the codes within the “Alpha Code” column is recommended when sharing data within the federal government or publishing data to the Open Government Portal. This alpha code was chosen for three reasons: it is comprehensible for users it is closely aligned with the ISO 3166-2 code for subdivision and is identical to the Canada Post abbreviation it has already been adopted by a number of federal departments The Alpha Code exactly matches the set of codes created and managed by Canada Post. If Canada Post changes its codes, the Government of Canada will review and separately approve any changes to this reference standard. If it is necessary to use a numerical code in a data system, then the numerical code created by Statistics Canada is included in the table. Roles and responsibilities Data Standard Stewards Statistics Canada Statistical Geomatics Centre, Analytical Studies, Methodology and Statistical Infrastructure Field Natural Resources Canada Geographical Names Board of Canada Secretariat Data Standard Custodian Treasury Board of Canada Secretariat Office of the Chief Information Officer, Data and Digital Policy Sector Recommended Review Period The reference data standard will be reviewed as required. The expected frequency of change is low.

Air Navigation Services Standards