Investigator(s): Federal Bureau of Investigation Since 1930, the Federal Bureau of Investigation (FBI) has compiled the Uniform Crime Reports (UCR) to serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. With the 1977 data, the title was expanded to Uniform Crime Reporting Program Data. Each year, participating law enforcement agencies contribute reports to the FBI either directly or through their state reporting programs. ICPSR archives the UCR data as five separate components: (1) summary data, (2) county-level data, (3) incident-level data (National Incident-Based Reporting System [NIBRS]), (4) hate crime data, and (5) various, mostly nonrecurring, data collections. Summary data are reported in four types of files: (a) Offenses Known and Clearances by Arrest, (b) Property Stolen and Recovered, (c) Supplementary Homicide Reports (SHR), and (d) Police Employee (LEOKA) Data (Law Enforcement Officers Killed or Assaulted). The county-level data provide counts of arrests and offenses aggregated to the county level. County populations are also reported. In the late 1970s, new ways to look at crime were studied. The UCR program was subsequently expanded to capture incident-level data with the implementation of the National Incident-Based Reporting System. The NIBRS data focus on various aspects of a crime incident. The gathering of hate crime data by the UCR program was begun in 1990. Hate crimes are defined as crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity. In September 1994, disabilities, both physical and mental, were added to the list. The fifth component of ICPSR's UCR holdings is comprised of various collections, many of which are nonrecurring and prepared by individual researchers. These collections go beyond the scope of the standard UCR collections provided by the FBI, either by including data for a range of years or by focusing on other aspects of analysis. NACJD has produced resource guides on UCR and on NIBRS data.

About this dataset

Context

The dataset tabulates the Federal Heights 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 Federal Heights. The dataset can be utilized to understand the population distribution of Federal Heights by age. For example, using this dataset, we can identify the largest age group in Federal Heights.

Key observations

The largest age group in Federal Heights, CO was for the group of age Under 5 years years with a population of 1,393 (9.84%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Federal Heights, CO was the 85 years and over years with a population of 44 (0.31%). 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

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 Federal Heights is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Federal Heights 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 Federal Heights Population by Age. You can refer the same here

Progress Needed on Identifying Expenditures, Building and Utilizing a Data Infrastructure, and Reducing Duplicative Efforts The federal government collects, maintains, and uses geospatial information—data linked to specific geographic locations—to help support varied missions, including national security and natural resources conservation. To coordinate geospatial activities, in 1994 the President issued an executive order to develop a National Spatial Data Infrastructure—a framework for coordination that includes standards, data themes, and a clearinghouse. GAO was asked to review federal and state coordination of geospatial data. GAO’s objectives were to (1) describe the geospatial data that selected federal agencies and states use and how much is spent on geospatial data; (2) assess progress in establishing the National Spatial Data Infrastructure; and (3) determine whether selected federal agencies and states invest in duplicative geospatial data. To do so, GAO identified federal and state uses of geospatial data; evaluated available cost data from 2013 to 2015; assessed FGDC’s and selected agencies’ efforts to establish the infrastructure; and analyzed federal and state datasets to identify duplication. What GAO Found Federal agencies and state governments use a variety of geospatial datasets to support their missions. For example, after Hurricane Sandy in 2012, the Federal Emergency Management Agency used geospatial data to identify 44,000 households that were damaged and inaccessible and reported that, as a result, it was able to provide expedited assistance to area residents. Federal agencies report spending billions of dollars on geospatial investments; however, the estimates are understated because agencies do not always track geospatial investments. For example, these estimates do not include billions of dollars spent on earth-observing satellites that produce volumes of geospatial data. The Federal Geographic Data Committee (FGDC) and the Office of Management and Budget (OMB) have started an initiative to have agencies identify and report annually on geospatial-related investments as part of the fiscal year 2017 budget process. FGDC and selected federal agencies have made progress in implementing their responsibilities for the National Spatial Data Infrastructure as outlined in OMB guidance; however, critical items remain incomplete. For example, the committee established a clearinghouse for records on geospatial data, but the clearinghouse lacks an effective search capability and performance monitoring. FGDC also initiated plans and activities for coordinating with state governments on the collection of geospatial data; however, state officials GAO contacted are generally not satisfied with the committee’s efforts to coordinate with them. Among other reasons, they feel that the committee is focused on a federal perspective rather than a national one, and that state recommendations are often ignored. In addition, selected agencies have made limited progress in their own strategic planning efforts and in using the clearinghouse to register their data to ensure they do not invest in duplicative data. For example, 8 of the committee’s 32 member agencies have begun to register their data on the clearinghouse, and they have registered 59 percent of the geospatial data they deemed critical. Part of the reason that agencies are not fulfilling their responsibilities is that OMB has not made it a priority to oversee these efforts. Until OMB ensures that FGDC and federal agencies fully implement their responsibilities, the vision of improving the coordination of geospatial information and reducing duplicative investments will not be fully realized. OMB guidance calls for agencies to eliminate duplication, avoid redundant expenditures, and improve the efficiency and effectiveness of the sharing and dissemination of geospatial data. However, some data are collected multiple times by federal, state, and local entities, resulting in duplication in effort and resources. A new initiative to create a national address database could potentially result in significant savings for federal, state, and local governments. However, agencies face challenges in effectively coordinating address data collection efforts, including statutory restrictions on sharing certain federal address data. Until there is effective coordination across the National Spatial Data Infrastructure, there will continue to be duplicative efforts to obtain and maintain these data at every level of government.https://www.gao.gov/assets/d15193.pdfWhat GAO Recommends GAO suggests that Congress consider assessing statutory limitations on address data to foster progress toward a national address database. GAO also recommends that OMB improve its oversight of FGDC and federal agency initiatives, and that FGDC and selected agencies fully implement initiatives. The agencies generally agreed with the recommendations and identified plans to implement them.

This document describes the physical design for the national data standard for the geospatial dataset. It is intended as a guideline for implementation. States may extend and expand upon this guideline in order to meet their specific needs, provided that when the data is pushed up to the national level, it will meet the minimum requirements as set forth in the Data Standard.

This table provides an overview of the non-financial transactions of the institutional sectors of the Dutch economy. Non-financial transactions consist of current transactions and capital account transactions. Transactions are broken down by resources and uses. In addition, the balances of the sectors are also shown. Non-financial transactions are estimated for the main sectors of the economy: non-financial corporations, financial institutions, general government, households, non-profit institutions serving households and the rest of the world. In addition, the financial corporations and general government sectors are further broken down by subsectors. Sectors are presented both consolidated and non-consolidated.

Data available from: Annual data since 1995. Quarterly data from the first quarter of 1999 onwards.

Status of figures: The annual data from 1995 to 2022 are final. Quarterly data from 2022 onwards are provisional.

Changes as of 24 June 2024: This is a new table. The Central Bureau of Statistics recently revised the national accounts. New sources, methods and concepts are introduced into the national accounts, so that the picture of the Dutch economy is optimally aligned with all underlying statistics, sources and international guidelines for compiling the national accounts. This table shows the figures after revision. For more information see section 3.

When will there be new figures? Annual figures: The first annual figures are available 85 days after the end of the reporting year as a sum of the figures for the four quarters of the year in question. Subsequently, the provisional and final annual estimates are published after 6 and 18 months respectively. In addition, for the sector accounts, the financial accounts and balance sheets for all reporting periods are revised annually. The figures will be available annually in June on StatLine, CBS's electronic database. In addition, the figures are published annually in July in the ‘National accounts table set'. Quarterly figures: 85 days after the end of a quarterly report, the first quarterly estimate will become available. Should new quarterly information become available thereafter, the first quarter may be revised in September and the second quarter in December. In March, the first three quarters can still be adjusted. If new annual figures become available in June, the quarterly figures will be revised again to align with those annual figures. In addition, interim updates may take place to provide the European Commission with the most up-to-date government data at the end of March and the end of September. The data for the quarters are linked to the adjusted annual figures.

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

About this dataset

Context

The dataset tabulates the Federal Way 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 Federal Way. The dataset can be utilized to understand the population distribution of Federal Way by age. For example, using this dataset, we can identify the largest age group in Federal Way.

Key observations

The largest age group in Federal Way, WA was for the group of age 30 to 34 years years with a population of 7,872 (7.93%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Federal Way, WA was the 80 to 84 years years with a population of 1,511 (1.52%). 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

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 Federal Way is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Federal Way 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 Federal Way Population by Age. You can refer the same here

The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). In the late 1970s, the law enforcement community called for a thorough evaluative study of the UCR with the objective of recommending an expanded and enhanced UCR program to meet law enforcement needs into the 21st century. The FBI fully concurred with the need for an updated program to meet contemporary needs and provided its support, formulating a comprehensive redesign effort. Following a multiyear study, a "Blueprint for the Future of the Uniform Crime Reporting Program" was developed. Using the "Blueprint" and in consultation with local and state law enforcement executives, the FBI formulated new guidelines for the Uniform Crime Reports. The National Incident-Based Reporting System (NIBRS) is being implemented to meet these guidelines. NIBRS data are archived at ICPSR as 13 separate data files, which may be merged by using linkage variables. The data focus on a variety of aspects of a crime incident. Part 4, Administrative Segment, offers data on the incident itself (date and time). Each crime incident is delineated by one administrative segment record. Also provided are Part 5, Offense Segment (offense type, location, weapon use, and bias motivation), Part 6, Property Segment (type of property loss, property description, property value, drug type and quantity), Part 7, Victim Segment (age, sex, race, ethnicity, and injuries), Part 8, Offender Segment (age, sex, and race), and Part 9, Arrestee Segment (arrest date, age, sex, race, and weapon use). The Batch Header Segment (Parts 1-3) separates and identifies individual police agencies by Originating Agency Identifier (ORI). Batch Header information, which is contained on three records for each ORI, includes agency name, geographic location, and population of the area. Part 10, Group B Arrest Report Segment, includes arrestee data for Group B crimes. Window Segments files (Parts 11-13) pertain to incidents for which the complete Group A Incident Report was not submitted to the FBI. In general, a Window Segment record will be generated if the incident occurred prior to January 1 of the previous year or if the incident occurred prior to when the agency started NIBRS reporting. As with UCR, participation in NIBRS is voluntary on the part of law enforcement agencies. The data are not a representative sample of crime in the United States. For 1993, eight states, fully or partially participating in NIBRS, were included in the dataset.

China Floor Space Completed: Year to Date: Residential data was reported at 882,418.173 sq m th in Dec 2017. This records an increase from the previous number of 667,165.574 sq m th for Nov 2017. China Floor Space Completed: Year to Date: Residential data is updated monthly, averaging 174,874.313 sq m th from Jan 1999 (Median) to Dec 2017, with 228 observations. The data reached an all-time high of 1,075,008.878 sq m th in Dec 2014 and a record low of 5,399.700 sq m th in Feb 1999. China Floor Space Completed: Year to Date: Residential data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under Global Database’s China – Table CN.RKD: Floor Space Completed: Monthly. Monthly data update issues on Fixed Assets Investment: Starting from 2018, NBS only announced growth (%) and did not publish absolute values. Individual absolute figures were from press releases or press conferences. This series of data also the relevant dataset being affected, both the absolute value and the % have not been released. 关于固定资产投资的月度数据更新问题： 由2018年起，NBS只公布增长(%)，不公布绝对值，个别绝对值数据来自新闻稿或新闻发布会。 这一系列数据也是受影响的相关数据，绝对值和%均没有公布。

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

This dataset includes percent distribution of births for females by age group in the United States since 1933.

The number of states in the reporting area differ historically. In 1915 (when the birth registration area was established), 10 states and the District of Columbia reported births; by 1933, 48 states and the District of Columbia were reporting births, with the last two states, Alaska and Hawaii, added to the registration area in 1959 and 1960, when these regions gained statehood. Reporting area information is detailed in references 1 and 2 below. Trend lines for 1909–1958 are based on live births adjusted for under-registration; beginning with 1959, trend lines are based on registered live births.

SOURCES

NCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/).

REFERENCES

1. National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf.

2. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf.

3. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I–Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf.

4. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf.

5. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf.

6. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.

Abstract

The World Bank and UNHCR in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform a targeted response. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets refugee household and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection, and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing. The data is uploaded in three files. The first is the hh file, which contains household level information. The 'hhid', uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'adult_id'. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the 'child_id'. The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 1,328 refugee households Wave 2: July 16 to September 18, 2020; 1,699 refugee households Wave 3: September 28 to December 2, 2020; 1,487 refugee households Wave 4: January 15 to March 25, 2021; 1,376 refugee households Wave 5: March 29 to June 13, 2021; 1,562 refugee households Wave 6: July 14 to November 3, 2021; 1,407 refugee households Wave 7: November 15, 2021, to March 31, 2022; 1,281 refugee households Wave 8: May 31 to July 8, 2022: 1,355 refugee households The same questionnaire is also administered to nationals in Kenya, with the data available in the WB microdata library: https://microdata.worldbank.org/index.php/catalog/3774

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Individual and Household

Universe

All persons of concern for UNHCR

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless, where sampling approaches differ across strata. For refugees in Kakuma and Kalobeyei, as well as for stateless people, recently conducted Socioeconomic Surveys (SES), were used as sampling frames. For the refugee population living in urban areas and the Dadaab camp, no such household survey data existed, and sampling frames were based on UNHCR's registration records (proGres), which include phone numbers. For Kakuma, Kalobeyei, Dadaab and urban refugees, a two-step sampling process was used. First, 1,000 individuals from each stratum were selected from the corresponding sampling frames. Each of these individuals received a text message to confirm that the registered phone was still active. In the second stage, implicitly stratifying by sex and age, the verified phone number lists were used to select the sample. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in. For the stateless population, all the participants of the Shona socioeconomic survey (n=400) were included in the RRPS, because of limited sample size. The sampling frames for the refugee and Shona stateless communities are thus representative of households with active phone numbers registered with UNHCR.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire included 12 sections Section 1: Introduction Section 2: Household background Section 3: Travel patterns and interactions Section 4: Employment Section 5: Food security Section 6: Income Loss Section 7: Transfers Section 8: Subjective welfare (50% of sample) Section 9: Health Section 10: COVID Knowledge Section 11: Household and Social Relations (50% of sample) Section 12: Conclusion

Cleaning operations

Variable names were kept constant across survey waves. For questions that remained exactly the same across survey waves, data points for all waves can be found under one variable name. For questions where the phrasing changed (even in a minimal way) across waves, variable names were also changed to reflect the change in phrasing. Extended missing values are used to indicate why a value is missing for all variables. The following extended missing values are used in the dataset: · .a for 'Don't know' · .b for 'Refused to respond' · .c for 'Outliers set to missing' · .d for 'Inconsistency set to missing' (used for employment data as explained below) · .e for 'Field Skipped' (where an error in the survey tool caused the question to be missed) · .z for 'Not administered' (as the variable was not relevant to the observation) More detailed data on children was collected between waves 3 and 7, compared to waves 1, 2 and 8. In waves 1 and 2, data on children, e.g. on their learning activities, was collected for all children in a household with one question. Therefore, variables related to children are part of the 'hh' data for waves 1 and 2. Between waves 3 and 7, questions on children in the household were asked for specific children. Some questions covered all children, while others were only administered to one randomly selected child in the household. This approach allows to disaggregate data at the level of the child household members, and the data can be found in the 'child' data set. The household level weights can be used for analysis of the children's data. In wave 8, detailed information on children was dropped, as the questionnaire focused on other topics. The education status of household members, except for the respondent, was imputed for rounds 1 and 2. For rounds 1 and 2, only the education status of the respondent was elicited, while for later rounds the education status for each household member was asked. In order to evaluate outcomes by the household member's education status, information on education was imputed for waves 1 and 2, using the information provided for all household members in waves 3, 4, and 5. This resulted in additional information on the education status for household members in round 1 and 2, which was not yet available for earlier versions of this data. Some questions are not asked repeatedly across waves such that their values were imputed. For some questions, answers are not possible or unlikely to change within two months between survey waves such that households were not asked about them in all waves. The questions on assets owned before March 2020 were only asked to households when they are interviewed for the first time. The questions on the dwelling's wall and floor material as well as the household's connection to the power grid was not asked for all households in wave 2 and 3, where only new households and those who moved were covered by these questions. Questions on the main source of electricity in the households and types of assets owned were not asked in wave 8. The missing values those variables have when they were not asked, are imputed from the answers given in earlier waves. Improved quality insurance algorithms lead to minor revisions to wave 1 to 5 data. Based on additional data checks, the team has made minor refinements to wave 1 to 5 data. The identification of the household members that were the respondent or the household head was refined in the rare cases where it was not possible to interview the same respondent as in previous waves for a given household such that another adult was interviewed. For this reason, for about 2 percent of observations the household head status was assigned to an incorrect household member, which was corrected. For <1 percent of households the respondent did not appear in adult level dataset. For about 1 percent of observations in wave 5 the respondent appeared twice in the adult level dataset. Data from questions on COVID-19 vaccinations from wave 7 was dropped from the dataset. Due to significantly higher self-reported vaccination rates compared to official administrative records, data on vaccinations was deemed unreliable, most likely due to social desirability bias. Consequently, questions on vaccination status and questions using the vaccination data as a validation criterion were dropped from the datasets.

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

On 6/16/2023 CDPH replaced the booster measures with a new “Up to Date” measure based on CDC’s new recommendations, replacing the primary series, boosted, and bivalent booster metrics The definition of “primary series complete” has not changed and is based on previous recommendations that CDC has since simplified. A person cannot complete their primary series with a single dose of an updated vaccine. Whereas the booster measures were calculated using the eligible population as the denominator, the new up to date measure uses the total estimated population. Please note that the rates for some groups may change since the up to date measure is calculated differently than the previous booster and bivalent measures.

This data is from the same source as the Vaccine Progress Dashboard at https://covid19.ca.gov/vaccination-progress-data/ which summarizes vaccination data at the county level by county of residence. Where county of residence was not reported in a vaccination record, the county of provider that vaccinated the resident is included. This applies to less than 1% of vaccination records. The sum of county-level vaccinations does not equal statewide total vaccinations due to out-of-state residents vaccinated in California.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

Totals for the Vaccine Progress Dashboard and this dataset may not match, as the Dashboard totals doses by Report Date and this dataset totals doses by Administration Date. Dose numbers may also change for a particular Administration Date as data is updated.

Previous updates:

• On March 3, 2023, with the release of HPI 3.0 in 2022, the previous equity scores have been updated to reflect more recent community survey information. This change represents an improvement to the way CDPH monitors health equity by using the latest and most accurate community data available. The HPI uses a collection of data sources and indicators to calculate a measure of community conditions ranging from the most to the least healthy based on economic, housing, and environmental measures.

• Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 16+ and age 5+ denominators have been uploaded as archived tables.

• Starting on May 29, 2021 the methodology for calculating on-hand inventory in the shipped/delivered/on-hand dataset has changed. Please see the accompanying data dictionary for details. In addition, this dataset is now down to the ZIP code level.

[2023-05-04 - Added WIC Infant and Toddler Feeding Practices Study-2 Data File Training Manual] The WIC Infant and Toddler Feeding Practices Study–2 (WIC ITFPS-2) (also known as the “Feeding My Baby Study”) is a national, longitudinal study that captures data on caregivers and their children who participated in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) around the time of the child’s birth. The study addresses a series of research questions regarding feeding practices, the effect of WIC services on those practices, and the health and nutrition outcomes of children on WIC. Additionally, the study assesses changes in behaviors and trends that may have occurred over the past 20 years by comparing findings to the WIC Infant Feeding Practices Study–1 (WIC IFPS-1), the last major study of the diets of infants on WIC. This longitudinal cohort study has generated a series of reports. These datasets include data from caregivers and their children during the prenatal period and during the children’s first four years of life (child ages 1 to 48 months). A full description of the study design and data collection methods can be found in Chapter 1 of the Second Year Report (https://www.fns.usda.gov/wic/wic-infant-and-toddler-feeding-practices-study-2-second-year-report). A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-ITFPS2-Year4Report-Appendix.pdf).
Processing methods and equipment used Data in this dataset were primarily collected via telephone interview with caregivers. Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. Study date(s) and duration Data collection occurred between 2013 and 2018. Study spatial scale (size of replicates and spatial scale of study area) Respondents were primarily the caregivers of children who received WIC services around the time of the child’s birth. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) This dataset includes sampling weights that can be applied to produce national estimates. A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-ITFPS2-Year4Report-Appendix.pdf).
Level of subsampling (number and repeat or within-replicate sampling) A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-ITFPS2-Year4Report-Appendix.pdf).
Study design (before–after, control–impacts, time series, before–after-control–impacts) Longitudinal cohort study. Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains caregiver-level responses to telephone interviews. Also available in the dataset are children’s length/height and weight data, which were objectively collected while at the WIC clinic or during visits with healthcare providers. In addition, the file contains derived variables used for analytic purposes. The file also includes weights created to produce national estimates. The dataset does not include any personally-identifiable information for the study children and/or for individuals who completed the telephone interviews. Description of any gaps in the data or other limiting factors Please refer to the Second Year Report (https://www.fns.usda.gov/wic/wic-infant-and-toddler-feeding-practices-study-2-second-year-report) for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used The majority of outcomes were measured via telephone interviews with children’s caregivers. Dietary intake was assessed using the USDA Automated Multiple Pass Method (https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/ampm-usda-automated-multiple-pass-method/). Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. See file list for descriptions of each data file.

This table provides annual data about the employment of employees and self-employed persons. It contains annual data on employed persons, jobs, full-time equivalent (fte) and hours worked.

Data available from: 1995

Status of the figures: Data from 1995 up to and including 2021 are final. Data of 2022 and further are provisional.

Changes as of June 24th 2024: This is a new table. Statistics Netherlands has carried out a revision of the national accounts. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. This table contains revised data. For further information see section 3.

When will new figures be published? Provisional data are published 6 months after the end of the reporting year. Final data are released 30 months after the end of the reporting year.

The National Transit Map - Agencies dataset was compiled on December 02, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Transit Map (NTM) is a nationwide catalog of fixed-guideway and fixed-route transit service in America. It is compiled using General Transit Feed Specification (GTFS) Schedule data. The GTFS Schedule documentation is available at, https://gtfs.org/schedule/. The NTM Agencies dataset represents the physical addresses of participating transit agencies. Regarding data coverage and licenses, starting in Report Year 2023, the Federal Transit Administration (FTA) has required National Transit Database (NTD) Reporters to submit General Transit Feed Specification (GTFS) data. Reporters will submit GTFS during their reporting period, which is determined by their fiscal year end date. All GTFS data submitted to the NTD will enter the public domain. Prior to the GTFS requirement, transit agencies voluntarily participated in the NTM and granted the U.S. Department of Transportation (USDOT) a Creative Commons Attribution 3.0 United States (CC-BY-3.0) license. The CC-BY-3.0 license is available at, https://creativecommons.org/licenses/by/3.0/us/legalcode. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529047

Investigator(s): Pretrial Services Resource Center The National Pretrial Reporting Program project was initiated in 1983 by the Pretrial Services Resource Center with funding from the Bureau of Justice Statistics (BJS) to determine the feasibility of a national pretrial database. Specifically, the project sought to determine whether accurate and comprehensive pretrial data can be collected at the local level and subsequently aggregated at the state and federal levels. Before this project began, there was no national system for regularly tracking information on persons and cases from the point they entered the local court system until they were adjudicated and sentenced, nor was it clear that such a system could be established. The project was developed in three phases and each phase has taken the program closer to its goal of providing BJS with reliable and valid data on the movement of defendants through the criminal court system. Phase 1 of the project, though limited to three jurisdictions, demonstrated that baseline data could be collected to describe how criminal defendants are processed through the courts. Phase II of the project not only focused on the collection and analyses of the data, but was also concerned with the procedural methods necessary to devise a national baseline data collection project. Although there were problems encountered and lessons learned in Phase II, the findings confirmed that a national effort could be undertaken. In the summer of 1987, Phase III of the program was developed. Changes incorporated in Phase III were a direct result of the lessons learned from Phase II, with the goal of achieving a more accurate and representative database. To accomplish this, significant changes were made, particularly in four areas: site selection, defendant sampling, site personnel training, and targeted charge. A more ambitious project was envisioned as well, with the project targeting 40 jurisdictions for data collection. Another major change in Phase III concerned the decision to target felony defendants only, since many of the ongoing BJS projects were limited to felony defendants. The data collected in the period 1988-1993 provide a picture of felony defendants' movements through the criminal courts and what happens during the course of their journey.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. These data provide locational and attribute information for places nominated to and included in the National Heritage List as determined by the Australian Government managed by the Department of Sustainability, Environment, Water, Population and Communities, Heritage and Wildlife Division. National Heritage List polygons with …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. These data provide locational and attribute information for places nominated to and included in the National Heritage List as determined by the Australian Government managed by the Department of Sustainability, Environment, Water, Population and Communities, Heritage and Wildlife Division. National Heritage List polygons with attribute information describing the place name, class (indigenous, natural, historic), and status. Places subject to confidentiality agreements are included in these data but the location is generalised to the bounding 250k mapsheet. The location data for place nominations that have been rejected, are ineligible, removed or destroyed are not included in the publicly downloadable spatial dataset. Places having current assessment and nomination processes involving boundary revisions being undertaken are not available to the public. Spatial data for listed places are available to the public. DATA QUALITY REPORT - COMPLETENESS The database is live and ongoing. There are current assessment and nomination process being undertaken. DATA QUALITY REPORT - CONCEPTUAL CONSISTENCY The conversion of the data from the original shapefiles follow existing protocols currently used by the Register of the National Estate. The attribution is assumed to be logically consistent as provided by the Heritage and Wildlife Division of the Australian Government Department of Sustainability, Environment, Water, Population and Communities. DATA QUALITY REPORT - POSITIONAL ACCURACY Most features have a positional accuracy of, at most, +/- 100 metres DATA QUALITY REPORT - ATTRIBUTE ACCURACY Attribute Information is verified by the Heritage Division. Dataset History The original spatial data for some places were captured and copied from the Register of the National Estate, which were digitised by the Australian Surveying and Land Information Group (AUSLIG) from stable-base overlays produced by the Australian Heritage Commission since 1986. Since 1999, data entry and attribution has been undertaken by the Australian Government Department of Sustainability, Environment, Water, Population and Communities, Heritage Division staff. Data are captured using topographic and cadastral data at map scales of up to 1:250,000, depending on the size and detail of the property. The majority of the source datasets are maintained and processed as ESRI shapefiles, in geographic projection using datum GDA94 The final dataset described by this metadata has been transformed to the Geocentric Datum of Australia (GDA94). This dataset was exported from SDE by ERIN on 17/09/2013 for use in compiling preliminary bioregional assets lists for the Office of Water Science Bioregional Assessment Program. Field "ElemetID" was added and a unique identifier created for each spatial feature for use in the BA Programme. Dataset Citation Department of the Environment (2014) National Heritage List Spatial Database (NHL) (v2.1). Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/26daa8d7-a90e-47f3-982b-0df362414e65.