The Department of Human Services through Medicare assesses claims and makes payments to medical, hospital and allied health providers who treat eligible veterans, spouses and dependents, on behalf of the Department of Veterans' Affairs (DVA). \r \r The Department of Human Services, Medicare and DVA promote electronic claiming as the primary way of doing business with the government. For health professionals, electronic claiming means faster payment times, paperless lodgement of claims, faster reconciliation and more efficient confirmation of patient details. It also means lower administrative costs for the government. \r \r ** Overview of the Department of Veterans' Affairs Claiming Channels dataset** \r \r This dataset provides information on the channels used by allied health, medical and hospital providers, to lodge DVA claims for processing by Medicare. The dataset includes details on the volume of services processed via a particular channel and the value of the benefit paid. Further information on the dataset may be found in the metadata accompanying the dataset. \r \r Data is provided in the following formats: \r \r * Excel/ XLXS : The human readable version of the dataset for the current financial year (2016-2017) will be provided in an individual excel file and will be updated monthly. The human readable files for the 2015-2016 financial year may be found in the zipped excel files. \r \r \r * CSV: The machine readable version of the dataset may be found in the zipped csv file. This contains both monthly and financial year summaries. Metadata and 'Item ranges' are contained in stand-alone csvs within the zipped file.\r \r If you require statistics at a more detailed level, please contact statistics@humanservices.gov.au detailing your request. The Department of Human Services charges on a cost recovery basis for providing more detailed statistics and their provision is subject to privacy considerations. \r \r The Department of Veterans’ Affairs website contains statistical information regarding the veteran population that may be accessed by the public. \r \r Disclaimer: This data is provided by the Department of Human Services (Human Services) for general information purposes only. While Human Services has taken care to ensure the information is as correct and accurate as possible, we do not guarantee, or accept legal liability whatsoever arising from, or connected to its use. \r We recommend that users exercise their own skill and care with respect to the use of this data and that users carefully evaluate the accuracy, currency, completeness and relevance of the data for their needs. \r \r \r

This survey is the sixth in a series of comprehensive nationwide surveys designed to help the Department of Veterans Affairs (VA) plan its future programs and services for Veterans. This is the first time VA has included groups other than Veterans.

Trend in Rate of Users by Gender over Time, FY 2009 - 2018. Data underlying the third figure of Part 1 of the FY2018 Utilization Profile, a report on Veterans' use of VA benefits and services.

Trend in the number and rate of veterans who used any benefit versus those who used none, FY 2009-2018. Data underlying the first figure of Part 2 of the FY2018 Utilization Profile, a report on Veterans' use of VA benefits and services.

The Patient Advocate Tracking System (PATS) is a centralized, web based application that records and tracks instances of patient compliments and complaints concerning their care at VA health care facilities. These instances of patient contacts may come from a variety of sources including the patient, family members, congressional members and/or Veterans service organizations on behalf of the Veterans receiving care at VA facilities. This database provides a menu of reports that can be used to track and trend data across Veterans Integrated Service Networks (VISNs). Reports of contact allow the Patient Advocate to trend compliments and complaints, and ensure that issues raised are resolved. The reports include data such as patient demographics, date of contact, method of contact, who made the contact, issues involved, what service was involved, resolution date and resolution status. Data is collected from Veterans Affairs Medical Centers and sent to the VHA Support Service Center (VSSC) where the data is maintained and reports created.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

A nationwide survey that collects information such as age, race, income, commute time to work, home value, veteran status, and other data. Data from the American Community Survey and the Puerto Rico Community Survey were collected during calendar years 2008-2010.

One hundred oak trees were selected from a set of 600 veteran oaks registered in the national oak survey. These oaks were divided into two sets of 50 oaks, where insects can be sampled every second year in each oak. For practical reasons, these two sets represent two different geographical regions; region East (Oslo, Akershus, Buskerud, Østfold, Telemark & Vestfold) and region West (Agder, Rogaland og Vestland). The oaks for each set were selected at random, with the exception of a doubled likelihood of selecting oaks with a visible cavity in addition to diameter at breast height larger than 200 cm, and a few criteria relating to the number of trees in each plot. The distribution of oaks between counties and within/outside of forest was controlled after the selection was made and was considered adequately balanced. Insects were sampled from the 50 selected oaks in region East in June and July in 2023, and from the 50 selected oaks in the Western region in June - September 2024. Two window traps were mounted at each tree, one in the canopy and one at the trunk, next to a cavity if the tree had any. Beetles were identified. Several original tables are here combined into a single event table, to fit the GBIF event core data format. These tables together represent the sampling design of the monitoring program and are noted as a series of hierarchical event levels in the "samplingProtocol" column.The data should therefore be unpacked to make proper sense of the data structure. Brief explanation of the hierarchical structure of the dataset: 1) The occurrence table can be joined to the event table through the parentEvent, which joins to an 2) identification event. This level exists because any sample may have gone through several identification events, possibly with differing methods. The identification events joins through its parentEvent with 3) a sampling_trap event, which designates a single trap in a single sampling event at a location. Sampling_trap events are joined through their parentEvent to a 4) locality sampling event, which is a single sampling period in a locality. Sampling events can have 1 or more traps (sampling trap events). Finally, the locality sampling events can be joined through their parentEvent to a 5) year locality event, which designates the sampling of insects in a single locality in a year. Relevant metadata or collected explanatory data is attached to each level, with the dynamicProperties column collecting the datatypes that the Darwin Event Core doesn't presently cater to. For example a range of environmental data at the sampling sites. An R-script for unpacking the data into a more usable format is available at https://github.com/NINAnor/national_insect_monitoring. Users will have to make minor adjustments to the script to fit this particular data set.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

Military Vs Civilian Vehicles

## Overview

Military Vs Civilian Vehicles is a dataset for object detection tasks - it contains Vehicles annotations for 4,153 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

[dataset PLOS ONE Facebook paper.xlsx]. (XLSX)

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show civilian veteran counts and percentages by Neighborhood Planning Units S, T, and V in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

CivPop18Plus_e

# Civilian population 18 years and over, 2017

CivPop18Plus_m

# Civilian population 18 years and over, 2017 (MOE)

pCivPop18Plus_e

% Civilian population 18 years and over, 2017

pCivPop18Plus_m

% Civilian population 18 years and over, 2017 (MOE)

CivVeteran_e

# Civilian veterans, 2017

CivVeteran_m

# Civilian veterans, 2017 (MOE)

pCivVeteran_e

% Civilian veterans, 2017

pCivVeteran_m

% Civilian veterans, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

These are the replication files for the paper "Outside the Wire: US military Deployments and Host-Country Public Opinion". Replication data contain csv data files as well as R script files required to replicate models, tables, and figures.

This repository includes the complete supplementary appendix for the paper, a list of supplementary tables, and publicly available data and scripts to reproduce: “'Let our ballots secure what our bullets have won': Union Veterans and the Making of Radical Reconstruction." The included code produces all tables and figures in the paper and in the supplementary appendix. To use this file: 1) Download the replication folder (with all materials) in archive format (.zip file). This creates the required directory structure. 2) Read the readme.html file to see directions for using this replication file and how to access restricted data that is not included in this replication file. 3) Open main.R and set the working directory and, if desired, paths to folders containing restricted data.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show civilian veteran counts and percentages by Zip Code Tabulation Area in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

CivPop18Plus_e

# Civilian population 18 years and over, 2017

CivPop18Plus_m

# Civilian population 18 years and over, 2017 (MOE)

pCivPop18Plus_e

% Civilian population 18 years and over, 2017

pCivPop18Plus_m

% Civilian population 18 years and over, 2017 (MOE)

CivVeteran_e

# Civilian veterans, 2017

CivVeteran_m

# Civilian veterans, 2017 (MOE)

pCivVeteran_e

% Civilian veterans, 2017

pCivVeteran_m

% Civilian veterans, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

The Republic of the Congo: Military spending, percent of total government spending : The latest value from 2022 is 10.13 percent, a decline from 11.25 percent in 2021. In comparison, the world average is 6.44 percent, based on data from 139 countries. Historically, the average for the Republic of the Congo from 1992 to 2022 is 9.04 percent. The minimum value, 4.82 percent, was reached in 2013 while the maximum of 12.32 percent was recorded in 2020.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Simple replication materials (data and R script) for all analyses in data feature as published in CMPS.