SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES VETERAN STATUS - DP02 Universe - Civilian population 18 Year and over Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Veteran status is used to identify people with active duty military service and service in the military Reserves and the National Guard. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty, not counting the 4-6 months for initial training or yearly summer camps.

The 2012 US Army Anthropometric Survey (ANSUR II) was executed by the Natick Soldier Research, Development and Engineering Center (NSRDEC) from October 2010 to April 2012 and is comprised of personnel representing the total US Army force to include the US Army Active Duty, Reserves, and National Guard. The data was made publicly available in 2017. In addition to the anthropometric and demographic data described below, the ANSUR II database also consists of 3D whole body, foot, and head scans of Soldier participants. These 3D data are not publicly available out of respect for the privacy of ANSUR II participants. The data from this survey are used for a wide range of equipment design, sizing, and tariffing applications within the military and has many potential commercial, industrial, and academic applications.These data have replaced ANSUR I as the most comprehensive publicly accessible dataset on body size and shape. The ANSUR II dataset includes 93 measurements from over 6,000 adult US military personnel, comprising 4,082 men (ANSUR_II_MALE_Public.csv) and 1,986 women (ANSUR_II_FEMALE_Public.csv).

The ANSUR II working databases contain 93 anthropometric measurements which were directly measured, and 15 demographic/administrative variables.

Much more information about the data collection methodology and content of the ANSUR II Working Databases may be found in the following Technical Reports, available from theDefense Technical Information Center (www.dtic.mil) through:

a. 2010-2012 Anthropometric Survey of U.S. Army Personnel: Methods and Summary
Statistics. (NATICK/TR-15/007)
b. Measurer’s Handbook: US Army and Marine Corps Anthropometric Surveys,
2010-2011 (NATICK/TR-11/017)

***************************************************************************************** April 29, 2025: STARRS - Longitudinal Study Wave 4 (LSW4) data released ***************************************************************************************** The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) is an extensive study of mental health risk and resilience among military personnel. Army STARRS consists of eight separate but integrated epidemiologic and neurobiologic studies. Survey data for three of the Army STARRS study components are available via Secure Dissemination or via the ICPSR Virtual Data Enclave: New Soldier Study (NSS); All Army Study (AAS) and Pre-Post Deployment Study (PPDS). Also available are data for the STARRS-Longitudinal Study (STARRS-LS), which are follow-up surveys conducted with Army STARRS participants from AAS, NSS and PPDS studies. Lastly, baseline administrative data from the Army/Department of Defense (DoD) and blood sample flags for Soldiers who had blood drawn as a part of their participation in NSS or PPDS are available. The AAS component of Army STARRS assesses soldiers' psychological and physical health, events encountered during training, combat, and non-combat operations, and life and work experiences across all phases of Army service. The AAS data includes data on soldiers' psychological resilience, mental health, and risk for self-harm. The NSS data are drawn from new soldiers who have just entered the Army. The data contain information on soldier health, personal characteristics, and prior experiences. Results from a series of neurocognitive tests are also included in the NSS data. The PPDS data are drawn from active duty soldiers who were interviewed at four points in time: 3-4 months prior to deployment to Afghanistan; within 1-2 weeks after return from deployment; 1-3 months after return from deployment; and 9-12 months after return from deployment. The PPDS data contain information on soldiers' psychological resilience, mental health, deployment experiences, and risk for self-harm. The STARRS-LS data are from multiple follow-up interviews with individuals who previously participated in the AAS, NSS and PPDS study components of Army STARRS. STARRS-LS data contain follow-up information on soldiers' and veterans' physical and mental health, resilience and risk for self-harm, military and employment status, deployment experience, and personal characteristics as they move through their Army careers and after they leave the Army.

This dataset represents the number of Canadian Armed Forces (CAF) Regular Force members by rank from 1997 to 2022. Military Personnel Command (MPC) supports the requirement to release accurate and timely information to Canadians, in line with the principles of Open Government. MPC has made every attempt to ensure the accuracy and reliability of the information provided. However, data contained within this report may also appear in historic, current and future reports of a similar nature where it may be represented differently, and in some cases appear to be in conflict with the current report. MPC assumes no responsibility, or liability, for any errors or omissions in the content of this publication.

Building successful collaboration between humans and robots requires efficient, effective, and natural communication. This dataset supports the study of RGB-based deep learning models for controlling robots through gestures (e.g., “follow me”). To address the challenge of collecting high-quality annotated data from human subjects, synthetic data was considered for this domain. This dataset of gestures includes real videos with human subjects and synthetic videos from our custom simulator. This dataset can be used as a benchmark for studying how ML models for activity perception can be improved with synthetic data.

Reference: de Melo C, Rothrock B, Gurram P, Ulutan O, Manjunath BS (2020) Vision-based gesture recognition in human-robot teams using synthetic data. In Proc. IROS 2020.

Methods For effective human-robot interaction, the gestures need to have clear meaning, be easy to interpret, and have intuitive shape and motion profiles. To accomplish this, we selected standard gestures from the US Army Field Manual, which describes efficient, effective, and tried-and-tested gestures that are appropriate for various types of operating environments. Specifically, we consider seven gestures: Move in reverse, instructs the robot to move back in the opposite direction; Halt, stops the robot; Attention, instructs the robot to halt its current operation and pay attention to the human; Advance, instructs the robot to move towards its target position in the context of the ongoing mission; Follow me, instructs the robot to follow the human; and, Move forward, instructs the robot to move forward.

The human dataset consists of recordings for 14 subjects (4 females, 10 males). Subjects performed each gesture twice, once for each of eight camera orientations (0º, 45º, ..., 315º). Some gestures can only be performed with one repetition (halt, advance), whereas others can have multiple repetitions (e.g., move in reverse); in the latter case, we instructed subjects to perform the gestures with as many repetitions as it felt natural to them. The videos were recorded in open environments over four different sessions. The procedure for the data collection was approved by the US Army Research Laboratory IRB, and the subjects gave informed consent to share the data. The average length of each gesture performance varied from 2 to 5 seconds and 1,574 video segments of gestures were collected. The video frames were manually annotated using custom tools we developed. The frames before and after the gesture performance were labelled 'Idle'. Notice that since the duration of the actual gesture - i.e., non-idle motion - varied per subject and gesture type, the dataset includes comparable, but not equal, number of frames for each gesture.

To synthesize the gestures, we built a virtual human simulator using a commercial game engine, namely Unity. The 3D models for the character bodies were retrieved from Mixamo, the 3D models for the face were generated on FaceGen, and the characters were assembled using 3ds Max. The character bodies were already rigged and ready for animation. We created four characters representative of the domains we were interested in: male in civilian and camouflage uniforms, and female in civilian and camouflage uniforms. Each character can be changed to reflect a Caucasian, African-American, and East Indian skin color. The simulator also supports two different body shapes: thin and thick. The seven gestures were animated using standard skeleton-animation techniques. Three animations, using the human data as reference, were created for each gesture. The simulator supports performance of the gestures with an arbitrary number of repetitions and at arbitrary speeds. The characters were also endowed with subtle random motion for the body. The background environments were retrieved from the Ultimate PBR Terrain Collection available at the Unity Asset Store. Finally, the simulator supports arbitrary camera orientations and lighting conditions.

The synthetic dataset was generated by systematically varying the aforementioned parameters. In total, 117,504 videos were synthesized. The average video duration was between 3 to 5 seconds. To generate the dataset, we ran several instances of Unity, across multiple machines, over the course of two days. The labels for these videos were automatically generated, without any need for manual annotation.

This dataset represents the total number of Officers and Non-Commissioned Members (NCMs) in the Canadian Armed Forces (CAF) from 1997 to 2022. Military Personnel Command (MPC) supports the requirement to release accurate and timely information to Canadians, in line with the principles of Open Government. MPC has made every attempt to ensure the accuracy and reliability of the information provided. However, data contained within this report may also appear in historic, current and future reports of a similar nature where it may be represented differently, and in some cases appear to be in conflict with the current report. MPC assumes no responsibility, or liability, for any errors or omissions in the content of this publication.

This is the first attempt to record the Jewish soldiers who became casualties in the numerous Wars between the Habsburg Monarchy and Revolutionary and Napoleonic France. Jewish military service in the Austrian and Austro-Hungarian army from the mid-19th century onwards, especially during the First World War, is well known and documented. By contrast, nothing comparable has been done for the very first Jewish soldiers in modern history. The time has come to set the record straight!

The current database was compiled from the personal records of the War Archive (Kriegsarchiv) of the Austrian State Archives. At that time, the Habsburg army did not publish casualty lists other than mentioning the names of the most senior officers. To find individual Jewish soldiers who became casualties, one must identify serving Jewish soldiers in the regular musters and revision papers. Those found so far can be seen in the database Jewish Soldiers of the Habsburg Army (1788-1820), which should be used in parallel with this one. The current database offers an outtake with a separate list of Jewish soldiers who were killed, wounded, missing in action, or taken prisoner. The first version has 253 entries. These are arranged chronologically based on the date the soldier first became a casualty. The name of the battle or the action shows at the top of the table. Under each such action, up to four sub-categories are given:

1. K/KIA (Killed in Action) – Soldier killed outright in combat. Readers might be surprised how few such cases appear in the database. There are several possible reasons. Firstly, since 1781 the Habsburg manpower reports began to omit the rubric Vor Feind geblieben (left in front of the enemy) denoting soldiers killed in battle. This was part of a broader rationalisation of military records in the early days of Joseph II’s rule. Whichever was the cause of their death, all fatalities were now perceived as irrecoverable manpower wastage. Soldiers who died in service were now simply marked as gestorben. Identifying combat deaths is only possible by looking at monthly reports called Standes- und Diensttabellen. Even then, the number of combat deaths remains extraordinarily low. It appears that the Habsburg army formally recorded a soldier as ‘killed in action’ only if the body was identified. For this to happen, the army had to remain in control of the battlefield – in other words, the battle had to be won. For much of the Revolutionary and Napoleonic period, this was rarely the case on the Austrian side. It appears that most combat deaths in the period landed in the rubric as ‘missing-in-action’.

2. W/WIA (Wounded in Action) – Muster rolls did not record wounds at all. Monthly tables did so very rarely. The latter were intended primarily as financial documents to record the source of the men’s pay. When a soldier entered hospital, his pay was issued from the hospital fund whose accounts were later reimbursed by the man’s regiment. While dates of hospitalisation were meticulously recorded, the cause of hospitalisation was not mentioned. In most cases, identifying wounded soldiers can only be done indirectly. When dozens or hundreds of men from the same unit were hospitalised on the same day directly after a major battle, it can be reasonably assumed that these were combat casualties. A sure way of identifying a wounded soldier was through the medical evaluation papers (Superarbitrierungs-Liste), which were filed for men no longer fit for wartime service. These papers always mentioned combat wounds, as this was a major argument in favour of making the soldier eligible for admission into the invalids. Unfortunately, the survival rate of these documents is variable and the majority simply do not exist. This database employs two categories for wounded soldiers. When medical papers or hospitalisation date allows clear identification, a soldier is entered into the database as a certain case. When broader context allows (such as wartime service and numerous other hospitalisations from the same company on the same day, suggesting a skirmish), such men are entered as probable cases.

3. P/POW (Prisoner of War) – Unlike the previous two rubrics, the Habsburg military records usually mentioned soldiers taken prisoner (Kriegsgefangen/ In Kriegsgefangenschaft gefallen). The reason was again financial. Firstly, returning men had to be issued with backpay. Secondly, from the Third Coalition War onwards, reciprocal wartime prisoner swaps (Cartel) were discontinued, but the system remained in place to ensure that mutual settlement of accounts between two belligerent armies could happen after the war. This is not the only reason why prisoners make the largest single category in our database. For much of the Revolutionary and Napoleonic period, entire Austrian army corps were forced to surrender (for instance in Ulm in 1805). This happened so often that musters from 1806 and 1811 sometimes blankly omitted case of POWs, based on the assumption that nearly every soldier fell prisoner in the previous war. Therefore, for regiments who fought in Germany and Austerlitz in 1805, and in Bavaria and Deutsch-Wagram in 1809, one must also consult the monthly tables.

4. M/MIA (Missing in Action) – Recorded as Vor Feind vermisst or vermisst for short, this category denotes men who were missing when the battle ended. Anything could have happened to them. Some were dead (see rubric one), but others were taken prisoner, were lost, or deserted. The army recorded such missing men for the same reason as prisoners of war – to settle their backpay in future if necessary.

The total for each category of casualties is given at the bottom of the table for every war fought by the Habsburg army from 1792 to 1815. At the right hand side of the table are the grand totals for each category marked in red. At the end of every personal record are fields showing what happened to the soldier after he became a casualty. Wounded could recover or perish in hospital, while the prisoners and the missing could return. The same soldier could appear in the database more than once as he could be taken prisoner, be wounded or go missing several times. Only for those killed in action could the record be closed. For those who survived, the final fate was noted where known: discharge (including sub category), invaliding, desertion, or non-combat death. Men still in service when last mentioned in the documents are noted as ‘serves’. Whether complete or not, a detailed service record for each soldier as as I could reconstruct it from the sources is available in the database Jewish Soldiers of the Habsburg Army (1788-1820).

The purpose of this agreement is for SSA to verify SSN information for Defense Manpower Data Center (DMDC) of the Department of Defense. DMDC will use the SSA data to verify SSN information of applicants for background checks, including checks of active duty military service, reserve duty military service, Army and Air National Guard military service, civilian Federal Government employees and contractors, and individuals seeking access to government facilities or networks.

Historical chart and dataset showing Indonesia military size by year from 1985 to 2020.

Historical chart and dataset showing China military size by year from 1985 to 2020.

Analysis of ‘CSRM Project Extents’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/6e43b3b6-7d1d-440c-ad85-14b848f39440 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Historical chart and dataset showing Greece military size by year from 1985 to 2020.

Multi-sectoral risk intervention model is a collaborative risk-based approach to address situations where individuals or families are experiencing a high level of risk. This is based on multiple risks factors that cross a number of different sectors and would be better managed through a holistic response. These models involve frontline service providers from a variety of agencies and sectors working together to develop a customized, multi-disciplinary intervention to help mitigate those risks. The Risk-driven Tracking Database (RTD) collects information from Situation Tables or similar multi-sectoral risk intervention models, which are regular meetings of frontline workers from a variety of government (e.g. Adult Probation and Parole Officers) and community partners (e.g. The Salvation Army). They identify individuals, families, groups or locations that are at an Acutely Elevated Risk (AER) of harm (i.e. risk of causing harm or being harmed in the near future), and work together through a multi-sectoral risk intervention to reduce those risks. Based on discussion, multi-sectoral agency partners will identify whether the threshold for acutely elevated risk has been met, in order to necessitate an intervention. If the threshold is not met, the discussion is "rejected”, and only limited data is collected in consistency with provincial privacy standards. Where a discussion is rejected, N/A may appear in the data row for Assisting Agencies, Risk Factors, Study Flags, Protective Factors, Affected Persons, and Services Mobilized etc., as this data must be redacted to conform with provincial privacy standards.

Historical chart and dataset showing Sierra Leone military size by year from 1985 to 2020.

Military Expenditure in Nepal decreased to 426.50 USD Million in 2024 from 436.50 USD Million in 2023. Nepal Military Expenditure - values, historical data, forecasts and news - updated on June of 2025.

Multi-sectoral risk intervention model is a collaborative risk-based approach to address situations where individuals or families are experiencing a high level of risk. This is based on multiple risks factors that cross a number of different sectors and would be better managed through a holistic response. These models involve frontline service providers from a variety of agencies and sectors working together to develop a customized, multi-disciplinary intervention to help mitigate those risks. The Risk-driven Tracking Database (RTD) collects information from Situation Tables or similar multi-sectoral risk intervention models, which are regular meetings of frontline workers from a variety of government (e.g. Adult Probation and Parole Officers) and community partners (e.g. The Salvation Army). They identify individuals, families, groups or locations that are at an Acutely Elevated Risk (AER) of harm (i.e. risk of causing harm or being harmed in the near future), and work together through a multi-sectoral risk intervention to reduce those risks. Based on discussion, multi-sectoral agency partners will identify whether the threshold for acutely elevated risk has been met, in order to necessitate an intervention. If the threshold is not met, the discussion is "rejected”, and only limited data is collected in consistency with provincial privacy standards. Where a discussion is rejected, N/A may appear in the data row for Assisting Agencies, Risk Factors, Study Flags, Protective Factors, Affected Persons, and Services Mobilized etc., as this data must be redacted to conform with provincial privacy standards.

Historical chart and dataset showing Malaysia military size by year from 1985 to 2020.

CSPI - Map data last updated 9/12/2018. Visit http://navigation.usace.army.mil/cspi/ for user interface.The Coastal Systems Portfolio Initiative (CSPI) databases provide an archive for data to support many of the CSPI initiatives. As the federal agency authorized by Congress to study, plan, design, construct, and renourish coastal risk reduction projects, the USACE is tasked with providing technical input on current and future needs for coastal projects. Accurate, up-to-date, and accessible technical information serves as a valuable resource for decision makers responsible for making balanced, information-based decisions for managing coastal programs. This web database presents the “big picture” about current and future needs for coastal projects within USACE. As the nation’s engineer, the USACE collected and presented technical data and estimated costs, with consideration of project reliability and risk. The process used by the USACE to examine federal projects as a total system instead of as individual projects will continue to be refined over time. This technical review is an initial systems-based tool that decision makers at any level can use to make more informed judgments as they manage coastal risk reduction projects in the United States, both now and in the near future.

*P-values based on χ square test of homogeneity for Sex, Service Branch, Housing Allowance, Occupation, and Race/Ethnicity and are based on unequal variance t-test for Age, Time in Service, and BMI. Statistical tests were not weighted for observation time.

Military Expenditure in Pakistan increased to 10166 USD Million in 2024 from 8626.20 USD Million in 2023. Pakistan Military Expenditure - values, historical data, forecasts and news - updated on June of 2025.