Since 1800, more than 37 million people worldwide have died while actively fighting in wars.

The number would be much higher still if it also considered the civilians who died due to the fighting, the increased number of deaths from hunger and disease resulting from these conflicts, and the deaths in smaller conflicts that are not considered wars.1

Wars are also terrible in many other ways: they make people’s lives insecure, lower their living standards, destroy the environment, and, if fought between countries armed with nuclear weapons, can be an existential threat to humanity.

Looking at the news alone, it can be difficult to understand whether more or less people are dying as a result of war than in the past. One has to rely on statistics that are carefully collected so that they can be compared over time.

While every war is a tragedy, the data suggests that fewer people died in conflicts in recent decades than in most of the 20th century. Countries have also built more peaceful relations between and within them.

How many wars are avoided, and whether the trend of fewer deaths in them continues, is up to our own actions. Conflict deaths recently increased in the Middle East, Africa, and Europe, stressing that the future of these trends is uncertain.

On this page, you can find data, visualizations, and writing on how common war and peace are between and within countries, and how this has changed over time.

The Military Bases dataset was last updated on November 11, 2025 and are defined by Fiscal Year 2024 data, from the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas world-wide. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment. Only sites reported in the BSR or released in a map supplementing the Foreign Investment Risk Review Modernization Act of 2018 (FIRRMA) Real Estate Regulation (31 CFR Part 802) were considered for inclusion. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD. While every attempt has been made to provide the best available data quality, this data set is intended for use at mapping scales between 1:50,000 and 1:3,000,000. For this reason, boundaries in this data set may not perfectly align with DoD site boundaries depicted in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards, will remain compliant when this data is incorporated. Boundary data is most suitable for larger scale maps; point locations are better suited for mapping scales between 1:250,000 and 1:3,000,000. If a site is part of a Joint Base (effective/designated on 1 October, 2010) as established under the 2005 Base Realignment and Closure process, it is attributed with the name of the Joint Base. All sites comprising a Joint Base are also attributed to the responsible DoD Component, which is not necessarily the pre-2005 Component responsible for the site. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529039

NOTE: 2001-2013 enlisted totals include "cadets-midshipmen" so officer+enlisted=total. This may not be the correct assumption, but the historical tables only have "officer" and "enlisted" totals.

Information about the types of eating disorders, some reasons why the military community are at risk, warning signs and how to get help. The Missouri Eating Disorders Council (MOEDC) created this document so support service members, veterans and their families.

This dataset, released by DoD, contains geographic information for major installations, ranges, and training areas in the United States and its territories. This release integrates site information about DoD installations, training ranges, and land assets in a format which can be immediately put to work in commercial geospatial information systems. Homeland Security/Homeland Defense, law enforcement, and readiness planners will benefit from immediate access to DoD site location data during emergencies. Land use planning and renewable energy planning will also benefit from use of this data. Users are advised that the point and boundary location datasets are intended for planning purposes only, and do not represent the legal or surveyed land parcel boundaries.

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.

This dataset is about countries per year in Central America. It has 512 rows. It features 3 columns: country, and armed forces personnel.

This dataset is about countries per year in Georgia. It has 64 rows. It features 4 columns: country, armed forces personnel, and population.

This dataset list the total number of military and civilian personnel from 2001-2017. The dataset is separated in the following categories: gender, regular forces, reserve forces, officer and non-commissioned member (NCM).

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

The Department of Defense Health Agency’s (DHA) Vision Center of Excellence (VCE) analyzed data from the MHS MART database on behalf of the VEHSS project. MHS MART is a data management and reporting system used to support decision-making, health care analysis, and operational reporting. MART integrates various sources within MHS to provide a centralized repository for health care data, facilitating access to information that aids in managing health care services, resources, and performance across MHS. Data are based on claims and encounter records in the MHS Management Analysis and Reporting Tool (MART) database. The population includes all active-duty and retired military members and their dependents in the MHS. The sample size is approximately 9.08 million persons. These data are also available in the VEHSS Data Explorer, an interactive data visualization tool reporting prevalence information from more than 10 data sources: https://www.cdc.gov/vision-health-data/index.html

The Military Bases dataset is as of May 21, 2019, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate. Sites were selected from the 2010 Base Structure Report (BSR), a summary of the DoD Real Property Inventory. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities, and only those in the fifty United States and US Territories were considered for inclusion. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

Acknowlegement

Original Data

Foto von israel palacio auf Unsplash

This report presents findings from the 2015 National Survey on Drug Use and Health (NSDUH) with a focus on comparing estimates related to substance use and mental health for military family members (spouses and children) with general population estimates. The numbers of military family members included in the 2015 NSDUH were relatively small. As a consequence, the report focuses on wives aged 18 to 49 and children aged 12 to 17. In the general area of substance use, the report includes estimates for past year use of any illicit substances (marijuana, cocaine in any form including crack, heroin, hallucinogens, inhalants, and methamphetamine and misuse of four categories of prescription drugs -- pain relievers, tranquilizers, stimulants and sedatives), past year cigarette use, past year alcohol use (both any use and binge use), and past year treatment for substance use, including alcohol use. In the general area of mental health, the report includes estimates for any mental illness (AMI) in the past year for wives. For both wives and children, the report includes estimates for past year major depressive episode (MDE) and mental health service use. For children, estimates of mental health service are reported by general treatment setting (e.g., mental health, educational, medical). As additional years of data become available, it will be possible in future reports to include both male and female spouses and to make more detailed comparisons -- for example for more specific types of illicit substances used and for treatment received by setting, by race/ethnicity and for spouses, by employment and educational background.

Replication Data for: Armed Violence and Patriarchal Values: A Survey of Young Men in Thailand and their Military Experiences What is the relationship between armed violence and patriarchal values? This question is addressed with the help of two surveys of young men in the conflict-affected southern provinces of Thailand. In Study 1 we find that men with more patriarchal values are more prone to volunteer for paramilitary service. Study 2 uses a natural experiment made possible by the conscription lottery in Thailand to compare survey responses of men who were involuntarily enlisted to do Military Conscription Service (treatment group) to the responses of those men who participated in the lottery but were not enlisted (control group). We find no difference in patriarchal values between the treatment and control groups. We conclude that patriarchal values drive voluntary participation in armed conflict while military service as a conscript in a conflict zone does not cause patriarchal values.

Bangladesh BD: Armed Forces Personnel: Total data was reported at 227,000.000 Person in 2020. This stayed constant from the previous number of 227,000.000 Person for 2019. Bangladesh BD: Armed Forces Personnel: Total data is updated yearly, averaging 214,000.000 Person from Jun 1985 (Median) to 2020, with 33 observations. The data reached an all-time high of 252,000.000 Person in 2005 and a record low of 91,300.000 Person in 1985. Bangladesh BD: Armed Forces Personnel: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Defense and Official Development Assistance. Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces.;International Institute for Strategic Studies, The Military Balance.;Sum;Data for some countries are based on partial or uncertain data or rough estimates.

Version 2 (18 March 2025) includes a further 356 service itineraries. In addition, 41 entries from the previous version were updated or expanded. Currently the database covers a total of 1,858 Jewish soldiers, 421 wives and 83 children.

ORIGINAL VERSION 1 (18 September 2024)

With more than 1,500 individual entries, this is the inaugural instalment of my research database collated in the framework of the Project Forgotten Soldiers: Jewish Military Experience in the Habsburg Monarchy. This is an open access database, and everyone is welcome to use it according to their own scholarly and personal interests. In 1,189 cases we have official documented records confirming the soldiers were Jewish. In another 313 entries I was able to identify likely Jewish soldiers based on circumstantial evidence cross-referencing names and places of birth, with the presence of confirmed Jewish soldiers drafted into the same units as part of the same recruitment drive. This dataset further includes evidence for 156 spouses and 47 children. While military records do mentions these, their number suggests that the Habsburg army preferred to enlist unmarried men.

The database is structured in a similar way to an official individual entry in the Habsburg military records. These were arranged in tables, with soldiers listed by seniority. Name, place and land of birth are followed by age and religion. This latter rubric allows identifying the bulk of the Jewish soldiers. Also included in the record is marital status, profession (if any), number, names and ages of children (if any), followed by a short summary text of the soldier’s service itinerary. While not always consistent in detail, these texts mention enlistment dates, transfers between units, promotions, desertions, periods as prisoner of war and military awards (if any). I have taken the material from the personal records and added several additional parameters:

The soldiers are entered into the database according to their date of enlistment. This is followed by a colour-coded table showing their years of service. To see the meaning of the different colours employed, scroll to the legend at the end of the dataset.

Following the years of service, we see the date when the soldier left service (final year in service for incomplete service records). When known, the reason the soldier left the army is given (discharge/ death/ desertion etc).

Then come the three most important columns within the table: service record, primary sources and units. At first glance, these columns have only a few letters and numbers, but bring your mouse courser onto the relevant field marked with red triangles. An additional window will then open:

a. Service Record: Shows the entire service record of the soldier arranged by date. I use original German as it appears in the archival records. If you see spelling differences with modern German – they are there for a reason.

b. Primary Sources: Provides the information on all the archival records consulted to reconstruct the service itinerary. The number in the field denotes the number of the archival cartons consulted.

c. Units: Number of units in which a soldier serves. Bringing the cursor on to the field will open their list. Most Jewish soldiers served in the line infantry (IR) and the Military Transport Corps (MFWK or MFK). However, there were also Jewish sharpshooters, cavalrymen, gunners and even a few members of the nascent Austrian Navy.

The next two columns provide entries of the soldier’s conduct and medical condition, which in Habsburg military jargon was referred to rather callously as Defekten. I note the original medical diagnoses verbatim. When possible to identify, I note the modern medical term.

General database-wide parameters are then noted in the next part of the table. Among others, it provides information on enlistment type (conscript/ volunteer?), main branches of service (such as Infantry/ Cavalry/ Artillery), and roles within the military (such as non-commissioned officers/ drummers/ medics).

Concluding this part of the table are columns covering desertions, periods as prisoner of war and awards of the army cannon cross (for veterans of 1813-14) and other military awards.

The last column provides the original German outtake rubric as to how the soldier left service. In special cases, additional service notes are provides on the right.

How to use this dataset

This depends on what you are looking for. Firstly, download the dataset on to your computer via the link provided below. It is a simple Excel file which is easy to work with. If you wish to find out whether one of your ancestors served in the Habsburg army, use a simple keyword search. Please note that in our period there was no single accepted orthography meaning that some letters were used interchangeably (for instance B/P; D/T). There were also various patronymic suffices used in different parts of the monarchy (-witz in German/ -wicz in Polish/ -vits in Hungarian). Habsburg military clerks were mostly German speakers who often recorded the name phonetically. For instance, Jankel/ Jankl/ Jacob/ Jacobus all denote the same name. A Jewish teenager who identified himself as Moische when first reporting to duty, may have stayed so in the military records for decades, even if he was already a non-commissioned officer whose subordinates referred to as Herr Corporal.

If you study the history of concrete Jewish communities, use the keyword search and the filter option to find entries in the database where this locality is mentioned. Some places like Prague and Lublin could be identified effortlessly. In other cases (and see the above point on German-speaking clerks), place names were recorded phonetically. The military authority usually stuck to official Polish names in Galicia, and Hungarian in the Lands of the Crown of St. Stephan. In reality, a Jewish recruit from Transcarpathian Ruthenia could have his place of birth recorded in Hungarian, Romanian or Rusin. When I could not identify the place in question, I marked it with italics. Do you think you identified something I could not? Excellent! Then please write me, and I will correct the entry in the next instalment of this database.

I should stress that, currently, the database is not statistically representative. I have worked chronologically, meaning that there are disproportionally more entries for Jewish soldiers from the Turkish War, the first two Coalition Wars, and the Wars of 1805 and 1809. If you look at some of my other databases (for instance, that of the 1st Line Infantry Regiment 'Kaiser'), you will find least as many Jews who served in the wars of 1813-15. I will cover these in due course. This said, using the filter option of the Excel sheet, you can already make some individual queries. For instance, did Jewish grenadiers meet the minimal height requirement to be eligible for transfer into the elite infantry? (Hint: they did not!) If you are interested in the historical study of nutritional standards, compare the height of the soldiers with their year and place of birth. In my other project, I made calculations of the average height of Habsburg soldiers and I can already reveal that Jewish conscripts were, on average, several centimetres smaller than their non-Jewish comrades drafted in the same annual intake. Whatever stereotypes said, most Jews in the Habsburg Monarchy around 1800 were very poor and the sad fact of malnutrition as a child is reflected in their height as adults.

I should stress that this is a cumulative database. ZENODO has an excellent feature allowing updated versions to supersede earlier files while retaining the same DOI (Digital Object Identifier) and metadata. As my research progresses, I plan to upload new versions of this database bi-annually. This includes not only adding new entries, but also expanding and correcting existing ones. It might well be that the service record of a soldier covered up to 1806 will be brought to a later date, possibly even to his discharge from the army. If you have not found whom you are looking for, or if you want to work with larger samples for your research, visit this page again in a few months’ time. And if you do use this database for scholarly research (by all means, please do), do not forget to cite it as you would cite any other item in your bibliography! If you are a museum professional and you want to employ material from your database to illustrate your exhibitions, you are welcome, but please cite this resource for others to learn. Links to this database will also be appreciated.

We evaluated gendered risks and the associations between demographics, anxiety, and depressive symptoms, military sexual trauma (MST), and severe pain in Canadian military personnel and veterans. A cross-sectional sample of 328 veterans and military members with chronic pain completed confidential surveys, including demographics, disability, pain location, and intensity, as well as a Patient Health Questionnaire (PHQ-4). We evaluated gendered exposures (rates, odds ratios), associations between MST and severe pain (logistic regression), and associations with pain severity and PHQ-4 (linear regression), adjusting for age, race, and education. We found high levels of chronic pain and disability for women and men (6.8 or 6.9/10), PHQ-4 scores (6.6 or 6.2/12), and SANE scores (11% or 10%). Women reported higher exposure to MST (e.g. 69% for sexual harassment, 76% for emotional abuse) than men (8% and 13%). Odds of severe pain were not gendered (OR = 1.05). Physical abuse (β = 1.09, p = .030) and gender discrimination (β = 1.10, p = .033) were associated with higher PHQ-4 scores. Sexual harassment was not independently associated (OR = 1.57, p = .354) with chronic pain, but was associated with increased anxiety and depressive symptoms (β = 1.06, p = .016). Higher PHQ-4 scores were significantly associated with higher pain scores (β = 0.18, p < .001). MST exposures are much more common among women, but the associations of MST with severe pain and PHQ-4 scores are similar across genders. Differences in operational trauma and occupational trauma may influence occupational stress injuries and chronic pain.

The high prevalence of dual use of cigarettes and smokeless tobacco is a unique tobacco use behavior in the US military population. However, dual tobacco use has rarely been addressed in active duty populations. We aimed to identify factors contributing to dual tobacco use among active duty service members from Army and Air Force. We also compared age at initiation, duration of use, and amount of use between dual users and exclusive users. The study included 168 exclusive cigarette smokers, 171 exclusive smokeless tobacco users, and 110 dual users. In stepwise logistic regression, smokeless tobacco use among family members (OR = 4.78, 95% CI = 2.05–11.13 for father use vs. no use, OR = 3.39, 95% CI = 1.56–7.37 for other relatives use vs. no use), and deployment history (serving combat unit vs. combat support unit: OR = 4.12, 95% CI = 1.59–10.66; never deployed vs. combat support unit: OR = 3.32, 95% CI = 1.45–7.61) were factors identified to be associated with dual use relative to exclusive cigarette smoking. Cigarette smoking among family members (OR = 1.96, 95% CI = 1.07–3.60 for sibling smoking), high perception of harm using smokeless tobacco (OR = 2.34, 95% CI = 1.29–4.26), secondhand smoke exposure (OR = 4.83, 95% CI = 2.73–8.55), and lower education (associated degree or some college: OR = 2.76, 95% CI = 1.01–7.51; high school of lower: OR = 4.10, 95% CI = 1.45–11.61) were factors associated with dual use relative to exclusive smokeless tobacco use. Compared to exclusive cigarette smokers, dual users started smoking at younger age, smoked cigarettes for longer period, and smoked more cigarettes per day. Our study addressed dual tobacco use behavior in military population and has implications to tobacco control programs in the military.

Military Personnel Command (MPC) supports the requirement to release accurate and timely information to Canadians, in line with the principles of Open Government. This dataset represents the recruits of Officers and Non-Commissioned Members (NCMs) joining the Canadian Armed Forces (CAF) Regular Force from 1997 to 2022. 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. The Commander of Military Personnel Command (MILPERSCOM) is also appointed as the Chief of Military Personnel (CMP).

This dataset is about countries per year in South America. It has 768 rows. It features 3 columns: country, and armed forces personnel.