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.

The United States Military Installations database contains the boundaries and location information for important military installations in the United States and Puerto Rico. The database includes records for 405 military installations.

Purpose

To provide graphic representation, location and attribute data for analysis, modeling and simulation, and studies. CLOSURE, REALIGN, and BRAC columns are from the office of Economic Adjustment and OSD websites at http://www.oea.gov , https://www.denix.osd.mil .

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Census Bureau includes landmarks such as military installations in the MTDB for locating special features and to help enumerators during field operations. In 2012, the Census Bureau obtained the inventory and boundaries of most military installations from the U.S. Department of Defense (DOD) for Air Force, Army, Marine, and Navy installations and from the U.S. Department of Homeland Security (DHS) for Coast Guard installations. The military installation boundaries in this release represent the updates the Census Bureau made in 2012 in collaboration with DoD.

This simulated combat reports dataset combines fictional headings, reporting units, and attack times with real data from 551 records of terrorist attacks in Afghanistan (2009–2010) [1]. The dataset combines selected attributes from the DA Form 1594 [2] and U.S. Army Spot Report [3]. The dataset also includes additional attributes for tactical context.A common use for the DA Form 1594 is for personnel in U.S. Army small unit command posts to record reports of combat action. This dataset was used in a prototype of a modified and modernized DA Form 1594 created as a Relational Database Management System (RDBMS). The prototype was designed to demonstrate how to use data science techniques to analyze tactical data captured in small unit command posts. The RDBMS was created using Microsoft Access. The .xlsx dataset was exported from the final version of the prototype.References[1] National Consortium for the Study of Terrorism and Responses to Terrorism, "Global Terrorism Database," University of Maryland, 2019. [Online]. Available: https://www.start.umd.edu/gtd/. [Accessed 23 November 2019].[2] Department of the Army, "Appendix E: Daily Staff Journal or Duty Officer's Log," in TC 3-22.6: Guard Duty, Washington, DC: Department of the Army, 2017 (w/Change 1, 2019), pp. E-1 - E-3.[3] Department of the Army, "U.S. Army Spot Report," in FM 6-99.2: U.S. Army Report and Message Formats, Washington, DC: Department of the Army, 2007, pp. 219 - 219.1.

United States US: Military Expenditure: % of GDP data was reported at 3.149 % in 2017. This records a decrease from the previous number of 3.222 % for 2016. United States US: Military Expenditure: % of GDP data is updated yearly, averaging 4.864 % from Sep 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9.063 % in 1967 and a record low of 2.908 % in 1999. United States US: Military Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Defense and Official Development Assistance. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; Weighted average; Data for some countries are based on partial or uncertain data or rough estimates.

The Military Bases dataset was last updated on October 23, 2024 and are defined by Fiscal Year 2023 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.

United States US: Military Expenditure data was reported at 609.758 USD bn in 2017. This records an increase from the previous number of 600.106 USD bn for 2016. United States US: Military Expenditure data is updated yearly, averaging 277.591 USD bn from Sep 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 711.338 USD bn in 2011 and a record low of 45.380 USD bn in 1960. United States US: Military Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Defense and Official Development Assistance. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; ; Data for some countries are based on partial or uncertain data or rough estimates. For additional details please refer to the military expenditure database on the SIPRI website: https://sipri.org/databases/milex

United States US: Armed Forces Personnel: Total data was reported at 1,348,400.000 Person in 2016. This records an increase from the previous number of 1,347,300.000 Person for 2015. United States US: Armed Forces Personnel: Total data is updated yearly, averaging 1,546,000.000 Person from Sep 1985 (Median) to 2016, with 29 observations. The data reached an all-time high of 2,240,000.000 Person in 1989 and a record low of 1,347,300.000 Person in 2015. United States US: Armed Forces Personnel: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.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.

The dataset depicts the authoritative locations 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 2009 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.

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

Military Expenditure in the United States increased to 876943.20 USD Million in 2022 from 806230.20 USD Million in 2021. United States Military Expenditure - values, historical data, forecasts and news - updated on March of 2025.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Census Bureau includes landmarks such as military installations in the MTDB for locating special features and to help enumerators during field operations. In 2008, the Census Bureau obtained the inventory and boundaries of most military installations from the U.S. Department of Defense (DOD) for Air Force, Army, Marine, and Navy installations and from the U.S. Department of Homeland Security (DHS) for Coast Guard installations. In a few cases, the files supplied to the Census Bureau contained older, unverified information than that obtained from the DOD for Census 2000; in those cases the military installations in MTDB were reviewed, but not updated to match the files obtained in 2008.

Wetland Delineation Regions of the U.S. Army Corps of EngineersImportant Note: This item is in mature support as of December 2024 and will be retired in April 2024.This feature layer, utilizing data from the U.S. Army Corps of Engineers (USACE), displays Wetland delineation regions. Per USACE, "Recognizing the potential for continued or accelerated degradation of the Nation's waters, the U.S. Congress enacted the Clean Water Act (here after referred to as the Act), formerly known as the Federal Water Pollution Control Act (33 U.S.C. 1344). The objective of the Act is to maintain and restore the chemical, physical, and biological integrity of the waters of the United States. Section 404 of the Act authorizes the Secretary of the Army, acting through the Chief of Engineers, to issue permits for the discharge of dredged or fill material into the waters of the United States, including wetlands." The USACE utilizes their Wetland Delineation Manual to delineate these regions based on three parameters: hydric soils, wetland hydrology and hydrophytic plants (plants that grow in water or in soil that is consistently wet). This manual provides the Corps Districts with a uniform way to delineate wetlands.Eastern Mountains and PiedmontData currency: This cached Esri service is checked monthly for updates from its federal source (USACE Wetland Delineation Regions)Data modification: NoneFor more information:Regional Supplements to Corps Delineation ManualField Indicators of Hydric Soils in the United StatesCorps of Engineers Wetlands Delineation ManualFor feedback: ArcGIScomNationalMaps@esri.comU.S. Army Corp of EngineersPer USACE, "With environmental sustainability as a guiding principle, our disciplined Corps team is working diligently to strengthen our Nation’s security by building and maintaining America’s infrastructure and providing military facilities where our servicemembers train, work and live. We are also researching and developing technology for our war fighters while protecting America’s interests abroad by using our engineering expertise to promote stability and improve quality of life."

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.

NARA PoW Data W.D. A.G.O. FORM NO. 0326.

This deposit contains a dataset relating to persons interned between December 7, 1941 and November 19, 1946, which has been enhanced to make it more accessible to scientists. It is based on information from the U.S. National Archives and Records Administration (NARA), which is unrestricted and available at https://aad.archives.gov/aad/series-description.jsp?s=644&popup=Y, and informs this summary. The NARA 'series' is part of Record Group 389: Records of the Office of the Provost Marshal General. It identifies 79 'places of capture' globally "Using copies of reports from the International Committee of the Red Cross ...". The Scope & Content Note states:

"This series has information about U.S. military officers and soldiers and U.S. and some Allied civilians who were prisoners of war and internees. The record for each prisoner provides serial number, personal name, branch of service or civilian status, grade, date reported, race, state of residence, type of organization, parent unit number and type, place of capture (theater of war), source of report, status, detaining power, and prisoner of war or civilian internee camp site. Records of prisoners of the Japanese who died also document whether the prisoner was on a Japanese ship that sank or if he or she died during transport from the Philippine Islands to Japan. There are no records for some prisoners of war whose names appear in the lists or cables transmitted to the Office of the Provost Marshal General by the International Committee of the Red Cross."

The U.S. War Department used punched cards to manage this information, although "The punch card records were transferred to NARA with virtually no agency documentation." According to the Custodial History Note:

"The U.S. Army transferred punch card records of World War II prisoners of war (POWs) to NARA as a unique series in its 1959 transfer of all of the U.S. Army's Departmental Archives. In 1978 the Veterans Administration borrowed most of the punch card records of repatriated U.S. military personnel for a study of Repatriated U.S. Military Prisoners of War, migrated the data on almost all of the borrowed cards to an electronic format and returned the punch cards and two electronic records data files to NARA. In 1995 NARA migrated the data from almost all of the remaining punch card records to an electronic format and has subsequently preserved all of the records in a single data file."

It is evident that the organization of this data file assumes access to other information, also accessible in CSV files in the series, in order to interpret detailed information, such as branch of service, grade, parent unit number and detaining power. For example, records appear in the following format:

O&745255ABDALLAH EDWARD A    2 LT  G1AC 200803413223003620O7222094171035  
32214872ABDALLAH JOSEPH T    CPL   61INF10230241231100157069802075181087  
36336867ABDAY JOSEPH C     PVT   81INF10170231611100168069516075181004 

constituting a serial number, then a name, then a textual code for rank; followed by a string, (starting G1AC on the first line) which encodes the remaining information. For example, the first digit (G) can be looked up in cl_1279.csv to decode ‘2nd lieutenant’, corroborating in this case the appearance of '2 LT. 'AC' indicates 'armofservicecode: AIR CORPS', but less obviously, 'detainingpower: Germany'; 'race: White' and 'theater: European Theater: Germany'. This single line is the entirety of the information provided per person instance by the NARA series. Users of this potentially valuable resource must develop automation in order to be able to search and employ it effectively; no such tools or specification from which software might be developed immediately is provided.

Significantly, this task is hampered by evidence of corruption of the some of the information, which may be due solely to the digitization process mentioned above being applied to the paper records, but possibly with subsequent contribution of fixity effects. NARA documentation does not refer to data integrity issues and, especially since the dataset which NARA provides is large, it may only be during development of automation to employ the series that such issues are discovered. Examples of problems include substitution of characters, such as 'O' replacing '0' and vice-versa; '}' replacing '3' and '&' replacing '8', or less obviously 'L' mis-recognized as '-' and 'II' replacing 'H'.

12138003 AREY GERALD J     S SG  41AC 2002064123S55}340069802055181033

Ideally, access to high resolution scans of the paper documents could be used to address these issues, or external documents. However, checking for completeness of each of the components of a person record enables detection of compromised entries and, where character substitution affects decoding of key information, other contextual information is often available to validate decoding such strings with these characters re-substituted. The larger percentage of strings which already decode plausibly without intervention do not contain incidences of such characters (so there is strong evidence that they are invalid in particular positions.

Unfortunately, there is a proportion of digital records with more severe corruption which cannot be addressed without access to scanned imagery of the paper records, for example:

O&557875ANDREW THOMAS A     2 LT  G1AC 2011094115     70140
 6881276AFTEWICZ EDWARD L    PVT   81INF10150231321100135069508065181004
   6 APLIN -OR-& -      3   1INF102  1       1 0 1 1

As of the initial date of this deposit is anticipated that such access will be possible to support further work on this series.

The dataset in this deposit does not contain records for which decoding is compromised to the extent that information to populate a basic person schema is incomplete. However, although 36,791 of the 143,374 person records in the NARA series were found to be compromised in some way, 19,624 of those have been substantially decoded and/or repaired and further work is being undertaken to both improve decoding of the 126,207 available here and to retrieve others among the 17,167 which are currently inaccessible.

This dataset has been enhanced to present the original NARA 'single data file' as a JSON resource which is more accessible for search and analysis, since each record is document-oriented (containing labels and values for each field, together with provenance information) for example:

{
  "$schema": "https://schemata.hasdai.org/historic-persons/historic-person-entry-v0.0.2.json",
  "location": [
   {
    "association": "military service",
    "transcription": "European Theater: Germany"
   },
   {
    "association": "interred",
    "transcription": "Stalag 2D Stargard Pomerania, Prussia 53-15"
   }
        ],
  "name": {
   "familyname": "AARON",
   "givenname": "JACK",
   "rank": "SGT",
   "transcription": "AARON JACK"
      },
  "set": {
   "id": "https://persons.freizo.org/export/pow/1.0.0",
   "partof": "10.5281/zenodo.3565392",
   "title": "WDAGO-0326"
      },
  "source": {
   "type": "data file"
       }
 },

The schema employed here serves a specific purpose, in addition to on-going work identifying and correcting errors in the NARA data: it supports work to discover other instances of persons appearing in this NARA series 0326, which also appear in external documentation. For example, in a separate collaborative project with Europa Institute at the University of Basel, a benchmark dataset has been produced based on listings of foreign residents in the Asian Directories and Chronicles, which forms a deposit at 10.5281/zenodo.2580997 and employs a compatible schema for the purpose of efficient comparison with this and other datasets. Other schemata could be employed for different purposes—leading to alternate datasets, all derived from series 0326. The full extent of information currently decoded from NARA series 0326 is presented at https://pow.freizo.org/ which provides search facilities by person name, plus interactive filters for person rank, service and theater of conflict.

FUDS Property points represent the location of properties that were formerly owned by, leased to or otherwise possessed by the United States and under the jurisdiction of the Secretary of Defense prior to October 1986. Such properties are known as Formerly Used Defense Sites or FUDS. The U.S. Army is DOD’s lead agent for the FUDS Program. The U.S. Army Corps of Engineers executes the FUDS Program on behalf of the U.S. Army and DOD. The U.S. Army and DOD are dedicated to protecting human health and the environment by investigating and, if required, cleaning up potential contamination or munitions that may remain on these properties from past DOD activities. Currently, not all properties have location information available.

FUDS Property polygon contains polygon features representing approximate boundaries of Formerly Used Defense Sites (FUDS) Properties. These polygons represent the location of properties that were formerly owned by, leased to or otherwise possessed by the United States and under the jurisdiction of the Secretary of Defense prior to October 1986. Such properties are known as Formerly Used Defense Sites or FUDS. The U.S. Army is DOD’s lead agent for the FUDS Program. The U.S. Army Corps of Engineers executes the FUDS Program on behalf of the U.S. Army and DOD. The U.S. Army and DOD are dedicated to protecting human health and the environment by investigating and, if required, cleaning up potential contamination or munitions that may remain on these properties from past DOD activities. Currently, not all properties have location information available.

The FUDS MRS dataset contains location information for the Formerly Used Defense Sites (FUDS) Munitions Response Sites (MRSs). MRSs are discrete sites associated with a FUDS property that may require a muntions response (response actions, including investigation, removal actions, and remedial actions, to address the explosives safety, human health, or environmental risks presented by UXO, discarded military munitions (DMM), or munitions constituents (MC), or to support a determination that no removal or remedial action is required.) Location information is subject to change as new data become available.

FUDS Project point describes the approximate location of a potential environmental restoration activity. These projects are associated with a single FUDS property (a property may have several or zero projects associated with it). Location information is not available for all properties, additional information will be added as it becomes available. Types of projects include: Hazardous, toxic and radioactive waste (HTRW); Building demolition and/or debris removal (BD/DR); Military munitions response program (MMRP); Containerized hazardous, toxic and radioactive waste (CON/HTRW), such as underground storage tanks; and, Principal responsible party actions (PRP), which involves defense of government interests or cost recovery on behalf of the government associated with CERCLA contamination requiring cleanup on a FUDS property.

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 09, 2017, the USGS conducted an oblique aerial photographic survey of the U.S. Army Corps of Engineers Field Research Facility (USACE FRF), located in Duck, North Carolina, aboard a Cessna 182 aircraft at an altitude of approximately 1000 feet (ft). This mission was conducted to collect data for USACE FRF Duck Unmanned Aerial Systems (UAS) Open Field Experiment, carried out June 5–21, 2017. The photographs provided are Joint Photographic Experts Group (JPEG) and Nikon Electronic Format (NEF) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the feature in the images. These photographs document the configuration of the USACE FRF at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. JPEG photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC). In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then the thumbnail or the link above the thumbnail. This KML, 2017-033-FA.kml, can be found in 2017-033-FA-SupplementalFiles.zip.

In 2016, the U.S. Army Corps of Engineers (USACE) started collecting high-resolution multibeam echosounder (MBES) data on Lake Koocanusa. The survey originated near the International Boundary (River Mile (RM) 271.0) and extended down the reservoir, hereinafter referred to as downstream, about 1.4 miles downstream of the Montana 37 Highway Bridge near Boulder Creek (about RM 253). USACE continued the survey in 2017, completing a reach that extended from about RM 253 downstream to near Tweed Creek (RM 244.5). In 2018, the U.S. Geological Survey (USGS) Idaho Water Science Center completed the remaining portion of the reservoir from RM 244.5 downstream to Libby Dam (RM 219.9). The MBES data collected in 2016 and 2017 by the USACE was combined with the MBES data collected in 2018 by the USGS. The USGS also developed a stage-area-capacity table at one-foot intervals from the minimum pool elevation (2,290.84 ft) to the maximum pool elevation (2462.84 ft) using the new bathymetry data. The updated stage-area-capacity table will be compared to the current usable storage estimate of 4,979,500 acre-feet and published in a USGS Scientific Investigations Map. A 10-ft digital elevation model (DEM) and minimum and maximum pool contours also were generated from the bathymetric data and are provided in this data release.