This comprehensive report chronicles the history of women in the military and as Veterans, profiles the characteristics of women Veterans in 2009, illustrates how women Veterans in 2009 utilized some of the major benefits and services offered by the Department of Veterans Affairs (VA), and discusses the future of women Veterans in relation to VA. The goal of this report is to gain an understanding of who our women Veterans are, how their military service affects their post-military lives, and how they can be better served based on these insights.

The Anthropometric Survey of US Army Personnel (ANSUR 2 or ANSUR II) data were published internally in 2012. They were made available publicly in 2017. They have replaced ANSUR I as the most comprehensive publicly available data set on body size and shape. They include 93 measures for over 6,000 adult US military personnel (4,082 men and 1,986 women). In contrast to the ANSUR I data, the new sample includes reservists. Despite the presence of reservists in the sample, it is still not an approximation of the US Civilian population. Consequently, while there is useful information here, designs and standards based on these data will not accommodate most user populations in the intended manner.

This dataset is collected from internet.

This dataset contains the tweet ids of 407,911 tweets, including tweets between October 1, 2021 and December 31, 2021. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.These tweets were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that there will be no updates to this dataset.The following list of terms includes the hashtags used to collect data for this dataset: #metoo, #timesup, #metoostem, #sciencetoo, #metoophd, #shittymediamen, #churchtoo, #ustoo, #metooMVMT, #ARmetoo, #TimesUpAR, #metooSociology, #metooSexScience, #timesupAcademia, and #metooMedicine.Be aware that previous quarters (up to the first quarter of 2020) only include one hashtag: #metoo.Per Twitter's Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets that are still available (not deleted by users) tools like Hydrator are available.There are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available by quarter, as well as a full dataset with a larger corpus of hashtags.

Context

Pakistan Drone Attacks (2004-2016)

The United States has targeted militants in the Federally Administered Tribal Areas [FATA] and the province of Khyber Pakhtunkhwa [KPK] in Pakistan via its Predator and Reaper drone strikes since year 2004. Pakistan Body Count (www.PakistanBodyCount.org) is the oldest and most accurate running tally of drone strikes in Pakistan. The given database (PakistanDroneAttacks.CSV) has been populated by using majority of the data from Pakistan Body Count, and building up on it by canvassing open source newspapers, media reports, think tank analyses, and personal contacts in media and law enforcement agencies. We provide a count of the people killed and injured in drone strikes, including the ones who died later in hospitals or homes due to injuries caused or aggravated by drone strikes, making it the most authentic source for drone related data in this region.

We will keep releasing the updates every quarter at this page.

Content

Geography: Pakistan

Time period: 2004-2016

Unit of analysis: Attack

Dataset: The dataset contains detailed information of 397 drone attacks in Pakistan that killed an estimated 3,558 and injured 1,333 people including 2,539 civilians.

Variables: The dataset contains Serial No, Incident Day & Date, Approximate Time of the attack, Specific Location, City, Province, Number of people killed who claimed to be from Al-Qaeeda, Number of people killed who claimed to be from Taliban, minimum and maximum count of foreigners killed, minimum and maximum count of civilians killed, minimum and maximum count of civilians injured, special mention (more details) and comments about the attack, longitude and latitude of the location. Sources: Unclassified media articles, hospital reports, think tank analysis and reports, and government official press releases.

Acknowledgements & References

Pakistan Body Count has been leveraged extensively in scholarly publications, reports, media articles and books. The website and the dataset has been collected and curated by the founder Zeeshan-ul-hassan Usmani. Users are allowed to use, copy, distribute and cite the dataset as follows: “Zeeshan-ul-hassan Usmani, Pakistan Body Count, Drone Attacks Dataset, Kaggle Dataset Repository, Jan 25, 2017.”

Past Research

Zeeshan-ul-hassan Usmani and Hira Bashir, “The Impact of Drone Strikes in Pakistan”, Cost of War Project, Brown University, December 16, 2014

Inspiration

Some ideas worth exploring:

• How many people got killed and injured per year in last 12 years?

• How many attacks involved killing of actual terrorists from Al-Qaeeda and Taliban?

• How many attacks involved women and children?

• Visualize drone attacks on timeline

• Find out any correlation with number of drone attacks with specific date and time, for example, do we have more drone attacks in September?

• Find out any correlation with drone attacks and major global events (US funding to Pakistan and/or Afghanistan, Friendly talks with terrorist outfits by local or foreign government?)

• The number of drone attacks in Bush Vs Obama tenure?

• The number of drone attacks versus the global increase/decrease in terrorism?

• Correlation between number of drone strikes and suicide bombings in Pakistan

Questions?

For detailed visit www.PakistanBodyCount.org

Or contact Pakistan Body Count staff at info@pakistanbodycount.org

This feature layer contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities. In this update the TRAUMA field was populated for 172 additional hospitals and helipad presence were verified for all hospitals.

This dataset contains the tweet ids of 24,443,707 tweets with the hashtag #metoo. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection, and contains tweets published between October 15, 2017 and March 31, 2020.Tweets between October 15, 2017 and December 10, 2018 were licensed from Twitter's Historical PowerTrack and received through GNIP. Tweets after December 10, 2018 were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that this is VERSION 1 of the dataset. New versions with updated data will be submitted at the end of each quarter.Because of the size of the files, the list of identifiers are split in 25 files containing 1,000,000 ids each.Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets still available (not deleted by users) tools like Hydrator are availableThere are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available in this dataverse

This study focuses on the gap between the military and civilian society. The surveys compare civilian and military values, attitudes, opinions, and perspectives and include a variety of topics about US civil-military relations, American foreign policy, and the use of military force. Other topics include social and religious values, domestic issues, national security policy, military professionalism, media and the military, confidence in institutions, and women in the military. Demographic ite ms include gender, year of birth, level of education, occupation, current enrollment status at a service academy or in ROTC, military service history, political views, political party identification, schooling of children, parent's education level, region of residence while growing up, race, and foreign officer status.

Context

This dataset contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands.

Content

This feature class/shapefile contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities.In this version any information contained in ADDRESS2 field found in earlier versions of this dataset has been merged with the ADDRESS field and the ADDRESS2 field has been deleted.In this update 75 additional records were added and the TRAUMA field was populated for 574 additional hospitals.

Acknowledgements

This dataset was downloaded on March 23, 2019 from: https://hifld-geoplatform.opendata.arcgis.com/datasets/a2817bf9632a43f5ad1c6b0c153b0fab_0

This dataset is provided by the Homeland Infrastructure Foundation-Level Data (HIFLD) without a license and for Public Use.

HIFLD Open GP - Public Health Shared By: jrayer_geoplatform Data Source: services1.arcgis.com

Users are advised to read the data set's metadata thoroughly to understand appropriate use and data limitations.

This feature class/shapefile contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities

This feature class/shapefile contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities. In this update 123 additional hospitals were added and 26 additional helipads were identified.

The US Department of Homeland Security, Homeland Infrastructure Foundations - Level Data (HIFLD) provided geographic shapefiles for United States hospitals. This feature class/shapefile contains locations of Hospitals for US territories of Puerto Rico. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities. This feature class/shapefile contains Hospitals derived from various sources (refer SOURCE field) for the Homeland Infrastructure Foundation-Level Data (HIFLD) database. This feature class/shape file has a one-to-many relationship class (HospitalsToTrauma) relate with the “Trauma_Levels” table. This table captures the relationship between Hospitals and the state trauma level designations. “Hospitals” is the origin using STATE as the primary key. “Trauma_Levels” table is the destination using STATE as the foreign key. This dataset is based on information from the period 20120605-20170329.

The complete dataset for 50 States can be obtained from the HIFLD website: https://hifld-dhs-gii.opendata.arcgis.com/datasets/5eafb083e43a457b9810c36b2414d3d3_0 The shape file metadata can be obtained from: https://www.arcgis.com/sharing/rest/content/items/5eafb083e43a457b9810c36b2414d3d3/info/metadata/metadata.xml?format=default&output=html

BackgroundThe military-to-civilian transition can be a challenging period for many service members; however, recent research suggests that female ex-service personnel (veterans) confront additional complexities during reintegration into civilian life. This systematic review aimed to identify and synthesise findings across qualitative studies exploring the impact of gender on this transition process.MethodsPeer-reviewed literature was drawn from a multi-database search, limited to qualitative studies. The studies included either female veterans or both male and female veterans aged 18 years or older who had previously served in the Armed Forces within the Five Eyes (FVEY) countries (Australia, Canada, New Zealand, the United Kingdom, and the United States). We used a Framework Analysis approach to guide the synthesis of the qualitative data. An assessment of study quality was conducted using the Joanna Briggs Institute (JBI) Qualitative Critical Appraisal Checklist for Qualitative Studies. The study protocol is registered with the Open Science Framework (registration: osf.io/5stuj).ResultsIn total, 10,113 articles were screened after the removal of duplicates, 161 underwent full-text review, with 19 meeting the eligibility criteria. The review identified eleven themes split across individual’s experience whilst serving and after transitioning out of the military service. Both male and female veterans discussed a period of acculturation when they joined service and adapted to military norms, culture and identity. Female veterans faced additional challenges at this stage centred on the conflict between feminine norms and the military masculine ideal. Upon leaving service both male and female veterans experienced a loss of military identity and purpose, and dissonance with civilian norms illustrating a military-civilian divide. For female veterans, adjustments and adaptations learned in the military clashed with civilian feminine norms and stereotypically male veteran culture. Female veterans also struggled with the legacies of gender inequality, discrimination, and sexual assault which affected their development of a female veteran identity and affected the provision of services designed to meet their needs as a female. Despite these challenges, female veterans’ expressed pride in their service and accomplishments.ConclusionsAny effort to improve the military-to-civilian transition should take account of the legacy of gender discrimination, especially within the military service, and the potential mismatch between historical civilian female norms and the more traditionally masculine norms of military life.DisclosuresThis project was supported by a grant from the Forces in Mind Trust (FiMT) 2202.