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.

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.

This dataset represents the total number of Female 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.

Percentage representation of Employment Equity (EE) Designated Group Members (DGM) in the Canadian Armed Forces(CAF). Data is grouped by CAF component and Rank Category (Officer or Non-commissioned Member) as well as by Designated Environmental Percentage representation of Employment Equity (EE) Designated Group Members (DGM) in the Canadian Armed Forces(CAF). Data is grouped by CAF component and Rank Category (Officer or Non-commissioned Member) as well as by Designated Environmental Uniform (DEU). These DEUs are Sea (Royal Canadian Navy), Land (Canadian Army) and Air (Royal Canadian Air Force).

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.

Over the course of the Second World War approximately 127.2 million people were mobilized. The world's population in 1940 was roughly 2.3 billion, meaning that between five and six percent of the world was drafted into the military in some capacity. Approximately one in every 25 people mobilized were women, who generally served in an administrative or medical role, although hundreds of thousands of women did see active combat. Largest armies In absolute numbers, the Soviet Union mobilized the largest number of people at just under 34.5 million, and this included roughly 35 percent of the USSR's male population. By the war's end, more Soviets were mobilized than all European Axis powers combined. However, in relative terms, it was Germany who mobilized the largest share of its male population, with approximately 42 percent of men serving. The USSR was forced to find a balance between reinforcing its frontlines and maintaining agricultural and military production to supply its army (in addition to those in annexed territory after 1941), whereas a large share of soldiers taken from the German workforce were replaced by workers drafted or forcibly taken from other countries (including concentration camp prisoners and PoWs). Studying the figures The figures given in these statistics are a very simplified and rounded overview - in reality, there were many nuances in the number of people who were effectively mobilized for each country, their roles, and their status as auxiliary, collaborative, or resistance forces. The British Empire is the only power where distinctions are made between the metropole and its colonies or territories, whereas breakdowns of those who fought in other parts of Asia or Africa remains unclear. Additionally, when comparing this data with total fatalities, it is important to account for the civilian death toll, i.e. those who were not mobilized.

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 outflow of Female Officers and Non-Commissioned Members (NCMs) leaving 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 contains the tweet ids of 384,350 tweets, including tweets between October 1, 2020 and December 31, 2020. 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.

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

The female interviewee is a woman from northwest Bosnia, born in 1960. She was two years old when her family moved to Kostrići. In 1979 she went to Zagreb in order to work as a nurse. She got married and lived a quiet life with her husband and children until she noticed that things were changing at work. All of a sudden it was relevant if another nurse was Serbian, for example. This never was an issue before. She describes more examples of this segregation, from a personal perspective. When the war started, her parents wanted to stay in Kostrići. It was here that they were killed during the war. She herself continued working and took care of their two young children, while her husband joined the army. It was a difficult and scary time, because she never knew whether her children were safe during the air raids, when she had to stay in the hospital. After Operation Storm she went to search for her family in Kostrići, together with her brother who had been in the army. They found the remains of their parents. Their sister and her young family are still missing, they found out that their sisters’ house was burned to the ground. Her feeling about the war is that it may have ended officially, but a lot of people still feel the hatred because of what people have done to each other in these years

Military service status by Indigenous identity, age and gender of the population aged 17 years and over in private households.

Abstract copyright UK Data Service and data collection copyright owner. To study psychometric properties of two scales devised in Spain with the purpose of assessing the structure of social attitudes in that country. Main Topics: Variables The study uses two scales: 1) Burgaleta's (1976) RD40 scale which purports to measure two supposedly orthogonal factors: radicalism (as opposed to conservatism) and dogmatism (as opposed to flexibility). This was tested on 764 subjects (335 male and 420 female) attending a social psychology seminar at Madrid's Complutense University. 2) SPS or Spanish Psychosocial Scale (Giorgi and Seisdedos, 1982) which attempts to operationalize attitudes towards politics, religion, sexual practices, foreigners, military institutions and a wide range of controversial social issues. This was tested on a sample of 1409 subjects made up of 764 university undergraduates plus a group of 645 non-students. The latter comprised 365 males and 280 females, the males being drawn from the Spanish Army. (Military service is compulsory in Spain, and conscripts come from various regions). See under Population Face-to-face interview Self-completion

BackgroundMental health during armed conflicts is of paramount importance, as such situations often lead to increased risks of anxiety and depression symptoms among civilians. The military conflict between the Sudanese army and Rapid Support Forces, which began on April 15, 2023, is currently ongoing mainly in Khartoum State. Despite the significant impact of the conflict on the region, there is a lack of data regarding the mental health status of the residents. The aim of this study is to assess anxiety and depression symptoms among residents of Khartoum State during the first months of the 2023 military conflict.MethodWe conducted a cross-sectional study among residents of Khartoum State between May 27 and June 19 using an online questionnaire. We used standardized screening questionnaires, namely the Generalized Anxiety Disorder (GAD-7) for anxiety and the Patient Health Questionnaire (PHQ-9) for depression. Multiple logistic regression was used to identify sociodemographic factors that are associated with anxiety and depression symptoms.ResultsOut of the 393 participants in the study, 70% had symptoms suggestive of depression and 57.3% suffered from anxiety symptoms. Both anxiety and depression were associated with being female (p < 0.001). Being married was a predictor of anxiety (p = 0.028) but not depression (p = 0.3). Other predictors were not significant (p > 0.05).ConclusionHigh levels of anxiety and depression symptoms were prevalent among Khartoum residents during the conflict, with females and married individuals at higher risk. Immediate medical assessment is essential for identifying cases and providing support. Mental health services should be integrated into emergency response efforts, particularly focusing on vulnerable groups. Future research should address study limitations and explore coping strategies for anxiety and depression in Sudanese adults.