Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2023.

Separations and men's violence against women is a study that aims to increase knowledge about the connection between men's violence against women and separations. The study is a thesis at the University of Gothenburg and has been carried out with financial support from the Crime Victims Fund. The study is based on questionnaires collected from over 350 women who have separated or gone through a divorce from a heterosexual relationship. In order to get in touch with these women, the researcher has worked together with various social agencies that women who have been subjected to separation violence can come into contact with; police, women's shelters, crime victim shelters, social services and family law agencies. Questionnaires have also been sent directly to women in the Gothenburg area who have gone through a divorce.

The study includes two questionnaires. Questionnaire 2 was sent after 6 months to those women who expressed interest in participating further via Questionnaire 1. The questionnaires are designed to cover different periods of the relationship and separation. They include questions about the man's behavior in certain situations, for example during discussions about joint children or assets, how conflicts have been handled, physical, psychological and sexual violence and the relationship as a whole.

The results of the study show that 60 percent of the women respondents have at some point been subjected to physical violence by the man they separated from. One purpose of the study is to investigate whether it is possible to predict the risk of violence after a separation. The study shows that the risk assessment model, to assess how great the risk is that the woman will be subjected to violence even after the separation, which is used by staff at women's shelters and crime victim shelters, is preferable to the police's way of working. It also shows that negotiations in family law are affected if the man has used violence. It often turns out that the woman lowers her demands, for example regarding custody of joint children, if the man has used violence against her.

Purpose: To investigate the extent of men's violence against women in connection with and after separations, separation violence, in order to be able to say something about the risk of violence that women who are considering leaving a heterosexual relationship face.

The purpose of this study was to use data from the National Crime Survey (NCS) and the National Crime Victimization Survey (NCVS) to explore whether the likelihood of police notification by rape victims had increased between 1973-2000. To avoid the ambiguities that could arise in analyses across the two survey periods, the researchers analyzed the NCS (1973-1991) and NCVS data (1992-2000) separately. They focused on incidents that involved a female victim and one or more male offenders. The sample for 1973-1991 included 1,609 rapes and the corresponding sample for 1992-2000 contained 636 rapes. In their analyses, the researchers controlled for changes in forms of interviewing used in the NCS and NCVS. Logistic regression was used to estimate effects on the measures of police notification. The analyses incorporated the currently best available methods of accounting for design effects in the NCS and NCVS. Police notification served as the dependent variable in the study and was measured in two ways. First, the analysis included a polytomous dependent variable that contrasted victim reported incidents and third-party reported incidents, respectively, with nonreported incidents. Second, a binary dependent variable, police notified, also was included. The primary independent variables in the analysis were the year of occurrence of the incident reported by the victim and the relationship between the victim and the offender. The regression models estimated included several control variables, including measures of respondents' socioeconomic status, as well as other victim, offender, and incident characteristics that may be related both to the nature of rape and to the likelihood that victims notify the police.

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.

In 1996 the Institute for Law and Justice (ILJ) began an evaluation of the law enforcement and prosecution components of the "STOP Violence Against Women" grant program authorized by the Violence Against Women Act of 1994. This data collection constitutes one component of the evaluation. The researchers chose to evaluate two specialized units and two multi-agency team projects in order to study the local impact of STOP on victim safety and offender accountability. The two specialized units reflected typical STOP funding, with money being used for the addition of one or two dedicated professionals in each community. The Dane County, Wisconsin, Sheriff's Office used STOP funds to support the salaries of two domestic violence detectives. This project was evaluated through surveys of domestic violence victims served by the Dane County Sheriff's Office (Part 1). In Stark County, Ohio, the Office of the Prosecutor used STOP funds to support the salary of a designated felony domestic violence prosecutor. The Stark County project was evaluated by tracking domestic violence cases filed with the prosecutor's office. The case tracking system included only cases involving intimate partner violence, with a male offender and female victim. All domestic violence felons from 1996 were tracked from arrest to disposition and sentence (Part 2). This pre-grant group of felons was compared with a sample of cases from 1999 (Part 3). In Hillsborough County, New Hampshire, a comprehensive evaluation strategy was used to assess the impact of the use of STOP funds on domestic violence cases. First, a sample of 1996 pre-grant and 1999 post-grant domestic violence cases was tracked from arrest to disposition for both regular domestic violence cases (Part 4) and also for dual arrest cases (Part 5). Second, a content analysis of police incident reports from pre- and post-grant periods was carried out to gauge any changes in report writing (Part 6). Finally, interviews were conducted with victims to document their experiences with the criminal justice system, and to better understand the factors that contribute to victim safety and well-being (Part 7). In Jackson County, Missouri, evaluation methods included reviews of prosecutor case files and tracking all sex crimes referred to the Jackson County Prosecutor's Office over both pre-grant and post-grant periods (Part 8). The evaluation also included personal interviews with female victims (Part 9). Variables in Part 1 (Dane County Victim Survey Data) describe the relationship of the victim and offender, injuries sustained, who called the police and when, how the police responded to the victim and the situation, how the detective contacted the victim, and services provided by the detective. Part 2 (1996 Stark County Case Tracking Data), Part 3 (1999 Stark County Case Tracking Data), Part 4 (Hillsborough County Regular Case Tracking Data), Part 5 (Hillsborough County Dual Arrest Case Tracking Data), and Part 8 (Jackson County Case Tracking Data) include variables on substance abuse by victim and offender, use of weapons, law enforcement response, primary arrest offense, whether children were present, injuries sustained, indictment charge, pre-sentence investigation, victim impact statement, arrest and trial dates, disposition, sentence, and court costs. Demographic variables include the age, sex, and ethnicity of the victim and the offender. Variables in Part 6 (Hillsborough County Police Report Data) provide information on whether there was an existing protective order, whether the victim was interviewed separately, severity of injuries, seizure of weapons, witnesses present, involvement of children, and demeanor of suspect and victim. In Part 7 (Hillsborough County Victim Interview Data) variables focus on whether victims had prior experience with the court, type of physical abuse experienced, injuries from abuse, support from relatives, friends, neighbors, doctor, religious community, or police, assistance from police, satisfaction with police response, expectations about case outcome, why the victim dropped the charges, contact with the prosecutor, criminal justice advocate, and judge, and the outcome of the case. Demographic variables include age, race, number of children, and occupation. Variables in Part 9 (Jackson County Victim Interview Data) relate to when victims were sexually assaulted, if they knew the perpetrator, who was contacted to help, victims' opinions about police and detectives who responded to the case, contact with the prosecutor and victim's advocate, and aspects of the medical examination. Demographic variables include age, race, and marital status.

This dataset contains the tweet ids of 1,482,343 tweets with the hashtag #believesurvivors. 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 2 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 dataset contains the tweet ids of 464,168 tweets, including tweets between April 1, 2021 and June 30, 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.

This dataset contains the tweet ids of 4,416,666 tweets, including tweets between October 17, 2017 and December 31, 2017. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.These tweets were licensed from Twitter's Historical PowerTrack and received through GNIP.Please note that there will be no updates to this datasetBecause of the size of the files, the list of identifiers are split in 5 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 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 dataset contains the tweet ids of 6,007,686 tweets, including tweets between July 1, 2018 and September 31, 2018. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.These tweets were licensed from Twitter's Historical PowerTrack and received through GNIP.Please note that there will be no updates to this datasetBecause of the size of the files, the list of identifiers are split in 7 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 dataset contains the tweet ids of 385,005 tweets, including tweets between January 1, 2022 and March 31, 2022. 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.