FBI National Incident-Based Reporting System (FBI NIBRS) crime data for Tampa-St. Petersburg-Clearwater, FL Metropolitan Statistical Area (MSA), including incidents, statistics, demographics, and agency information across multiple jurisdictions.

This study focused on the effect of economic resources and racial/ethnic composition on the change in crime rates from 1970-2004 in United States cities in metropolitan areas that experienced a large growth in population after World War II. A total of 352 cities in the following United States metropolitan areas were selected for this study: Atlanta, Dallas, Denver, Houston, Las Vegas, Miami, Orange County, Orlando, Phoenix, Riverside, San Bernardino, San Diego, Silicon Valley (Santa Clara), and Tampa/St. Petersburg. Selection was based on the fact that these areas developed during a similar time period and followed comparable development trajectories. In particular, these 14 areas, known as the "boomburbs" for their dramatic, post-World War II population growth, all faced issues relating to the rapid growth of tract-style housing and the subsequent development of low density, urban sprawls. The study combined place-level data obtained from the United States Census with crime data from the Uniform Crime Reports for five categories of Type I crimes: aggravated assaults, robberies, murders, burglaries, and motor vehicle thefts. The dataset contains a total of 247 variables pertaining to crime, economic resources, and race/ethnic composition.

FBI National Incident-Based Reporting System (FBI NIBRS) crime data for University of South Florida: St. Petersburg (University or College) in Florida, including incidents, statistics, demographics, and detailed incident information.

The data for this study were collected in order to examine the delivery of police services in selected neighborhoods. Performances of police agencies organized in different ways were compared as they delivered services to different sets of comparable neighborhoods. For Part 1, Citizen Debriefing Data, data were drawn from telephone interviews conducted with citizens who were involved in police-citizen encounters or who requested police services during the observed shifts. The file contains data on the citizens involved in observed encounters, their satisfaction with the delivered services, and neighborhood characteristics. This file includes variables such as the type of incident, estimated property loss, police response time, type of action taken by police, citizen satisfaction with the handling of the problem by police, reasons for dissatisfaction, the emotional state of the citizen during the encounter, whom the officers referred the citizen to for help, the citizen's prior contacts with police, and the citizen's education, age, sex, and total family income. Part 2, General Shift Information, contains data describing the shift (i.e., the eight-hour tour of duty to which the officers were assigned), the officers, and the events occurring during an observed shift. This file includes such variables as the total number of encounters, a breakdown of dispatched runs by type, the number of contacts with other officers, the number of contacts with non-police support units, officer discretion in taking legal action, and officer attitudes on patrol styles and activities. Part 3, Police Encounters Data, describes police encounters observed by the research team during selected shifts. It consists of information describing the officers' role in encounters with citizens observed during a shift and their demeanor toward the citizens involved. The file includes variables such as the type of encounter, how the encounter began, whether the citizens involved possessed a weapon, the encounter location, what other agencies were present during the encounter and when they arrived, police actions during the encounter, the role of citizens involved in the encounter, the demeanor of the officer toward the citizens during the encounter, actions taken by the citizens, which services were requested by the citizens, and how the observer affected the encounter. Part 4, Victimization Survey Data, examined citizen attitudes about the police and crime in their neighborhoods. The data were obtained through telephone interviews conducted by trained interviewers. These interviews followed a standard questionnaire designed by the project leaders. Variables include perceived risk of victimization, evaluations of the delivery of police services, household victimization occurring in the previous year, actions taken by citizens in response to crime, and demographic characteristics of the neighborhood.

Overview: The Crime and Punishment Text Corpus is a comprehensive dataset containing the full text of Fyodor Dostoevsky's classic novel "Crime and Punishment." This dataset is provided in plain text format, preserving the original language of the work. The primary objective of this dataset is to facilitate a thorough linguistic analysis of the book, allowing researchers, linguists, and language enthusiasts to delve into the intricacies of Dostoevsky's writing style, character development, and thematic elements.

Content: The dataset encompasses the entire narrative of "Crime and Punishment," including the dialogues, monologues, and descriptive passages that constitute the rich tapestry of the novel. It includes the protagonist Rodion Raskolnikov's internal reflections, interactions with other characters, and the unfolding events in 19th-century St. Petersburg. The original Russian text is faithfully represented, capturing the nuances and idiosyncrasies of Dostoevsky's language.

Data Format: The dataset is provided in a plain text (txt) format, ensuring ease of access and compatibility with various text analysis tools and frameworks. Each line corresponds to a segment of the novel, making it convenient for users to navigate through the text for specific analyses.

Potential Uses:

1. Linguistic Analysis: Researchers can conduct in-depth linguistic analyses, exploring the syntactic structures, vocabulary richness, and rhetorical devices employed by Dostoevsky throughout the novel.

2. Sentiment Analysis: Investigate the emotional tone and sentiment expressed in different sections of the text, discerning the psychological nuances of the characters and the overall mood of the narrative.

3. Character Profiling: Explore the development of characters through their dialogues and actions, tracking the evolution of their language patterns and speech styles.

4. Thematic Exploration: Investigate the recurrence of themes, motifs, and symbols in the text, shedding light on the deeper layers of meaning embedded in Dostoevsky's narrative.

Stylistic Features: Analyze the author's stylistic choices, such as the use of symbolism, allegory, and narrative techniques, to gain insights into Dostoevsky's literary craftsmanship.

Acknowledgment: The dataset is compiled from the public domain text of "Crime and Punishment" and is made available for academic and research purposes. The original work, authored by Fyodor Dostoevsky, is a seminal piece of Russian literature and a classic exploration of morality, guilt, and redemption.

Researchers and enthusiasts are encouraged to utilize this dataset responsibly, giving due credit to the literary contributions of Fyodor Dostoevsky.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study contains Uniform Crime Report geocoded data obtained from St. Petersburg Police Department, Orlando Police Department, and Miami-Dade Police Department for the years between 2010 and 2014. The three primary goals of this study were: to determine whether Florida law HB 7095 (signed into law on June 3, 2011) and related legislation reduced the number of pain clinics abusively dispensing opioid prescriptions in the State to examine the spatial overlap between pain clinic locations and crime incidents to assess the logistics of administering the law The study includes: 3 Excel files: MDPD_Data.xlsx (336,672 cases; 6 variables), OPD_Data.xlsx (160,947 cases; 11 variables), SPPD_Data.xlsx (211,544 cases; 14 variables) 15 GIS Shape files (95 files total) Data related to respondents' qualitative interviews and the Florida Department of Health are not available as part of this collection. For access to data from the Florida Department of Health, interested researchers should apply directory to the FDOH.

The data for this study were collected in order to examine the delivery of police services in selected neighborhoods. Performances of police agencies organized in different ways were compared as they delivered services to different sets of comparable neighborhoods. For Part 1, Citizen Debriefing Data, data were drawn from telephone interviews conducted with citizens who were involved in police-citizen encounters or who requested police services during the observed shifts. The file contains data on the citizens involved in observed encounters, their satisfaction with the delivered services, and neighborhood characteristics. This file includes variables such as the type of incident, estimated property loss, police response time, type of action taken by police, citizen satisfaction with the handling of the problem by police, reasons for dissatisfaction, the emotional state of the citizen during the encounter, whom the officers referred the citizen to for help, the citizen's prior contacts with police, and the citizen's education, age, sex, and total family income. Part 2, General Shift Information, contains data describing the shift (i.e., the eight-hour tour of duty to which the officers were assigned), the officers, and the events occurring during an observed shift. This file includes such variables as the total number of encounters, a breakdown of dispatched runs by type, the number of contacts with other officers, the number of contacts with non-police support units, officer discretion in taking legal action, and officer attitudes on patrol styles and activities. Part 3, Police Encounters Data, describes police encounters observed by the research team during selected shifts. It consists of information describing the officers' role in encounters with citizens observed during a shift and their demeanor toward the citizens involved. The file includes variables such as the type of encounter, how the encounter began, whether the citizens involved possessed a weapon, the encounter location, what other agencies were present during the encounter and when they arrived, police actions during the encounter, the role of citizens involved in the encounter, the demeanor of the officer toward the citizens during the encounter, actions taken by the citizens, which services were requested by the citizens, and how the observer affected the encounter. Part 4, Victimization Survey Data, examined citizen attitudes about the police and crime in their neighborhoods. The data were obtained through telephone interviews conducted by trained interviewers. These interviews followed a standard questionnaire designed by the project leaders. Variables include perceived risk of victimization, evaluations of the delivery of police services, household victimization occurring in the previous year, actions taken by citizens in response to crime, and demographic characteristics of the neighborhood.