The study on security and crime in Saxony 2022 - main dataset - was conducted by Infas Institute for Applied Social Science on behalf of the Saxon Institute for Police and Security Research (SIPS). In the survey period from 27.04.2022 to 02.08.2022, the Saxon resident population aged 16 and over was asked about fear of crime and its causes in online interviews (CAWI) and in writing (self-completed questionnaire: paper). In addition to complex measurements of the perception of safety (personal/social, affective/cognitive/conative), including the fear of extremism and political crime, the survey covered the experience of being a victim, trust in various institutions, political participation behavior, the Big Five personality dimensions, the socio-demographics of the respondents and many other variables. The respondents were selected by means of a multi-stage random sample from the registers of the residents´ registration offices of selected municipalities. In order to enable small-scale analyses, a regional dataset is provided in addition to the main dataset. It contains coarsened socio-demographics and numerous regional macro variables (demographic and economic indicators, crime recorded by the police, density of clubs, etc.). The two data sets cannot be linked with each other. Further information can be found in the documentation of the study.
1. Residential area: Social cohesion in the residential area in terms of mutual help, trust, shared values and respect for law and order; feeling of safety at night and during the day in the residential area; assessment of various impairments of the residential area (salience: Litter lying around, bulky waste left without permission, graffiti, unkempt front gardens or green spaces, dog excrement on sidewalks and green spaces, vandalism, broken lighting on streets or in parks, discarded syringes or needles on streets and sidewalks); frequency of perception of these impairments in the last 12 months; assessment of social problems in the residential area (groups of young people standing or sitting around, homeless people or beggars, noise on the street, drug addicts or drug dealers, fights or (groups of young people standing or sitting around, homeless or beggars, noise on the street, drug addicts or drug dealers, fights or brawls, adult cyclists, inline skaters and roller skaters on sidewalks, loose and stray dogs, public urination, illegal parking); frequency of perception of these social problems in the last 12 months.

1. Crime in Saxony and own security (based on the last 12 months): Crime development in Saxony in general; assessment of this development; development of various crimes (theft, burglary, assault, murder, fraud outside the Internet, damage to property, sexual abuse, blackmail, stalking, sexual harassment, Internet crimes in general, insults, coercion and threats in social media, terrorist attacks, drug-related crime, crimes near the border with Poland or the Czech Republic, crimes against public officials or rescue workers, crimes against politicians); fear of crime with regard to the aforementioned offenses; assessment of the risk of becoming a victim of various offenses (e.g. assault, burglary in apartment or house, robbery, theft, etc.); avoidance behavior (avoid certain streets, squares and parks, avoid people who appear threatening, take detours, avoid walking alone in the dark, carry repellents with me, secure my home, avoid expressing political opinions at public events/on social media, avoid spreading political content on social media).

2. Politically motivated crime and extremism: Assessment of the threat to the democratic order in Germany posed by right-wing extremism, left-wing extremism and Islamic extremism; concerns about right-wing extremism, left-wing extremism and Islamic extremism in Germany; in each case in relation to the last 12 months: development of politically motivated crime in Germany and Saxony in the areas of right-wing extremism, left-wing extremism and Islamic extremism; assessment of the development of politically motivated crime in Germany and in Saxony in the aforementioned areas.

3. Experience of discrimination and crime (based on the last 12 months): Victims of discrimination based on various characteristics (religion, sexual orientation, gender or gender identity, disability or impairment, skin color, origin, age, political attitudes, social status, other characteristic, other characteristic open mention); victim experience in Saxony (attacked by a person without a weapon, attacked by a person with a weapon or several persons with or without a weapon, sexually harassed, sexually abused or raped, unwanted messages with sexual content, insulted on social media, internet offense, pain or physical harm on the internet or social media/ outside the internet or social media, robbery, pickpocketing without violence, theft without violence, burglary, stalking, damage to property, fraud outside the Internet, other,...

List of Individuals Wanted for Arrest on German BKA Website

The two zip-files include Stata do files (version 15) in "Diegmann_files" and data set that can be made available to the public. The data used in this paper comes from different sources. The main variables of interest – crime and internet information – can be made available to the public. However, I use as control variables in the empirical specification data from the Social Security Records that cover wage and employment data of employees in Germany. These data are governed by strict confidentially rules and therefore cannot be made available to the public. Researchers wishing to work with the data of this source may contact the Research Data Centre (FDZ) of the Federal Employment (http//fdz.iab.de/en.aspx).

In Germany, the most frequently used legal section to order forensic mental health treatment is § 63 of the Penal Code (Strafgesetzbuch; StGB). This disposition is primarily aimed at individuals with major mental illnesses who are not fully responsible for a criminal act they committed. Despite evaluation and follow-up studies being conducted within individual hospitals or federal states we lack key epidemiological data on this patient group across the whole country. The present study aims to fill this gap by conducting an annual survey of all eligible forensic mental health hospitals to develop a database of basic clinical, legal and demographic data. Staff at participating hospitals will complete an online survey answering questions about individual patients using routinely collected hospital records. Over the duration of the study, eight-and-a-half years, we aim to collect data on approximately N = 6,450 patients. Alongside important clinical data, we will use official reconviction data at 3- and 6-year follow-ups to investigate the number and types of crimes committed by discharged patients. We aim to extend the scientific literature on factors associated with reconviction in the Risk-Needs-Responsivity model by also measuring the extent to which treatment engagement and programme completion during care predicts reconviction. This study protocol describes the background and theoretical framework for this study, its methods of data collection and analysis, and steps taken to ensure compliance with ethical and data protection principles.

LegISTyr is a machine translation test set for evaluating the quality of legal terminology translation from Italian to South Tyrolean German, a minor standard variety of German. It covers specific legal subdomains or legal translation issues: 1) standardised terminology, 2) occupational health and safety, 3) subsidised housing, 4) family law, 5) criminal and criminal procedure law, 6) homonyms, 7) abbreviated forms, 8) gender-inclusive writing strategies. Each subset contains at least 250 examples, i.e. five examples for each term or twenty examples for each inclusive writing strategy. The total number of examples is 2067. The example sentences in the test set showcase single-word and multi-word terms from the Italian legal system, together with their correct, standardised or non-standardised South Tyrolean German target hypothesis. It also lists other (less) acceptable variants used in South Tyrol and, where available, equivalent terms from other German-speaking legal systems (mainly Austria, Germany, Switzerland). The legal subdomain is specified for each example in every subset, except for the last subset on gender-inclusive writing. This subset contains examples for different strategies used in Italian but no target hypotheses, as there may be several acceptable ones. LegISTyr can be used, for example, to assess the success of terminology enforcement strategies when machine translating legal and administrative texts from Italian into German as well as the influence of major varieties of legal German on translations into a minor standard variety.

The data for this study were sourced from the crowd-sourcing project DrugRoutes, which we launched online on January 1, 2020. DrugRoutes was an online platform that gathered transaction data directly from individuals who had bought or sold drugs on cryptomarkets. The website, accessible via the clear web or the darknet, allowed users to anonymously share information regarding their latest cryptomarket transactions. The data gathered included the specific type of illicit drug involved, the quantity traded, the transaction amount, the transaction date, the countries of origin and destination, and confirmation of parcel receipt. To encourage participation, DrugRoutes openly shared the collected data, enabling cryptomarket users to identify the most popular routes. Consistent with previous studies, our methodology aimed to create a safe space for cryptomarket participants to contribute information for research purposes.Every submission to the project underwent moderation by the authors to filter out potential spam. Submissions deemed too deviant from the prevalent cryptomarket prices per unit at the time were labeled as spam and excluded from the dataset. The research team cross-referenced the price per unit from multiple listings on several cryptomarkets and calculated an average. A transaction price from the same origin country that deviated more than one standard deviation from the mean was regarded as spam and removed from the dataset. We also removed multiple submissions made within seconds of each other as potential spam. While DrugRoutes was one of the few crowd-sourcing initiatives collecting information on illicit drug transactions, it stands out as the only one incorporating successful delivery of illicit drugs. The research team advertised the crowd-sourcing platform on approximately 140 darkweb platforms, and the consent form and contact information were readily available on the website.In total, we collected 1,364 submissions between 2020 and 2022, all of which were confirmed to be authentic and genuine. As this paper is exclusively concerned with international transactions, the subsequent analyses will omit data that pertain strictly to domestic trade.This study views drug trafficking on cryptomarkets as a network of relationships between countries. This perspective aligns with previous literature analyzing drug trafficking across nations, and recent studies investigating the geographic structure of drug trafficking on cryptomarkets.We utilize data from DrugRoutes to identify relationships between countries. DrugRoutes solicited information from cryptomarket participants about their home country and the country with which they most recently transacted. Consequently, we establish a link from Germany to Spain if a participant based in Germany reports purchasing drugs from a dealer in Spain, or if a Spanish drug dealer declares having shipped drugs to Germany. Using this method, we identified a total of 731 different transactions involving 372 dyads across 42 pairs of countries.The network of drugs trafficked via cryptomarkets is characterized by two distinctive features. First, we only consider a connection if at least two submissions are reported for a pair of countries. For example, we dismissed the connection between Albania and Ireland since we have only one observation following this route. These connections are more likely to be random or sporadic links between countries and, therefore, are not included in our analysis. The final network is predicated on a total of 100 exchanges between any two countries.Secondly, we do not differentiate between substances. For example, a connection between Spain and Germany for cannabis is regarded in the same way as a connection between France and Germany for cocaine. Given that we have only a few transactions for most substances, creating individual networks for each illicit drug type would result in very small networks. As a result, we opted to group all drug types together to avoid information loss. More crucially, we anticipate the independent variables to exert a similar effect on cryptomarket transactions, irrespective of the drug type. This approach also enables us to compare our findings to previous studies that do not differentiate between substances .