Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

The Global Living Arrangements Database (GLAD), is a global resource designed to fill a critical gap in the availability of statistical information for examining patterns and changes in living arrangements by age, sex, marital status and educational attainment. Utilizing comprehensive census microdata from IPUMS International and the European Labour Force Survey (EU-LFS), GLAD summarizes over 740 million individual records across 107 countries, covering the period from 1960 to 2021. This database has been constructed using an innovative algorithm that reconstructs kinship relationships among all household members, providing a robust and scalable methodology for studying living arrangements. GLAD is expected to be a valuable resource for both researchers and policymakers, supporting evidence-based decision-making in areas such as housing, social services, and healthcare, as well as offering insights into long-term transformations in family structures. The open-source R code used in this project is publicly available, promoting transparency and enabling the creation of new ego-centred typologies based in interfamily relationships

The repository is composed of the following elements: a Rda file named CORESIDENCE_GLAD_2025.Rda in the form of a List. In R, a List object is a versatile data structure that can contain a collection of different data types, including vectors, matrices, data frames, other lists, spatial objects or even functions. It allows to store and organize heterogeneous data elements within a single object. The CORESIDENCE_GLAD_2025 R-list object is composed of six elements:

1. SINGLE AGES: a data frame where data is aggregated by single ages, marital status, educational attainment and living arrangement types. Source of the original data: IPUMS-I
2. AGE GROUPS IPUMS: a data frame where data is aggregated by five-year age groups, marital status, educational attainment and living arrangement types. Source of the original data: IPUMS-I.
3. AGE GROUPS LFS: a data frame where data is aggregated by five-year age groups, marital status, educational attainment and living arrangement types. Source of the original data: EU-LFS.
4. HARMONIZED: a data frame where data is aggregated by five-year age groups, marital status, educational attainment and living arrangement types. The categories of marital status and educational attainment have been harmonized between the two data sources. Source of the original data: IPUMS-I and EU-LFS
5. CODEBOOK: a data frame with the complete list of variables included, their names description and categories.
6. LABELS LAT: A R function to add the qualitative labels to Living Arrangement Types (LAT).
7. ATLAS LIVING ARRANGEMENTS: The url of the folder with leaflet of living arrangements for each sample included in GLAD.

This dataset provides key economic indicators for five of the world's largest economies, based on their nominal Gross Domestic Product (GDP) in 2022. It includes the GDP values, population, GDP growth rates, per capita GDP, and each country's share of the global economy.

Columns: Country: Name of the country. GDP (nominal, 2022): The total nominal GDP in 2022, represented in USD. GDP (abbrev.): The abbreviated GDP in trillions of USD. GDP growth: The percentage growth in GDP compared to the previous year. Population: Total population of each country in 2022. GDP per capita: The GDP per capita, representing average economic output per person in USD. Share of world GDP: The percentage of global GDP contributed by each country. Key Highlights: The dataset includes some of the largest global economies, such as the United States, China, Japan, Germany, and India. The data can be used to analyze the economic standing of countries in terms of overall GDP and per capita wealth. It offers insights into the relative growth rates and population sizes of these leading economies. This dataset is ideal for exploring economic trends, performing country-wise comparisons, or studying the relationship between population size and GDP growth.

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system; its properties are purposefully represented in reverse pairs; and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.

Dataset Details

The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:

• Subject matter triples file
  • fb+/-CVT+/-REV One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.
    • Example of a row in train.txt, valid.txt, and test.txt:
      • 2, 192, 0
    • Example of a row in entity2id.txt:
      • /g/112yfy2xr, 2
    • Example of a row in relation2id.txt:
      • /music/album/release_type, 192
    • Explaination
      • "/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.
• Type system file
  • freebase_endtypes: Each row maps an edge type to its required subject type and object type.
    • Example
      • 92, 47178872, 90
    • Explanation
      • "92" and "90" are the type id of the subject and object which has the relationship id "47178872".
• Metadata files
  • object_types: Each row maps the MID of a Freebase object to a type it belongs to.
    • Example
      • /g/11b41c22g, /type/object/type, /people/person
    • Explanation
      • The entity with MID "/g/11b41c22g" has a type "/people/person"
  • object_names: Each row maps the MID of a Freebase object to its textual label.
    • Example
      • /g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en
    • Explanation
      • The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.
  • object_ids: Each row maps the MID of a Freebase object to its user-friendly identifier.
    • Example
      • /m/05v3y9r, /type/object/id, "/music/live_album/concert"
    • Explanation
      • The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.
  • domains_id_label: Each row maps the MID of a Freebase domain to its label.
    • Example
      • /m/05v4pmy, geology, 77
    • Explanation
      • The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.
  • types_id_label: Each row maps the MID of a Freebase type to its label.
    • Example
      • /m/01xljxh, /government/political_party, 147
    • Explanation
      • The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.
  • entities_id_label: Each row maps the MID of a Freebase entity to its label.
    • Example
      • /g/11b78qtr5m, Viroliano Tries Jazz, 2234
    • Explanation
      • The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.
    • properties_id_label: Each row maps the MID of a Freebase property to its label.
      • Example
        • /m/010h8tp2, /comedy/comedy_group/members, 47178867
      • Explanation
        • The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.
    • uri_original2simplified and uri_simplified2original: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.
      • Example
        • uri_original2simplified
          • "http://rdf.freebase.com/ns/type.property.unique": "/type/property/unique"
        • uri_simplified2original
          • "/type/property/unique": "http://rdf.freebase.com/ns/type.property.unique"
      • Explanation
        • The URI "http://rdf.freebase.com/ns/type.property.unique" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.
        • The identifier "/type/property/unique" in our dataset has URI http://rdf.freebase.com/ns/type.property.unique in the original Freebase RDF dataset.

This dataset provides an extensive view of global population statistics and health metrics across various countries from 2014 to 2024. It combines population data with vital health-related indicators, making it a valuable resource for understanding trends in population growth and health outcomes worldwide. Researchers, data scientists, and policymakers can utilize this dataset to analyze correlations between population dynamics and health performance at a global scale.

Key Features: - Country: Name of the country. - Year: Year of the data (2014–2024). - Population: Total population for the respective year and country. - Country Code: ISO 3-letter country codes for easy identification. - Health Expenditure (health_exp): Percentage of GDP spent on healthcare. - Life Expectancy (life_expect): Average life expectancy at birth in years. - Maternal Mortality (maternal_mortality): Maternal deaths per 100,000 live births. - Infant Mortality (infant_mortality): Deaths of infants under 1 year per 1,000 live births. - Neonatal Mortality (neonatal_mortality): Deaths of newborns (0–28 days) per 1,000 live births. - Under-5 Mortality (under_5_mortality): Deaths of children under 5 years per 1,000 live births. - HIV Prevalence (prev_hiv): Percentage of the population living with HIV. - Tuberculosis Incidence (inci_tuberc): Estimated new and relapse TB cases per 100,000 people. - Undernourishment Prevalence (prev_undernourishment): Percentage of the population that is undernourished.

Use Cases: - Health Policy Analysis: Understand trends in healthcare expenditure and its relationship to health outcomes. - Global Health Research: Investigate global or regional disparities in health and nutrition. - Population Studies: Analyze population growth trends alongside health indicators. - Data Visualization: Build visual dashboards for storytelling and impactful data representation.

NeSy4VRD

NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the computer vision field of AI and, within that field, to the application tasks of visual relationship detection (VRD) and scene graph generation.

Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind). This is the sense in which we describe NeSy4VRD as being multipurpose: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives.

The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.

NeSy4VRD on Zenodo: the NeSy4VRD dataset package

This entry on Zenodo hosts the NeSy4VRD dataset package, which includes the NeSy4VRD dataset and its companion NeSy4VRD ontology, an OWL ontology called VRD-World.

The NeSy4VRD dataset consists of an image dataset with associated visual relationship annotations. The images of the NeSy4VRD dataset are the same as those that were once publicly available as part of the VRD dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations. The NeSy4VRD dataset is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects. A visual relationship for an image of the NeSy4VRD dataset has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them. Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes. For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>.

Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation. Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing.

The NeSy4VRD ontology, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the NeSy4VRD dataset. It directly describes the domain of the NeSy4VRD dataset, as reflected in the NeSy4VRD visual relationship annotations. More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components. There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning.

Use of the NeSy4VRD ontology, VRD-World, in conjunction with the NeSy4VRD dataset is, of course, purely optional, however. Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the NeSy4VRD ontology and use the NeSy4VRD dataset by itself.

All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.

NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code

The NeSy4VRD research resource incorporates additional components that are companions to the NeSy4VRD dataset package here on Zenodo. These companion components are available at NeSy4VRD on GitHub. These companion components consist of:

• comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the NeSy4VRD ontology, VRD-World, as well)
• open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the NeSy4VRD ontology, VRD-World, and RDF knowledge graphs.

The NeSy4VRD infrastructure supporting extensibility consists of:

• open source Python code for conducting deep and comprehensive analyses of the NeSy4VRD dataset (the VRD images and their associated NeSy4VRD visual relationship annotations)
• an open source, custom-designed NeSy4VRD protocol for specifying visual relationship annotation customisation instructions declaratively, in text files
• an open source, custom-designed NeSy4VRD workflow, implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.

The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the NeSy4VRD dataset in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests. The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned.

The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the NeSy4VRD ontology, VRD-World, in conjunction with the NeSy4VRD dataset. These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer. If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained.

To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between thing objects (that have well-defined shapes) and stuff objects (that are amorphous). Suppose a researcher wishes to have a greater number of stuff object classes with which to work. Water is such a stuff object. Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a Water class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following:

• use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);
• use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;
• use the NeSy4VRD protocol to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);
• use the NeSy4VRD workflow to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;
• introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);
• continue experimenting, now with the added benefit of the additional stuff object class 'water';
• contribute the enriched set of NeSy4VRD visual relationship

Global Sanction Screening and Access Options

Our Global Sanction Lists Database is a powerful tool designed for quick and easy global sanction screening and verification of both individuals and organizations listed on international sanction lists. This service emphasizes the fight against money laundering and terrorism financing (AML-CFT), ensuring your business stays in line with global regulations. We keep our database up to date every day, processed into a professional and secure system, giving you access to the most current information.

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EU-Level PEP Screening and Access Options

Our service provides exclusive access to a database for EU-level PEP screening of Politically Exposed Persons at the European Union level. It empowers obligated entities to efficiently identify individuals with significant public roles within EU institutions and bodies. This database provides insights into persons currently in or those who have held significant public positions in Brussels and other EU institutions in the last 12 months. It spans not only individuals in key positions but also their relatives, broadening the scope for risk assessment. With daily updates from diverse public sources and careful manual processing, our database aids organizations in effectively navigating compliance and mitigating PEP-related risks.

PEP Group 1: Significant Public Functions

Includes individuals currently in or who have in the last 12 months held function of significant public role as defined by the Directive of the European Parliament and of the Council EU 2015/849 and further detailed in the Commission Decision C/2002/3105. Profiles generally include the exact date of birth and usually the domicile. In cases where the full date of birth is not available, the indication "Partially Identified PEP" is displayed. Individuals with enduring risks are recorded for up to five years after ending their function, especially for positions of pan-European significance or extended duration.

Specific Positions within PEP Group 1: Executive Authority Leaders Legislative Members Judges Members of the European Central Bank Bodies Members of the Court of Auditors Ambassadors and Chargés d’Affaires

PEP Groups 2 - 4 and 7: Family and Close Associates

Includes spouses/partners, children (including sons-in-law and daughters-in-law), and parents of individuals in Group PEP1, as well as individuals in a familial or similar relationship.

Specification PEP2: Spouse/partner PEP3: Child/son-in-law/daughter-in-law PEP4: Parent PEP7: Individuals in a long-term familial or similar relationship

This study was conducted as a part of MS's PhD project investigating the experience of romantic relationships and sexuality education in neurodivergent and neurotypical young people from the perspectives of young people, educational professionals, and caregivers. This study aimed to contribute to the existing limited knowledge on sexuality education and experiences of romantic relationships in neurodivergent young people (autistic, with ADHD, and with ASD co-occurring with ADHD [to the best of the researcher’s knowledge, no research has yet to explore the topic in the group with a dual diagnosis]). The outcomes of this study contribute to a greater understanding of what type of sexuality education young people (neurodivergent and neurotypical) receive in their schools/colleges and from their parents, as well as what suggestions they offer for the improvement of sexuality education (to make it more beneficial for young people). This knowledge may help contribute to the design of adequate and inclusive sexuality and romantic relationship education for all young people including the neurodivergent groups, which would be in line with young people's voices on the topic. Appropriate sexuality education for young people may subsequently positively influence their skills in navigating romantic relationships. This may be especially beneficial for the neurodivergent groups of young people since as this research highlighted, they tend to experience greater challenges navigating the complicated world of romance and intimacy than their neurotypical peers. The outcome of this research may also help promote a greater general quality of life in the neurodivergent groups of young people. The key findings of this study: Six themes were developed from the participants’ narratives: Societal ideology about sexuality; Substandard school-based sexuality education; The role of adults in sexuality education; Pornography, as a very powerful alternative means of sexuality education; Young people and romance - a complicated world to navigate; Experience of abuse in the young neurodivergent population is a serious matter. The findings revealed that many young people (neurodivergent and neurotypical) received basic sex education in their schools/colleges and homes and encountered challenges navigating romantic relationships. Neurodivergent young people reported experiencing greater challenges related to their understanding of and building romantic relationships than their neurotypical peers.Abstract Purpose – The literature indicates that sexuality education provided in schools/colleges in the United Kingdom (UK) may not be appropriate for people with Autism Spectrum Disorder (ASD). There appears to be a lack of understanding of the subject regarding young people with Attention-Deficit/Hyperactivity Disorder (ADHD) and a dual diagnosis (ASD co-occurring with ADHD). Research also suggests that compared to neurotypical (NT) peers, young people with ASD tend to receive less support on sexuality from their parents, who often feel that they lack the appropriate skills to help their children with some sex-related issues. Some young people with ASD and ADHD also report lacking an understanding of the social nuances of dating and intimacy, which is crucial for navigating romantic relationships. Design/methodology/approach – This study explored sexuality education and romantic relationships in young people based on a semi-structured interview approach to the topic. Thematic Analysis (TA) was employed to analyze the data. Findings –Six themes were developed from the participants’ narratives: Societal ideology about sexuality; Substandard school-based sexuality education; The role of adults in sexuality education; Pornography, as a very powerful alternative means of sexuality education; Young people and romance - a complicated world to navigate; Experience of abuse in the young neurodivergent population is a serious matter. Findings revealed that many young people (ND and NT) received basic sex education in their schools/colleges and homes and encountered challenges navigating romantic relationships. ND young people reported experiencing greater challenges related to their understanding of and building romantic relationships than their NT peers. Originality/value – To the researchers’ knowledge, this is the first exploration of romantic relationships and sexuality education in NT young people as well as three groups of ND young people (with ASD, ADHD, and ASD co-occurring with ADHD). Keywords Autism spectrum disorder, Autistic, Attention-deficit/hyperactivity disorder, ASD co-occurring with ADHD, Romantic relationships, Sexuality education, Young People, The United Kingdom.

Introduction

The Turkish General Domain Chat Dataset is a high-quality, text-based dataset designed to train and evaluate conversational AI, NLP models, and smart assistants in real-world Turkish usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level Turkish conversations covering a broad spectrum of everyday topics.

Conversational Text Data

This dataset includes over 15000 chat transcripts, each featuring free-flowing dialogue between two native Turkish speakers. The conversations are spontaneous, context-rich, and mimic informal, real-life texting behavior.

Diversity and Domain Coverage

Conversations span a wide variety of general-domain topics to ensure comprehensive model exposure:

This diversity ensures the dataset is useful across multiple NLP and language understanding applications.

Linguistic Authenticity

Chats reflect informal, native-level Turkish usage with:

Metadata

Every chat instance is accompanied by structured metadata, which includes:

This metadata supports model filtering, demographic-specific evaluation, and more controlled fine-tuning workflows.

Data Quality Assurance

All chat records pass through a rigorous QA process to maintain consistency and accuracy:

This ensures a clean, reliable dataset ready for high-performance AI model training.

Applications

This dataset is ideal for training and evaluating a wide range of text-based AI systems:

THIS DATASET WAS LAST UPDATED AT 2:11 AM EASTERN ON AUG. 11

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

EN: Mangrove forests are located at the in-between of the sea and the land. People around the world have made sense of mangroves in many different ways, ranging from mangroves as "home of ghosts", "resources", "biodiversity reserve", "coastal protection" to "death bringers". The aim of the research project is to understand how people in Southern Ecuador have made sense of their mangrove surrounding at the Gulf of Guayaquil over the last 200 years, and how this guides the use of mangrove areas around the Gulf of Guayaquil. The research draws on Communicative and Discursive Constructivism and the Sociology of Knowledge Approach to Discourse. The research data is composed of ethnographic and historical data, collected online, in archives and in an eight months' field research from 06/2019 until 02/2020 in Southern Ecuador. Besides unfolding empirically how meaning is attached to mangroves around the Gulf of Guayaquil and how discourses have shaped these meanings, the results aim to contribute to the ongoing discussions about the methodological framework used.ES: Los manglares se encuentran en el intermedio del mar y de la tierra. Personas en todo el mundo han entendido los manglares de muchas maneras distintas, desde "casa de espíritus", "recursos", "reserva de biodiversidad", "protección costal" hasta "traedor de muerte". El objetivo de este proyecto de investigación es entender cómo la gente en el Ecuador del sur entiende a los manglares de alrededor del Golfo de Guayaquil a lo largo de los últimos 200 años, y cómo esto guia el uso de los manglares alrededor del Golfo de Guayaquil. Esta investigación se refiere al constructivismo comunicativo y al análisis de discurso de la sociología del conocimiento. Los datos de la investigación están compuestos de datos etnográficos e históricos, recabados online, en archivos y durante un trabajo de campo desde 06/2019 hasta 02/2020 en el Ecuador del sur. Además de revelar empíricamente cómo se asigna un significado a los manglares alrededor del Golfo de Guayaquil y cómo los discursos han formado estos significados, los resultados quieren contribuir a las discusiones actuales acerca del esquema metodológico usado aquí. Transcription method: Standard orthographyStudy-Materials note: Context materials include photos, sound recordingEN: Research data were created in cooperation with the following project partners: Escuela Superior Politécnica del Litoral (ESPOL): Indira Nolivos; Junta de Manejo Participativo Comunitario Manglares Don Goyo (JUMAPACOM): Leonardo Lindao, Genaro Vera and Orlando Leyton; Fundación Cerro Verde: Federico Koelle D. and Wendy Chavez; Schutzwald e. V.: Daniel Schönig and Stefan Dietrich

Abstract copyright UK Data Service and data collection copyright owner.The 1970 British Cohort Study (BCS70) is a longitudinal birth cohort study, following a nationally representative sample of over 17,000 people born in England, Scotland and Wales in a single week of 1970. Cohort members have been sureveyed throughout their childhood and adult lives, mapping their individual trajectories and creating a unique resource for researchers. It is one of very few longitudinal studies following people of this generation anywhere in the world.Since 1970, cohort members have been surveyed at ages 5, 10, 16, 26, 30, 34, 38, 42 and 46. Featuring a range of objective measures and rich self-reported data, BCS70 covers an incredible amount of ground and can be used in research on many topics Evidence from BCS70 has illuminated important issues for our society across five decades. Key findings include how reading for pleasure matters for children's cognitive development, why grammar schools have not reduced social inequalities, and how childhood experiences can impact on mental health in mid-life. Every day researchers from across the scientific community are using this important study to make new connections and discoveries.BCS70 is run by the Centre for Longitudinal Studies (CLS), a research centre in the UCL Institute of Education, which is part of University College London. The content of BCS70 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from BCS70 that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Secure Access datasetsSecure Access versions of BCS70 have more restrictive access conditions than versions available under the standard End User Licence (EUL). 1970 British Cohort Study: Partnership Histories, 1986-2016: Data on live-in relationships lasting one month or more have been collected in all BCS70 sweeps from sweep 6 (age 30) as well as data on current live-in relationship at sweep 5 (age 23). The purpose of the Partnership Histories dataset is to merge all data on live-in relationships in successive sweeps into one longitudinal dataset. The focus of the questions asked at each sweep are about the relationship start date; whether married/became civil partner (sweep 8 and later) to this partner and if so the marriage/civil partnership dates; whether still together with this partner and if not the date that the relationship ended; how the relationship ended; if relevant whether divorced and divorce dates; the sex, marital status and age at start of relationship of the partner. For the fourth edition (March 2021), both data files have been updated to include partnership data from the latest BCS70 data sweep (2016). Following Sweep 10 (2016, age 46), longitudinal datasets have been streamlined by removing cases which have never participated in any main sweep survey and are no longer being issued.

Mental Health Services Children & Young People

Monthly Statistics on Referrals, Contacts and Care

By data.world's Admin [source]

About this dataset

This dataset provides essential information on the mental health services provided to children and young people in England. The data contained within the Mental Health Services Data Set (MHSDS) - Children & Young People covers a variety of different categories during a given reporting period, including primary level details, secondary level descriptions, number of open referrals for children's and young people's mental health services at the end of the reporting period, as well as number of first attended contacts for referrals open in the reporting period aged 0-18. It also provides insight into how many people are in contact with mental health services aged 0 to 18 at the time of reporting, how many referrals starting during this time were self-refreshers and more. This dataset includes valuable information that is necessary to better track and understand trends in order to provide more effective care

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How to use the dataset

This guide will provide you with an overview of the data contained in this dataset as well as information on how to effectively use it for your own research or personal purposes. Let's get started!

Overview of Data Fields

• REPORTING_PERIOD: The month and year of the reporting period (Date)
• BREAKDOWN: The type of breakdown of the data (String)
• PRIMARY_LEVEL: The primary level of the data (String)
• PRIMARY_LEVEL_DESCRIPTION: A description at the primary level of the data (String)
• SECONDARY_LEVEL: The secondary level of the data (String)

Research Ideas

• Evaluating the efficacy of existing mental health services for children and young people by examining changes in relationships between different aspects of service delivery (e.g. referral activity, hospital spell activity, etc).
• Analysing geographical trends in mental health services to inform investment decisions and policies across different regions.
• Identifying areas of high need among vulnerable or marginalised citizens, such as those aged 0-18 or those with particular genetic makeup, to better target resources and support those most in need of help

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: mhsds-monthly-cyp-data-file-feb-fin-2017-1.csv | Column name | Description | |:-------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | REPORTING_PERIOD | The period of time for which the data was collected. (String) | | BREAKDOWN | The breakdown of the data by age group. (String) | | PRIMARY_LEVEL | The primary level of the data. (String) | | PRIMARY_LEVEL_DESCRIPTION ...

Global Data: GDP, Life Expectancy & More

This dataset comprises 204 entries and 38 attributes, providing a comprehensive analysis of key economic and social indicators across various countries. It includes a diverse range of metrics, allowing for in-depth exploration of global trends related to GDP, education, health, and environmental factors.

Key Features:

• GDP: Gross Domestic Product (in current US dollars), representing the total economic output of a country.
• Sex Ratio: The ratio of males to females in the population, highlighting demographic trends.
• Life Expectancy: Average lifespan for males and females, an essential indicator of healthcare quality.
• Education Enrollment Rates: Data on primary, secondary, and post-secondary education enrollment for males and females, reflecting educational attainment.
• Unemployment Rate: Percentage of the labor force that is unemployed, indicating economic health.
• Homicide Rate: Number of homicides per 100,000 population, providing insight into safety and crime levels.
• Urban Population Growth: Rate of growth in urban populations, illustrating migration trends.
• CO2 Emissions: Carbon dioxide emissions per capita, an important measure of environmental impact.
• Forested Area: Percentage of land covered by forests, indicating biodiversity and environmental health.
• Tourist Numbers: Total number of international visitors, which can reflect a country's tourism potential.

Applications and Uses:

1. Research and Analysis: Ideal for researchers studying the correlation between economic performance and social indicators. This dataset can help identify trends and patterns relevant to global development.

2. Policy Development: Policymakers can utilize this data to inform decisions on education, healthcare, and environmental policies, aiming to improve national outcomes.

3. Machine Learning and Data Science: Data scientists can apply machine learning techniques to predict economic trends, analyze social impacts, or classify countries based on various indicators.

4. Educational Purposes: Suitable for students and educators in fields like economics, sociology, and environmental science for practical data analysis exercises.

5. Visualization Projects: Perfect for creating compelling visualizations that illustrate relationships between different metrics, aiding in public understanding and engagement.

By leveraging this dataset, users can uncover insights into how different factors influence a country's development, making it a valuable resource for diverse applications across various fields.

Spotify Million Playlist Dataset Challenge Summary The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights). Background Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes. Our users love playlists too. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits. But our users don’t love just listening to playlists, they also love creating them. To date, over 4 billion playlists have been created and shared by Spotify users. People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special. The other thing we love here at Spotify is playlist research. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. Why do certain songs go together? What is the difference between “Beach Vibes” and “Forest Vibes”? And what words do people use to describe which playlists? By learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. This can make playlist creation easier, and ultimately help people find more of the music they love. Dataset To enable this type of research at scale, in 2018 we sponsored the RecSys Challenge 2018, which introduced the Million Playlist Dataset (MPD) to the research community. Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. The dataset includes public playlists created by US Spotify users between January 2010 and November 2017. The challenge ran from January to July 2018, and received 1,467 submissions from 410 teams. A summary of the challenge and the top scoring submissions was published in the ACM Transactions on Intelligent Systems and Technology. In September 2020, we re-released the dataset as an open-ended challenge on AIcrowd.com. The dataset can now be downloaded by registered participants from the Resources page. Each playlist in the MPD contains a playlist title, the track list (including track IDs and metadata), and other metadata fields (last edit time, number of playlist edits, and more). All data is anonymized to protect user privacy. Playlists are sampled with some randomization, are manually filtered for playlist quality and to remove offensive content, and have some dithering and fictitious tracks added to them. As such, the dataset is not representative of the true distribution of playlists on the Spotify platform, and must not be interpreted as such in any research or analysis performed on the dataset. Dataset Contains 1000 examples of each scenario: Title only (no tracks) Title and first track Title and first 5 tracks First 5 tracks only Title and first 10 tracks First 10 tracks only Title and first 25 tracks Title and 25 random tracks Title and first 100 tracks Title and 100 random tracks Download Link Full Details: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge Download Link: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files {"references": ["C.W. Chen, P. Lamere, M. Schedl, and H. Zamani. Recsys Challenge 2018: Automatic Music Playlist Continuation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18), 2018."]}

World Youth Day experiences (personal or from family members). Perception and evaluation of World Youth Day coverage. Impact and sustainability of World Youth Day. Attitudes towards religion and the Church. Topics: 1. World Youth Day experiences (personal or of family members): Participation in the World Youth Day in Cologne and form of the event; length of stay at the World Youth Day; previous participation in a major religious event (previous World Youth Day, Taizé, pilgrimage, church day, other (open)); family members as participants in the World Youth Day and relationship; length of stay of family members; guests during the World Youth Day or the days of the encounter; country of origin of the guests; contact; personal participation or participation of family members in the days of the encounter; form of participation. 2. Perception and evaluation of the coverage of WYD: Perception of the coverage of WYD in different media; live broadcasts of WYD seen or heard; topics of these live broadcasts; number of hours of live broadcasts watched in total; aspects of the coverage that remained in the memory; evaluation of the media coverage of WYD. 3.Impact and sustainability of WYD: WYD as a motivation to participate in the Church; more conversations about faith and religion due to WYD in the personal environment; expected impact of WYD in the areas of religion, Mass, Church and community; consequences of WYD (open); expected long-term impact of WYD on the personal environment; change in the Pope´s image due to WYD coverage; positive or negative change in the Pope´s image. 4. Attitudes towards religion and church: religious affiliation; attitude towards the institution of church; frequency of church service attendance; personal involvement in a church group; nature of this church group; self-assessment of religiosity; church must change; basic trust through faith; religion and faith as old hat or uninteresting; desire for more influence of faith and religion in society; stronger interest of young people in religion and faith than it appears (religious silence spiral). Demography: sex; age (year of birth); religious denomination; highest school-leaving qualification; marital status; own children; number of children; occupational status. Additionally coded: ID; city size. Weltjugendtagerfahrungen (persönlich oder von Familienmitgliedern). Wahrnehmung und Bewertung der Berichterstattung über den Weltjugendtag. Auswirkungen und Nachhaltigkeit des Weltjugendtags. Einstellungen zu Religion und Kirche. Themen: 1. Weltjugendtagerfahrungen (persönlich oder von Familienmitgliedern): Teilnahme am Weltjugendtag in Köln und Form der Veranstaltung; Aufenthaltsdauer auf dem Weltjugendtag; frühere Teilnahme an einer religiösen Großveranstaltung (früherer Weltjugendtag, Taizé, Wallfahrt, Kirchentag, sonstiges (offen)); Familienmitglieder als Teilnehmer am Weltjugendtag und Verwandtschaftsverhältnis; Aufenthaltsdauer der Familienmitglieder; Gäste während des Weltjugendtages oder den Tagen der Begegnung; Herkunftsland der Gäste; Kontakt; persönliche Teilnahme bzw. Teilnahme von Familienmitgliedern an den Tagen der Begegnung; Form der Teilnahme. 2. Wahrnehmung und Bewertung der Berichterstattung über den Weltjugendtag: Wahrnehmung der Berichterstattung über den Weltjugendtag in verschiedenen Medien; Live-Übertragungen vom Weltjugendtag gesehen bzw. gehört; Themen dieser Live-Übertragungen; Stundenzahl der verfolgten Live-Übertragungen insgesamt; Aspekte der Berichterstattung, die im Gedächtnis geblieben sind; Bewertung der Medienberichterstattung zum Weltjugendtag. 3. Auswirkungen und Nachhaltigkeit des Weltjugendtags: Weltjugendtag als Motivation zur Mitarbeit in der Kirche; mehr Gespräche über Glauben und Religion aufgrund des Weltjugendtages im persönlichen Umfeld; erwartete Auswirkungen des Weltjugendtages in den Bereichen Religion, Messe, Kirche und Gemeinde; Folgen des Weltjugendtages (offen); erwarteter langfristiger Einfluss des Weltjugendtages auf das persönliche Umfeld; Veränderung des Papst-Images durch die Berichterstattung über den Weltjugendtag; positive oder negative Veränderung des Papst-Images. 4. Einstellungen zu Religion und Kirche: Religionszugehörigkeit; Einstellung zur Institution Kirche; Häufigkeit von Gottesdienstbesuchen; persönliches Engagement in einer kirchlichen Gruppe; Art dieser kirchlichen Gruppe; Selbsteinschätzung der Religiosität; Kirche muss sich ändern; Grundvertrauen durch den Glauben; Religion und Glauben als alter Hut bzw. uninteressant; Wunsch nach mehr Einfluss von Glaube und Religion in der Gesellschaft; stärkeres Interesse von Jugendlichen an Religion und Glauben als es den Anschein hat (religiöse Schweigespirale). Demographie: Geschlecht; Alter (Geburtsjahr); Konfession; höchster Schulabschluss; Familienstand; eigene Kinder; Kinderzahl; berufliche Stellung. Zusätzlich verkodet wurde: ID; Ortsgröße.

Experience unprecedented access to the world's corporate data with our Ultimate Beneficiary Owners Database (UBO). This data product is meticulously curated, providing insightful information on over 200 million companies across the globe.

This highly comprehensive database gives you a clear understanding of corporate structures, highlighting the hierarchy from parent companies to subsidiaries and making it easier than ever to identify Ultimate Beneficiary Owners (UBOs) – individuals or entities that have the power to control or profit significantly from a company.

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Verified and Updated: Our data is meticulously verified and regularly updated to maintain accuracy and relevance, minimizing the risk of outdated or erroneous information.

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Data Uses:

This data can be particularly valuable for applications in:

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Get ahead in your industry with the power of data, providing you the insight needed to make informed decisions and ensure compliance. This Ultimate Beneficiary Owners Database (UBO) is your key to unlocking extensive knowledge of global company hierarchies.

Within the past four decades, research has been increasingly drawn toward understanding whether there is a link between the changing human–nature relationship and its impact on people’s health. However, to examine whether there is a link requires research of its breadth and underlying mechanisms from an interdisciplinary approach. This article begins by reviewing the debates concerning the human–nature relationship, which are then critiqued and redefined from an interdisciplinary perspective. The concept and chronological history of “health” is then explored, based on the World Health Organization’s definition. Combining these concepts, the human–nature relationship and its impact on human’s health are then explored through a developing conceptual model. It is argued that using an interdisciplinary perspective can facilitate a deeper understanding of the complexities involved for attaining optimal health at the human–environmental interface. _

Jutta worked in civil service in Stuttgart, specifically in Esslingen, from 1989 to 2018. After taking a break for three years due to the birth of her second son, Jutta was asked by the mayor to create programs for the visit of Jewish people who had previously lived in Esslingen. This experience marked her first involvement with hosting foreign individuals in Esslingen and caring for them. Following that exprience, her role involved leading the office of International relationships, focusing on town twinning and European programs. Working directly for the mayor, she coordinated various exchanges such as school, club, and youth exchanges, as well as collaborative European projects. Concerning the origins of town twinning, young people from different countries, despite being burdened by war-related differences, focused on building peace and unity. War was not a central theme in their discussions; instead, they emphasized the importance of living together harmoniously and the freedom to study and travel across Europe. They aspired to create a free world where people could live in peace and prosperity. There was a lack of education about the Holocaust and the experiences of Jewish people in schools. Many students reported not learning about it in their lessons, mirroring the experiences of Jutta's generation, where teachers avoided discussing it altogether. Even the Jutta's parents, who were teenagers during the war, were aware of the events but chose not to acknowledge them fully. Jutta draws a parallel to contemporary attitudes towards events such as the conflict in Ukraine. Jutta had discussions with town-twinning friends during the reunification of Germany. While she felt positive about the idea of a united Germany, their friends expressed anxiety about it. She struggled to understand their friends' concerns, but one friend mentioned historical apprehensions related to Germany's size and its past actions, particularly during the Second World War. The complexities and differing perspectives about the reunification of Germany were hard to understand for Jutta. She couldn’t understand why they were so anxious. Friends in a Cold Climate: After the Second World War a number of friendship ties were established between towns in Europe. Citizens, council-officials and church representatives were looking for peace and prosperity in a still fragmented Europe. After a visit of the Royal Mens Choir Schiedam to Esslingen in 1963, representatives of Esslingen asked Schiedam to take part in friendly exchanges involving citizens and officials. The connections expanded and in 1970, in Esslingen, a circle of friends was established tying the towns Esslingen, Schiedam, Udine (IT) Velenje (SL) Vienne (F) and Neath together. Each town of this so called “Verbund der Ringpartnerstädte” had to keep in touch with at least 2 towns within the wider network. Friends in a Cold Climate looks primarily through the eyes the citizen-participant. Their motivation for taking part may vary. For example, is there a certain engagement with the European project? Did parents instil in their children a a message of fraternisation stemming from their experiences in WWII? Or did the participants only see youth exchange only as an opportunity for a trip to a foreign country? This latter motivation of taking part for other than Euro-idealistic reasons should however not be regarded as tourist or consumer-led behaviour. Following Michel de Certeau, Friends in a Cold Climate regards citizen-participants as a producers rather than as a consumers. A participant may "put to use" the Town Twinning facilities of travel and activities in his or her own way, regardless of the activities programme. INTEGRATION OF WESTERN EUROPE AFTER THE SECOND WORLD WAR was driven by a broad movement aimed at peace, security and prosperity. Organised youth exchange between European cities formed an important part of that movement. This research focuses on young people who, from the 1960s onwards, participated in international exchanges organised by twinned towns, also called jumelage. Friends in a Cold Climate asks about the interactions between young people while taking into account the organisational structures on a municipal level, The project investigates the role of the ideology of a united West-Europe, individual desires for travel and freedom, the upcoming discourse about the Second World War and the influence of the prevalent “counterculture” of that period, thus shedding a light on the formative years of European integration.

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...