Denmark recorded 8643 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Denmark reported 3413017 Coronavirus Cases. This dataset includes a chart with historical data for Denmark Coronavirus Deaths.

About this dataset

Context

The dataset tabulates the population of New Denmark town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Denmark town. The dataset can be utilized to understand the population distribution of New Denmark town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Denmark town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for New Denmark town.

Key observations

Largest age group (population): Male # 55-59 years (72) | Female # 35-39 years (99). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the New Denmark town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Denmark town is shown in the following column.
• Population (Female): The female population in the New Denmark town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in New Denmark town for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Denmark town Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Denmark population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Denmark. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 2,036 (64.82% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Denmark population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Denmark is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Denmark is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Denmark Population by Age. You can refer the same here

Unemployment Rate in Denmark remained unchanged at 2.50 percent in April. This dataset provides - Denmark Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This data set contains observations of dead or alive harbor porpoises made by the public, mostly around the Swedish coast. A few observations are from Norwegian, Danish, Finish and German waters. Each observation of harbor porpoise is verified at the Swedish Museum of Natural History before it is approved and published on the web. The verification consists of controlling the accuracy of number of animals sighted, if the coordinates are correct and if pictures are attached that they really show a porpoise and not another species. If any of these three seem unlikely, the reporter is contacted and asked more detailed questions. The report is approved or denied depending on the answers given. Pictures and movies that can’t be uploaded to the database due to size problems are saved at the museum server and marked with the identification number given by the database. By the end of the year the data is submitted to HELCOM who then summarize all the member state’s data from the Baltic proper to the Kattegat basin. The porpoise is one of the smallest tooth whales in the world and the only whale species that breeds in Swedish waters. They are to be found in temperate water in the northern hemisphere where they live in small groups of 1-3 individuals. The females give birth to a calf in the summer months which then suckles for about 10 months before it is left on its own and she has a new calf. The porpoises around Sweden are divided in to three groups that don’t mix very often. The North Sea population is found on the west coast in Skagerrak down to the Falkenberg area. The Belt Sea population is to be found a bit north of Falkenberg down to Blekinge archipelago in the Baltic. The Baltic proper population is the smallest population and consists only of a few hundred animals and is considered as an endangered sub species. They are most commonly found from the Blekinge archipelago up to Åland Sea with a hot spot area south of Gotland at Hoburg’s bank and the Mid-Sea bank. The Porpoise Observation Database was started in 2005 at the request of the Swedish Environmental Protection Agency to get a better understanding of where to find porpoises with the idea to use the public to expand the “survey area”. The first year 26 sightings were reported, where 4 was from the Baltic Sea. The museum is particularly interested in sightings from the Baltic Sea due to the low numbers of animals and lack of data and knowledge about this group. In the beginning only live sightings were reported but later also found dead animals were added. Some of the animals that are reported dead are collected. Depending on where it is found and its state of decay, the animal can be subsampled in the field. A piece of blubber and some teeth are then send in by mail and stored in the Environmental Specimen Bank at the Swedish Museum of Natural History in Stockholm. If the whole animal is collected an autopsy is performed at the National Veterinary Institute in Uppsala to try and determine cause of death. Organs, teeth and parasites are sampled and saved at the Environmental Specimen Bank as well. Information about the animal i.e. location, founding date, sex, age, length, weight, blubber thickness as well as type of organ and the amount that is sampled is then added to the Specimen Bank database. If there is an interest in getting samples or data from the Specimen Bank, one have to send in an application to the Department of Environmental research and monitoring and state the purpose of the study and the amount of samples needed.

Employment Rate in Denmark increased to 69.70 percent in April from 69.20 percent in March of 2025. This dataset provides - Denmark Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Forecast: Number of Maternal Deaths in Denmark 2022 - 2026 Discover more data with ReportLinker!

Economic-demographic panel dataset over the Danish West Indies (current-day US Virgin Islands), 1760-1914. The dataset contains demographic information on the population, and their ownership of property. The dataset has been assembled from primary sources in the Danish National Archive's collection, for more than 50 benchmark years. The dataset contains information about historical individuals, but all individuals are long deceased, so the dataset is in a legal sense not personal data.

Welcome to the Danish Chain of Thought prompt-response dataset, a meticulously curated collection containing 3000 comprehensive prompt and response pairs. This dataset is an invaluable resource for training Language Models (LMs) to generate well-reasoned answers and minimize inaccuracies. Its primary utility lies in enhancing LLMs' reasoning skills for solving arithmetic, common sense, symbolic reasoning, and complex problems. Dataset Content: This COT dataset comprises a diverse set of instructions and questions paired with corresponding answers and rationales in the Danish language. These prompts and completions cover a broad range of topics and questions, including mathematical concepts, common sense reasoning, complex problem-solving, scientific inquiries, puzzles, and more. Each prompt is meticulously accompanied by a response and rationale, providing essential information and insights to enhance the language model training process. These prompts, completions, and rationales were manually curated by native Danish people, drawing references from various sources, including open-source datasets, news articles, websites, and other reliable references. Our chain-of-thought prompt-completion dataset includes various prompt types, such as instructional prompts, continuations, and in-context learning (zero-shot, few-shot) prompts. Additionally, the dataset contains prompts and completions enriched with various forms of rich text, such as lists, tables, code snippets, JSON, and more, with proper markdown format. Prompt Diversity: To ensure a wide-ranging dataset, we have included prompts from a plethora of topics related to mathematics, common sense reasoning, and symbolic reasoning. These topics encompass arithmetic, percentages, ratios, geometry, analogies, spatial reasoning, temporal reasoning, logic puzzles, patterns, and sequences, among others. These prompts vary in complexity, spanning easy, medium, and hard levels. Various question types are included, such as multiple-choice, direct queries, and true/false assessments. Response Formats: To accommodate diverse learning experiences, our dataset incorporates different types of answers depending on the prompt and provides step-by-step rationales. The detailed rationale aids the language model in building reasoning process for complex questions. These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers. Data Format and Annotation Details: This fully labeled Danish Chain of Thought Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt complexity, prompt category, domain, response, rationale, response type, and rich text presence. Quality and Accuracy: Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses and rationales are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance. The Danish version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset. Continuous Updates and Customization: The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom chain of thought prompt completion data tailored to specific needs, providing flexibility and customization options. License: The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Danish Chain of Thought Prompt Completion Dataset to enhance the rationale and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

What’s Included

Welcome to the Danish Closed Ended Classification Prompt-Response Dataset—an extensive collection of 3000 meticulously curated prompt and response pairs. This dataset is a valuable resource for training Language Models (LMs) to classify input text accurately, a crucial aspect in advancing generative AI.

This closed-ended classification dataset comprises a diverse set of prompts and responses where the prompt contains input text to be classified and may also contain task instruction, context, constraints, and restrictions while completion contains the best classification category as response. Both these prompts and completions are available in Danish language. As this is a closed-ended dataset, there will be options given to choose the right classification category as a part of the prompt.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Danish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This closed-ended classification prompt and completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains prompts and responses with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this closed-ended classification dataset includes prompts with varying complexity levels, ranging from easy to medium and hard. Different types of prompts, such as multiple-choice, direct, and true/false, are included. Additionally, prompts are diverse in terms of length from short to medium and long, creating a comprehensive variety. The classification dataset also contains prompts with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate diverse learning experiences, our dataset incorporates different types of responses depending on the prompt. These formats include single-word, short phrase, and single sentence type of response. These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

This fully labeled Danish Closed Ended Classification Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, response type, and rich text presence.

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Danish version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom closed-ended classification prompt and completion data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Danish Closed Ended Classification Prompt-Completion Dataset to enhance the classification abilities and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

Home Ownership Rate in Denmark increased to 60.90 percent in 2024 from 60 percent in 2023. This dataset provides the latest reported value for - Denmark Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The tourism sector GDP share in Denmark was forecast to continuously increase between 2023 and 2028 by in total 1.9 percentage points. The share is estimated to amount to 6.82 percent in 2028. While the share was forecast to increase significant in the next years, the increase will slow down in the future.Depited is the economic contribution of the tourism sector in relation to the gross domestic product of the country or region at hand.The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the tourism sector GDP share in countries like Sweden and Norway.

The international tourism receipts in Denmark were forecast to continuously increase between 2024 and 2029 by in total 5.9 billion U.S. dollars (+8.78 percent). After the sixth consecutive increasing year, the tourism receipts is estimated to reach 73.2 billion U.S. dollars and therefore a new peak in 2029. Receipts denote expenditures by inbound tourists from other countries. Domestic tourism expenditures are not included. The forecast has been adjusted for the expected impact of COVID-19. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the international tourism receipts in countries like Finland and Sweden.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Denmark was 1,005 individuals. Copenhagen was over-sampled in Denmark.

Mode of data collection

Landline and cellular telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Denmark DK: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 0.570 % in 2015. Denmark DK: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 0.570 % from Dec 2015 (Median) to 2015, with 1 observations. Denmark DK: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

What’s Included

The Danish Open-Ended Question Answering Dataset is a meticulously curated collection of comprehensive Question-Answer pairs. It serves as a valuable resource for training Large Language Models (LLMs) and Question-answering models in the Danish language, advancing the field of artificial intelligence.

Each question is accompanied by an answer, providing valuable information and insights to enhance the language model training process. Both the questions and answers were manually curated by native Danish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This question-answer prompt completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains questions and answers with different types of rich text, including tables, code, JSON, etc., with proper markdown.

Both the question and answers in Danish are grammatically accurate without any word or grammatical errors. No copyrighted, toxic, or harmful content is used while building this dataset.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Denmark was 1,002 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Context

This Dataset contains complete information of all types of crimes(90) committed in Denmark's 5 regions from the year 2007 to 2019.

Content

The types of Offences are:

1.) Nature of the offence, total 2.) Penal Code, total 3.) Penal code, unspecified 4.) Sexual offenses, total 5.) Incest, etc. 6.) Rape, etc. 7.) Heterosexual offence against a child under 12 (Repealed in 2013) 8.) Sexual offence against a child under 12 (New from 2013) 9.) Any other kind of heterosexual offence (Repealed in 2013) 10.) Sexual offence against a child under 15 (New from 2013) 11.) Any other kind of sexual offence (New from 2013) 12.) Homosexual offence against a child under 12 (Repealed in 2013) 13.) Any other kind of homosexual offence (Repealed in 2013) 14.) Offence against public decency by groping 15.) Offence against public decency by indecent exposure 16.) Any other kind of offence against public decency 17.) Prostitution, etc. 18.) Crimes of violence, total 19.) Violence against public authority 20.) Unlawful assembly/disturbance of public order 21.) Homicide 22.) Attempted homicide 23.) Coercive control 24.) Common assault 25.) Assault causing actual bodily harm 26.) Particularly aggravated assault 27.) Unprovoked assault 28.) Any other kind of intentional trespass to the person 29.) Intentional bodily harm 30.) Causing death or bodily harm by negligence 31.) Offences against life and limb 32.) Offences against personal liberty 33.) Threats 34.) Offences against property, total 35.) Forgery 36.) Cheque forgery 37.) Arson 38.) Burglary - business and community 39.) Residential burglaries 40.) Burglary (uninhabited buildings) 41.) Theft from conveyances 42.) Shoplifting, etc. 43.) Other kinds of theft 44.) Theft of/taking vehicle without the owners consent (TWOC) 45.) Theft of/taking moped without the owners consent (TWOC) 46.) Theft of/taking bicycle without the owners consent (TWOC) 47.) Theft of/taking other objects without the owners consent (TWOC) 48.) Theft by finding 49.) Embezzlement 50.) Fraud 51.) Fraud by cheque 52.) Fraud by abuse of position 53.) Blackmail and usury 54.) Fraud against creditors 55.) Handling stolen goods 56.) Robbery 57.) Aggravated tax evasion etc. 58.) Malicious damage to property 59.) Receiving stolen goods by negligence 60.) Offence against and infringement of property 61.) Other offences, total 62.) Offences against public authority, etc. 63.) Offences by public servants 64.) Perjury 65.) Any other kind of false statement 66.) Offences concerning money and evidence 67.) Trafficking of drugs, etc. 68.) Smuggling etc. of drugs 69.) General public offences etc. 70.) Illegal trade, etc. 71.) Family relation offences 72.) Involuntary manslaughter etc. in connection with traffic accident 73.) Non-molestation order (Repealed in 2012) 74.) Invasion of privacy and defamation 75.) Special acts, total 76.) Euphoriants Act 77.) The Offensive Weapons Act 78.) Tax legislation and fiscal acts, etc. 79.) Other special acts in criminal law 80.) Health and social security legislation 81.) Building and housing legislation 82.) The environmental protection act 83.) Legislation on animals, hunting, etc. 84.) Legislation on employment, transport, etc. 85.) The Companies Act 86.) Legislation on the national defence 87.) Legislation applying to public utilities 88.) Legislation on gambling, licencing, trade 89.) Any other special legislation 90.) Special legislation, unspecified

Inspiration

Many things can be analyzed through this dataset: 1. Spatial and Temporal occurance of crime in Denmark 2. Reason of Low Crime rate in Denmark(also in Scandinavian Countries) 3. Major hotspots of crime in Denmark

Acknowledgements

Statistics-Denmark (Statbank) has published this data under Open Data Policy

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Landline and Cellular Telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

The Swedish income panel was originally set up in the beginning of the 90s to make studies of how immigrants assimilate in the Swedish labour market possible. It consists of large samples of foreign-born and Swedish-born persons. Income information from registers is added for nearly 40 years. In addition income information relating to spouses is also available as well as for a subset of mothers and fathers. This makes it possible to construct measures of household income based on a relatively narrow definition. However, starting in 1998 there is also more information making it possible to include children over 18 and their incomes in the family. By matching with some different additional registers information has been added for people who have been unemployed or involved in labour market programmes during the 90s, on causes of deaths for people who have deceased since 1978 and on recent arrived immigrants from various origins. It has turned out that the data-base is quite useful for analysing research-questions other than originally motivating construction of the panel. The panel has been used for cross country comparisons of immigrants in the labour market and to analyse income mobility for different breakdowns of the population, and analyses the development in cohort income. There have been analyses of social assistance receipt among immigrants as well as studies of intergeneration mobility of income, the labour market situation of young immigrants and the second generation of immigrants. On-going work includes evaluation of labour market training programmes and studies of early retirement among immigrants. Planned work includes studies of the economic transition from child to adulthood during the 80s and 90s as well as studies of how frequent immigrant children are subject to measures under the Social Service Act and the Care of Youth Persons Act. The potentials of the Swedish Income Panel can be understood if one compares it with better known income-panels in other countries. For example SWIP covers more years and has a larger sample than the German Socio-Economic Panel (GSOEP). On the other hand, the fact that information is obtained from registers only makes this Swedish panel less rich in variables. There are striking parallels between the Gothenburg Income Panel and the labour market panel at the Centre for Labour Market and Social Research in Aarhus for the Danish population.