This dataset tracks annual diversity score from 2003 to 2023 for Denmark Early Childhood Center vs. Wisconsin and Denmark School District

About this dataset

Context

The dataset presents the median household income across different racial categories in Denmark town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Denmark town population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 91.64% of the total residents in Denmark town. Notably, the median household income for White households is $66,587. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $66,587.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Denmark town.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 town median household income by race. You can refer the same here

The share of women on boards in the financial services industry in Denmark increased slightly between 2018 and 2023. In 2018, **** percent of the directors in financial services companies were female. By 2021, the share of women on boards increased to **** percent. As of 2023, Denmark ranked third among European countries in terms of gender diversity on boards of directors, with **** percent of the board seats held by women.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within New Denmark town. The dataset can be utilized to gain insights into gender-based income distribution within the New Denmark town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within New Denmark town, among individuals aged 15 years and older with income, there were 534 men and 513 women in the workforce. Among them, 324 men were engaged in full-time, year-round employment, while 270 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.10% fell within the income range of under $24,999, while 5.93% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 36.11% of men in full-time roles earned incomes exceeding $100,000, while 5.56% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 median household income by race. You can refer the same here

Sulfate-reducing microorganisms (SRM) are key players in the marine carbon and sulfur cycles, especially in coastal sediments. We identified and characterised SRM using dsrB gene sequencing in a marine coastal sediment in Aarhus Bay, Denmark.

This dataset tracks annual diversity score from 2019 to 2023 for Denmark High School vs. Georgia and Forsyth County School District

The Danish Environmental Portal: One gateway to data on nature and the environment in Denmark. This dataset is an extract of species observations from the nationwide Naturdatabasen. Data are mainly collected by municipalities and governmental organizations (the Danish Environmental Protection Agency), in nature monitoring and other administrative tasks and projects.

Introduction

Welcome to the Danish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Danish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Danish communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Danish speech models that understand and respond to authentic Danish accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Danish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Danish speech and language AI applications:

This dataset contains the digitized treatments in Plazi based on the original journal article Haraguchi, Lumi, Moestrup, Øjvind, Jakobsen, Hans Henrik, Lundholm, Nina (2022): Phytoflagellate diversity in Roskilde Fjord (Denmark), including the description of Pyramimonas octopora sp. nov. (Pyramimonadales, Chlorophyta). Phycologia 61 (1): 45-59, DOI: 10.1080/00318884.2021.2000285, URL: https://doi.org/10.1080/00318884.2021.2000285

Replication data for the article.. Visit https://dataone.org/datasets/sha256%3Ad18874b8dd306ddc40762e17de722ad2532ca7e76cb97f6e98bc59a3104b1ddf for complete metadata about this dataset.

The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified national checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level.

Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness.

Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 aimed to achieve global coverage including non-party countries and all overseas territories of countries, e.g. those of the Netherlands, France, and the United Kingdom.

All kingdoms of organisms occurring in all environments and systems are covered.

Checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records, as well as those under review, are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF Integrated Publishing Tool.

This comprehensive dataset is derived from a large-scale survey focused on communal singing (fællessang) in Denmark. Recently, this cultural practice has garnered significant attention, especially during the COVID-19 lockdowns. Despite its growing prominence, there has been a lack of empirical data concerning the extent, diversity, contexts, and perceptions surrounding communal singing in Denmark—until now. The dataset, compiled from responses of 2,031 Danish adults in October 2022 through a collaboration with YouGov Denmark, offers valuable insights into the current state of singing practices and attitudes among the Danish population. This dataset forms the basis for ongoing research projects and forthcoming publications by the Unit for Song Studies at Aarhus University. The dataset contains a detailed spreadsheet of the survey's comprehensive findings.

Table 1. Flagellates identified to species level, the month in which they were isolated, strain code, techniques used for identification, and GenBank accession number.

This bar chart depicts PERM case filings at Denmark College sorted by the citizenship of the graduates. The filter by major feature provides a deeper understanding of the international diversity of graduates who are being sponsored by employers in the U.S.

CoRal v2 is a comprehensive Automatic Speech Recognition (ASR) dataset designed to capture the diversity of the Danish language across various dialects, accents, genders, and age groups. The primary goal of the CoRal dataset is to provide a robust resource for training and evaluating ASR models that can understand and transcribe spoken Danish in all its variations.

Key Features:

Dialect and Accent Diversity: The dataset includes speech samples from all major Danish dialects as well as multiple accents, ensuring broad geographical coverage and the inclusion of regional linguistic features.

Gender Representation: Both male and female speakers are well-represented, offering balanced gender diversity. Age Range: The dataset includes speakers from a wide range of age groups, providing a comprehensive resource for age-agnostic ASR model development.

High-Quality Audio: All recordings are of high quality, ensuring that the dataset can be used for both training and evaluation of high-performance ASR models.

Forbidden Use Cases Speech Synthesis and Biometric Identification are not allowed using the CoRal dataset. For more information, see addition 4 in our license (https://huggingface.co/datasets/alexandrainst/coral/blob/main/LICENSE).

Access information: This dataset has gated access, meaning access must be requested. Access is available to everyone upon application.

A research paper will be submitted soon, but until then, if you use the CoRal dataset in your research or development, please cite it as follows:

@dataset{coral2024, author = {Dan Saattrup Nielsen, Sif Bernstorff Lehmann, Simon Leminen Madsen, Anders Jess Pedersen, Anna Katrine van Zee and Torben Blach}, title = {CoRal: A Diverse Danish ASR Dataset Covering Dialects, Accents, Genders, and Age Groups}, year = {2024}, url = {https://hf.co/datasets/alexandrainst/coral}, }

The aim of this project was to study the bacterial diversity in Jyllinge Habor, Denmark. For this purpose, environmental DNA and RNA was isolated and used as template in a 16S rRNA gene PCR and two step 16S RT-PCR, respectively.

Bioblitz 2013

• A snapshot of the biodiversity of the city.

Bioblitz 2013 was a public “hunt” for species in a specific area – the “Østre Anlæg” public park in the centre of Copenhagen, Denmark. It took place on May 17, 2013 and the aim was to see how many different species could be found in the course of a single day at a single site. Children, adults, students and museum researchers worked together to map the diversity of species in the selected locality. Plants, fungi and animals - all the life they could possibly find.

All collected species were identified with qualified help from the Natural History Museum of Denmark's researchers and associated experts, and were recorded in a designated registration system, so that the results could be saved for the future. The recording system was developed in collaboration with DanBIF (www.DanBIF.dk) and project www.allearter.dk.

Within one day, a total of 734 recordings were made and 342 species were identified.

Database containing observations of organisms at all taxonomic levels and within all taxonomic kingdoms mainly from Denmark. New observations are continuously added through the registration portal https://arter.dk. The portal is a collaboration project between the Danish Environmental Protection Agency, the Natural History Museum of Denmark and the Natural History Museum Aarhus. The project received generous financial support from Aage V. Jensen Naturfond and 15. Juni Fonden as well as an early contribution from the Danish state. The aim of Arter is to gather all available information about observations of Danish biodiversity and make the data available to the general public. Futhermore the portal offers free access to knowledge about the Danish biodiversity on species level making it easier for the general public to obtain knowledge about the species and protect same. The portal contains old finds as well as new ones and the users continously contribute to the overview of Danish biodiversity by reporting their finds directly into Arter.

Occurrence dataset created by emeritus Associate Professor Peter Vestergaard and published by Department of Biology, University of Copenhagen. In Danish waters, a gradient in seawater salinity between the brackish Baltic Sea and the salt North Sea exists. Along protected, low-energy parts of the adjacent coasts, coastal meadows (salt marshes), influenced by seawater of varying salinity, is a common type of nature. During the 1980s, the vegetation ecology of these salt and brackish coastal meadows was studied in the southeastern part of Denmark by Peter Vestergaard. For the study, 20 localities (loc. 1-20) were selected. The field work was carried out 1982-1985. In each locality, a number of compartments, each with a uniform plant cover, were selected and mapped. In total, 262 compartments (comp.1-262) were studied. The abundance of each species was estimated according to a 1-7 ordinal scale of cover-abundance. The dataset contains URLs for online images of maps with localities and selected examples of the habitats studied.

"Threatened species occurrences, Denmark 1991-2015" is the occurrence dataset developed and used during a three-year industrial PhD-project 2015-2018 "Analysis and prioritization of future efforts for biodiversity – with particular regard to Danish Nature Agency lands" (Buchwald 2018). It was used as part of the basis for the 2018 designation of more than 13 000 ha of new forest reserves in Denmark as part of the Danish governments actions to halt the decline in biodiversity in line with Aichi and EU targets for 2020. The PhD-project was conducted in cooperation with the Natural History Museum of Denmark, Center for Macroecology, Evolution and Climate (CMEC) at University of Copenhagen.
Study species (N = 1378) were delimited as being terrestrial or amphibious species known from Denmark, and being globally or nationally red-listed in the high threat categories (RE, CR, EN, VU), plus birds listed on Annex I of the EU Birds Directive and species listed on annexes II, IV or V of the EU Habitats Directive. Taxonomy and naming was updated to match the standard species checklist of Denmark maintained by the Danish Biodiversity Information Facility (DANBIF).
The quality-checks yielded detailed occurrence data (N= 267,556) for 1,378 species in 24,317 localities counted as different 100 x 100 meter grid cells. One data provider (Fugleognatur.dk) would not let details of the occurences be public, so those observations have details withheld but full details were used in the study.