Other data providers sharing occurence data via the Norwegian Species Map Service. These providers allow Nbic to share their data as they do not provide their own IPT servide. Providers: Following databases/datasets from the Norwegian Environment Agency (http://www.miljodirektoratet.no/): Predator database, water species database, naturedatabase and salmon registry. From Sustain.no (http://www.miljolare.no/en/) following databases/datasets: Alien species, plants, butterflies, earthworms, snails, water species, garden birds, coastal species, ponds species and steam species.

This dataset tracks annual diversity score from 2010 to 2023 for Vulcan Middle School vs. Michigan and Norway-Vulcan Area Schools School District

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 Norway town. The dataset can be utilized to gain insights into gender-based income distribution within the Norway town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Norway town, among individuals aged 15 years and older with income, there were 345 men and 311 women in the workforce. Among them, 151 men were engaged in full-time, year-round employment, while 147 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.62% fell within the income range of under $24,999, while 10.20% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 10.60% of men in full-time roles earned incomes exceeding $100,000, while 6.80% 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 Norway town median household income by race. You can refer the same here

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

The aim of the project was to assess the effect of water-level regulation on diversity of lake littoral meiofauna and protists. Littoral fine-gravel bottoms of four pairs of Norwegian lakes were sampled in June and September 2021. Lakes were sampled in pairs that had similar water-quality but one of which was regulated and the other was unregulated. The bottom material was sampled at 30-40 cm depth, 1-5 m from the shoreline. Six replicate samples from each sampling point was taken by lowering a 25-cm diameter cylinder (12 cm high) to the bottom and 5 spoons of the bottom material from inside the cylinder was transferred to each sample bottle. No macroinvertebrates (size > 1 mm) were observed in the spoons before emptying them to the sample bottles. Water was filtrated out of the bottles using a 10 µm mesh, and then, samples were stored in 96-% ethanol. In total, the dataset contains 101 samples, including 5 control samples and 96 samples from the lakes. Taxonomic composition of bottom-dwelling meiofauna and protists was determined using DNA metabarcoding of the V4 fragment of the 18S rRNA gene. Taxa were identified using the PR2 reference database (protists) and NCBI GenBank nucleotide database (meiofauna). Caution should be exercised when interpreting occurrences of single species, as the DNA metabarcoding and bioinformatics may contain errors. The effect of regulation on species richness and change in community composition (Alpha-diversity) was assessed using mixed effects models.

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 Norway. The dataset can be utilized to gain insights into gender-based income distribution within the Norway population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Norway, among individuals aged 15 years and older with income, there were 226 men and 182 women in the workforce. Among them, 136 men were engaged in full-time, year-round employment, while 116 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 13.24% fell within the income range of under $24,999, while 18.97% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 16.18% of men in full-time roles earned incomes exceeding $100,000, while 1.72% 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 Norway median household income by race. You can refer the same here

This dataset tracks annual diversity score from 2003 to 2023 for Norway J7 School District vs. Wisconsin

Field data: 30 x 30 meter plots (n=28). Biodiversity of vascular plants and lichens. Percent cover of rock & bare ground, grasses & sedges, shrubs, other woody species, lichens, and mosses.

Citizen science species observations from the Species Observation Service in Norway (Artsobservasjoner).

This dataset tracks annual diversity score from 2016 to 2023 for Norway Elementary School vs. Iowa and Benton Comm School District

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 Norway town. The dataset can be utilized to gain insights into gender-based income distribution within the Norway town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Norway town, among individuals aged 15 years and older with income, there were 1,702 men and 2,338 women in the workforce. Among them, 528 men were engaged in full-time, year-round employment, while 781 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 3.79% fell within the income range of under $24,999, while 2.18% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 19.70% of men in full-time roles earned incomes exceeding $100,000, while 16.01% 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 Norway town median household income by race. You can refer the same here

The genetic diversity and population structure of Arabidopsis thaliana populations from Norway were studied and compared to a worldwide sample of A. thaliana in order to investigate the demographic history and elucidate possible colonization routes of populations at the northernmost species limit. We genotyped 282 individuals from 31 local populations using 149 single nucleotide polymorphism (SNP) markers. A high level of population subdivision (FST = 0.85 ± 0.007) was found indicating that A. thaliana is highly structured at the regional level. Significant relationships between genetic and geographic distances were found, suggesting an isolation by distance mode of evolution. Genetic diversity was much lower and the level of linkage disequilibrium (LD) higher in populations from the north (65–68oN) compared to populations from the south (59–62oN); this is consistent with a northward expansion pattern. A neighbor-joining (NJ) tree showed that populations from northern Norway form a separate cluster, while the remaining populations are distributed over a few minor clusters. Minimal gene flow seems to have occurred between populations in different regions, especially between the geographically distant northern and southern populations. Our data suggest that northern populations represent a homogenous group that may have been established from a few founders during northward expansions, while populations in the central part of Norway constitute an admixed group established by founders of different origins, most probably as a result of human-mediated gene flow. Moreover, Norwegian populations appeared to be homogenous and isolated compared to a worldwide sample of A. thaliana, but they are still grouped with Swedish populations, which may indicate common colonization histories.

Introduction

Welcome to the Norwegian General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Norwegian 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 Norwegian 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 Norwegian speech models that understand and respond to authentic Norwegian accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Norwegian. 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 Norwegian speech and language AI applications:

The Directorate of Integration and Diversity (IMDi) publishes statistics on immigration and integration on its website, with data on municipal, industrial, county and country levels, as well as districts in the Oslo Statistical Base covering a number of topics, from population and demographics, education, labour market, living conditions and grant payments from IMDi. The statistics are mainly specially ordered from Statistics Norway, but there are also data from IMDi’s systems: resettlement of refugees, introductory programmes for refugees and grants paid to Norwegian municipalities. The web pages provide a clear presentation of current statistics, explanatory text to the tables, and the possibility to search for and download data. Different data sets are available for different time periods, but everything should be available from 2014. The data is updated mainly once a year, but at different times. IMDi’s own statistics have other update routines. The Directorate of Integration and Diversity (IMDi) publishes statistics on immigration and integration on its website, with data on municipal, industrial, county and country levels, as well as districts in the Oslo Statistical Base covering a number of topics, from population and demographics, education, labour market, living conditions and grant payments from IMDi. The statistics are mainly specially ordered from Statistics Norway, but there are also data from IMDi’s systems: resettlement of refugees, introductory programmes for refugees and grants paid to Norwegian municipalities. The web pages provide a clear presentation of current statistics, explanatory text to the tables, and the possibility to search for and download data. Different data sets are available for different time periods, but everything should be available from 2014. The data is updated mainly once a year, but at different times. IMDi’s own statistics have other update routines.

Data from the Norwegian breeding bird monitoring scheme from 2006 up until today. The project is carried out in cooperation between BirdLife Norway, Norwegian Institute for Nature Research (NINA) and the Norwegian Environment Agency, and is the most important project for monitoring population trends for Norwegian bird species on land.

The data collections were conducted as part of a larger project called FuncFinse. We aimed to study if and how the (functional) diversity of lichen communities determines the species diversity and community composition of micro-arthropods (Collembola). We created experimental patches with mixtures of one, two, three, or four different lichen species and placed these in lichen mats in semi-open pine forest (Pinus sylvestris) in Kollåsen, Ski, Norway. The soil micro-arthropods were harvested from the lichen patches after one summer season of incubation.

Altitudinal gradients provide valuable information about the effects of environmental variables on changes in species richness and composition as well as the distribution of below ground fungal communities. Since most knowledge in this respect has been gathered on aboveground communities, we focused our study towards the characterization of belowground fungal communities associated with two different ages of Norway spruce (Picea abies) trees along an altitudinal gradient. By sequencing the internal transcribed spacer (ITS) region on the Illumina platform, we investigated the fungal communities in a floristically and geologically relatively well explored forest on the slope of Mt. Iseler of the Bavarian Alps. From fine roots and rhizosphere of a total of 90 of Norway spruce trees from 18 plots we detected 1285 taxa, with a range of 167 to 506 (average 377) taxa per plot. Fungal taxa are distributed over 96 different orders belonging to the phyla Ascomycota, Basidiomycota, Chrytridiomycota, Glomeromycota, and Mucoromycota. Overall the Agaricales (438 taxa) and Tremellales (81 taxa) belonging to the Basidiomycota and the Hypocreales (65 spp.) and Helotiales (61 taxa) belonging to the Ascomycota represented the taxon richest orders. The evaluation of our multivariate generalized mixed models indicate that the altitude has a significant influence on the composition of the fungal communities (p < 0.003) and that tree age determines community diversity (p < 0.05). A total of 47 ecological guilds were detected, of which the ectomycorrhizal and saprophytic guilds were the most taxon-rich. Our ITS amplicon Illumina sequencing approach allowed us to characterize a high fungal community diversity that would not be possible to capture with fruiting body surveys alone. We conclude that it is an invaluable tool for diverse monitoring tasks and inventorying biodiversity, especially in the detection of microorganisms developing very ephemeral and/or inconspicuous fruiting bodies or lacking them all together. Results suggest that the altitude mainly influences the community composition, whereas fungal diversity becomes higher in mature/older trees. Finally, we demonstrate that novel techniques from bacterial microbiome analyses are also useful for studying fungal diversity and community structure in a DNA metabarcoding approach, but that incomplete reference sequence databases so far limit effective identification.

What’s Included

Introducing the Norwegian Newspaper, Books, and Magazine Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Norwegian language.

Containing a total of 5000 images, this Norwegian OCR dataset offers an equal distribution across newspapers, books, and magazines. Within, you'll find a diverse collection of content, including articles, advertisements, cover pages, headlines, call outs, and author sections from a variety of newspapers, books, and magazines. Images in this dataset showcases distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personal identifiable information (PII), and in each image a minimum of 80% space is contain visible Norwegian text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, further enhancing dataset diversity. The collection features images in portrait and landscape modes.

All these images were captured by native Norwegian people to ensure the text quality, avoid toxic content and PII text. We used latest iOS and android mobile devices above 5MP camera to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data you will also receive detailed structured metadata in CSV format. For each image it includes metadata like device information, source type like newspaper, magazine or book image, and image type like portrait or landscape etc. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of Norwegian text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native Norwegian crowd community.

If you require a custom dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this image dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the Norwegian language. Your journey to enhanced language understanding and processing starts here.

The Norwegian Biodiversity Information Centre is a national source of information on biodiversity. The organisations main function is to supply the public with updated and accessible information on Norwegian species and ecosystems. More information on this dataset can be found in the Freshwater Metadatabase - BFE_100 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BFE_100).

This dataset tracks annual diversity score from 1991 to 2023 for Muskego-Norway School District vs. Wisconsin