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These data products are preliminary burn severity assessments derived from data obtained from suitable imagery (including Landsat TM, Landsat ETM+, Landsat OLI, Sentinel 2A, and Sentinel 2B). The pre-fire and post-fire subsets included were used to create a differenced Normalized Burn Ratio (dNBR) image. The dNBR image attempts to portray the variation of burn severity within a fire. The severity ratings are influenced by the effects to the canopy. The severity rating is based upon a composite of the severity to the understory (grass, shrub layers), midstory trees and overstory trees. Because there is often a strong correlation between canopy consumption and soil effects, this algorithm works in many cases for Burned Area Emergency Response (BAER) teams whose objective is a soil burn severity assessment. It is not, however, appropriate in all ecosystems or fires. It is expected that BAER teams will adjust the thresholds to match field observations to produce a soil burn severity. This map layer is a thematic raster image of burn severity classes for all inventoried fires occurring in CONUS. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires which were not discernable from available imagery.

Abstract copyright UK Data Service and data collection copyright owner.

This project was designed to identify the principal general categories of consumers in Great Britain, on the basis of their different levels of engagement in and use of electronic information technologies. A key purpose of the classification was to allow surveys and other data files to be coded with a consumer segmentation relevant to this particular area of behaviour. Around 40,000 electors in Great Britain were thereby allocated to one of 23 distinct clusters in the data. The classification system was built using a series of publicly-available data items known about virtually all British electors. These have been combined in such a way as to optimise discrimination across a series of 80 different measures of consumer behaviour sources, taken from a lifestyle questionnaire database. Thus, the classification was compiled using results of some 80 response codes on over 500,000 lifestyle questionnaires collected over the three years prior to the project.

The classification system is in the form of a look-up table relating the cluster codes to a set of values on two multivariate classification systems, one at the person level (Pixel), the other operating at the postcode level (Mosaic). The information used to compile the classification included: length of residence and household composition obtained from the electoral registers of Great Britain local authorities; estimates of age based partly on attributes from said electoral registers; textual parsing of information from the Post Office Postal Address File (PAF); and names and addresses of company directors supplied by Companies House and from a number of shareholder registers. Because of their proprietary nature, these source files are not included in the dataset.

An arrangement has been negotiated between the Economic and Social Research Council (ESRC) and Experian Ltd., whereby Experian will append their proprietary Mosaic and Pixel codes to survey respondent and/or customer/client records. The look-up table contained in this dataset thus allows the cluster code to be derived from these two data items. As a result of the annual updating of Experian's electoral register, there should be no obstacle to the coding of files containing new electors or those who have recently moved house.

Main Topics:

The data include four variables, comprising Mosaic and Pixel classification codes, and codes denoting likely access to and ability to use electronic communications and technologies. See documentation for further details.

Standard Measures:
Mosaic Classification, (1991), produced by Experian Ltd.

Database files for the MOSAIC database (See associated manuscript: MOSAIC: A Unified Trait Database to Complement Structured Population Models for more information and guidance).

See, also, user guide and further information on the MOSAIC portal: https://mosaicdatabase.web.ox.ac.uk/

The primary key for linking databases is the species name.

File #1 - Primary trait database file, organised by species name (csv). Filte #2 - ERA-5 climate data for all population models in COMADRE, COMPADRE, and PADRIN (csv). Organised by population model ID. File #3 - OTL phylogeny for species in the COMADRE and COMPADRE databases. Note that these data files are intended for loading and use in R using the ape package. (txt)

The American Mosaic Project is a multiyear, multi-method study of the bases of solidarity and diversity in American life. The principal investigators of this project are Doug Hartmann, Penny Edgell and Joseph Gerteis at the "https://twin-cities.umn.edu/" Target="_blank">University of Minnesota. The survey portion of the project consists of a random-digit-dial telephone survey (N=2,081) conducted during the summer of 2003 by the "https://uwsc.wisc.edu/" Target="_blank">University of Wisconsin Survey Center. The survey was designed to gather data on attitudes about race, religion, politics and American identity as well as demographic information and social networks.

Buildings Mosaic

## Overview

Buildings Mosaic is a dataset for instance segmentation tasks - it contains Buildings Um5B annotations for 242 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

These data products are preliminary burn severity assessments derived from data obtained from suitable imagery (including Landsat TM, Landsat ETM+, Landsat OLI, Sentinel 2A, and Sentinel 2B). The pre-fire and post-fire subsets included were used to create a differenced Normalized Burn Ratio (dNBR) image. The dNBR image attempts to portray the variation of burn severity within a fire. The severity ratings are influenced by the effects to the canopy. The severity rating is based upon a composite of the severity to the understory (grass, shrub layers), midstory trees and overstory trees. Because there is often a strong correlation between canopy consumption and soil effects, this algorithm works in many cases for Burned Area Emergency Response (BAER) teams whose objective is a soil burn severity assessment. It is not, however, appropriate in all ecosystems or fires. It is expected that BAER teams will adjust the thresholds to match field observations to produce a soil burn severity. This map layer is a thematic raster image of burn severity classes for all inventoried fires occurring in CONUS during calendar year 2022. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires which were not discernable from available imagery.

The survey is from the American Mosaic Project, a multiyear, multimethod study of the bases of solidarity and diversity in American life. The survey contains items measuring the place of diversity in visions of American society and in respondents' own lives; social and cultural boundaries between groups and dimensions of inclusion and exclusion; racial and religious identity, belonging and discrimination; opinions about sources of advancement for Whites and African Americans; opinions about immigration and assimilation; diversity in respondents' close-tie network; political identity and demographic information. The survey also includes oversamples of African American and Hispanic respondents, allowing for comparisons across racial/ethnic categories. Demographic variables include race, age, gender, religion, level of education, United States citizenship status, partisan affiliation, and family income. See Appendix: Project Narrative for more information.

This dataset is produced for the Government of Alberta and is available to the general public. Please consult the Distribution Information of this metadata for the appropriate contact to acquire this dataset. The Alberta Ground Cover Classification Mosaic is a land cover dataset for the province of Alberta. It is a composite of the Alberta Ground Cover Classification (AGCC) created by Alberta Environment and Sustainable Resource Development for the green areas of the province and the Land Cover for Agricultural Regions of Canada, circa 2000 created by Agriculture and Agri-Food Canada for the white areas of the province.

Mosaic is Experian's flagship consumer classification for today's multichannel world, providing a deep view of UK consumers' characteristics and lifestyles.

Mosaiced 100m resolution global datasets. The methodology used to estimate the annual subnational census-based figures can be found in LLoyd et al (https://www. tandfonline.com/doi/full/10.1080/20964471.2019.1625151). The mapping approach is Random Forest-based dasymetric redistribution. More info at: www.worldpop.org.

Population genomic data was obtained through individually barcoded 2b-RAD libraries that were constructed following the procedures in Pereyra et al. (2023). The libraries were sequenced using a Novaseq6000 Illumina platform at SciLifeLab, Uppsala. A total of 962 individuals from 55 different sites were sequenced. Removal of PCR duplicates, quality filtering, and variant calling was performed in the computer cluster Albiorix (M. Töpel, IVL, Sweden), following a reference-based pipeline modified from Mikhail Matz available at https://github.com/z0on/2bRAD_GATK. Reads were mapped to an F. vesiculosus draft genome assembly previously used for population genomic studies (Kinnby et al., 2020; Pereyra et al., 2023; NCBI Bioproject No. PRJNA629489). Variant calling calibration was carried out with technical replicates labeled with the Individual name followed by the suffix "Rep". These replicates are included in the dataset. Morphological data comprises three morphometric characters: Fron...

Mosaiced 100m resolution global datasets. The methodology used to estimate the annual subnational census-based figures can be found in LLoyd et al (https://www. tandfonline.com/doi/full/10.1080/20964471.2019.1625151). The mapping approach is Random Forest-based dasymetric redistribution. More info at: www.worldpop.org.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into state, county, congressional district (116th) and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality in the United States, including Puerto Rico. A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis. The county and state layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Each layer has been enriched with a set of 2019 US demographic attributes (excluding Puerto Rico) apportioned to the geography in order to map patterns alongside each other. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries:50km hex bins generated using the Generate Tessellation toolStates and counties come from 2018 TIGER boundaries with coastlines clipped116th Congressional Districts come from this ArcGIS Living Atlas layerData processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The Enrich tool was run to add 2019 Esri demographic and 2014-2018 ACS attributes to the geographies. Attributes such as population, poverty, minority population, and others were added to the layer.To create the population-weighted attributes on the state and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and summarized within the state and county boundaries.The summation of these values were then divided by the total population of each state/county. This population value was determined by summarizing the population values from the hex bins within each geography.

The global mosaic tile market is experiencing robust growth, driven by increasing demand from both residential and commercial construction sectors. The market's diverse applications, encompassing everything from kitchen backsplashes to expansive bathroom installations and sophisticated commercial interiors, contribute to its expansion. While precise figures for market size and CAGR aren't provided, a logical estimation based on industry reports and current market trends suggests a 2025 market size of approximately $5 billion USD, exhibiting a compound annual growth rate (CAGR) of 6% from 2025 to 2033. This growth is fueled by several key factors: the enduring appeal of mosaic tiles for their aesthetic versatility and design flexibility, the rise of sustainable and eco-friendly tile options (such as recycled glass mosaic tiles), and ongoing investments in infrastructure projects globally. However, the market also faces certain challenges. Fluctuations in raw material prices, particularly ceramic and glass, can impact production costs and profitability. Furthermore, competition from alternative flooring and wall cladding materials, along with economic downturns impacting construction activity, represent potential restraints on market expansion. The segmentation of the mosaic tile market into ceramic and glass types, along with residential and commercial applications, offers valuable insights into specific market dynamics and growth potential within each category. Leading players, including SCG, Mohawk, Lamosa, RAK Ceramics, and others, are actively engaging in strategies such as product innovation, strategic partnerships, and geographic expansion to maintain their competitive edge in this dynamic market. The Asia-Pacific region, particularly China and India, is anticipated to witness significant growth due to rapid urbanization and infrastructure development. This comprehensive report provides an in-depth analysis of the global mosaic tiles market, projected to be worth $15 billion by 2028. We delve into market dynamics, competitive landscapes, and future growth prospects, focusing on key trends shaping this vibrant sector. This report is ideal for investors, manufacturers, distributors, and anyone seeking a detailed understanding of the mosaic tile industry.

Demographic information specialists Experian Limited have made available a selection of their popular datasets at 2001 Lower Layer Super Output Area (LSOA) level. The Experian data are restricted to staff and students from UK further/higher education institutions. The Experian data are restricted to staff and students from UK further/higher education institutions.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into state, county, congressional district (116th) and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality in the United States, including Puerto Rico. A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis. The county and state layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Each layer has been enriched with a set of 2019 US demographic attributes (excluding Puerto Rico) apportioned to the geography in order to map patterns alongside each other. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries:50km hex bins generated using the Generate Tessellation toolStates and counties come from 2018 TIGER boundaries with coastlines clipped116th Congressional Districts come from this ArcGIS Living Atlas layerData processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The Enrich tool was run to add 2019 Esri demographic and 2014-2018 ACS attributes to the geographies. Attributes such as population, poverty, minority population, and others were added to the layer.To create the population-weighted attributes on the state and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and summarized within the state and county boundaries.The summation of these values were then divided by the total population of each state/county. This population value was determined by summarizing the population values from the hex bins within each geography.

During the MOSAiC expedition, a comprehensive aerial observation program was conducted by the helicopters of the R/V Polarstern. This dataset contains two GeoTiff orthomosaics compiled from aerial RGB images recorded during Leg 4 above the central observatory (CO) on 2020-06-30 and 2020-07-22 and derived surface type classes maps on the leg 4 floe stored in shape file format. The data was processed based on the workflow presented in: https://gitlab.awi.de/nifuchs/pasta-ice. Both orthomosaics were overlaid with the position of the leg 4 floe on 2020-06-30 to facilitate comparison between both. The data is projected to UTM31N (EPSG: 32631) and resampled to a resolution of 0.5m. The aim of this dataset was to retrieve melt pond fraction on the leg 4 floe and give a visual identification for pond locations on both dates.

This dataset tracks annual distribution of students across grade levels in Mosaic Home Education Partnership

Historical Dataset of Mosaic Home Education Partnership is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2003-2023),Total Classroom Teachers Trends Over Years (2002-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2002-2023),American Indian Student Percentage Comparison Over Years (2010-2013),Asian Student Percentage Comparison Over Years (2004-2022),Hispanic Student Percentage Comparison Over Years (2002-2023),Black Student Percentage Comparison Over Years (2002-2018),White Student Percentage Comparison Over Years (2003-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2002-2023),Free Lunch Eligibility Comparison Over Years (2014-2022),Reduced-Price Lunch Eligibility Comparison Over Years (2016-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2022),Overall School Rank Trends Over Years (2010-2022)