Supplementary Data for "Revisiting the discrimination and distribution of S-type granites from zircon trace element compositions" by Nick Roberts et al.

This is a collection of data tables supporting the LCMAP CONUS Geographic Assessment v1.0. The data used to generate these tables come from the USGS LCMAP reference dataset and the map products released by LCMAP. Tables include annual land cover class composition and annual rate of land cover change metrics developed with a post-stratified estimator. Other tables including annual gross change of specific types of land covers, cumulative metrics of overall geographic footprint of change, frequency of overall geographic footprint of change, overall area estimates of specific class changes, and all unique changes in land cover classes. All tables cover the time period 1985-2016. All values in these tables are presented in percent and/or square kilometers of CONUS and include standard errors (SE). Tables 1a and 1b are annual land-cover class area estimates. Tables 1a and 1b are annual land-cover class area estimates. Table 1a presents the estimated area of each LCMAP land cover class as a percent of CONUS and Table 1b is the same in square kilometers, both with SEs. The area estimates include the following eight LCMAP land covers classes: Developed, Cropland, Grass/Shrub, Tree Cover, Water, Wetland, Snow/Ice, and Barren (Brown et al., 2020; Zhu et al., 2014).Table 2 is the estimated overall net change in each of the eight LCMAP land-cover classes: Developed, Cropland, Grass/Shrub, Tree Cover, Water, Wetland, Ice/Snow, and Barren (Brown et al., 2020; Zhu et al., 2014). It includes percent and square kilometers of CONUS in 1985 and 2016. The net area that changed between 1985 and 2016 is presented in percent and km2 of CONUS with the standard error of this change. Table 3 represents area estimates for the area that changed a specific number of times during 1985–2016. These areas are presented as both the fraction of LCMAP’s CONUS extent and as the equivalent in square kilometers, with standard errors. The overall footprint of change that occurred between 1985 and 2016 is presented as the percent and km2 of CONUS. Table 4 is the estimated percent and areal extent (km2) of CONUS that experienced either a change, or none, for each year 1986–2016, with associated standard errors. Tables 5a–h present the areal extent estimates (in km2) for specific land-cover class changes with standard error for every year, 1986–2016. The specific land-cover classes include Developed, Cropland, Grass/Shrub, Tree Cover, Water, Wetland, Ice/Snow, and Barren (Brown et al., 2020; Zhu et al., 2014). Table 6a presents area estimates (km2) of CONUS for all land-cover class changes with standard error for every year, 1986–2016. Table 6b presents area estimates (km2) of CONUS that did not change for all eight LCMAP land-cover classes with standard error for every year, 1986–2016. The eight LCMAP land-cover classes include Developed, Cropland, Grass/Shrub, Tree Cover, Water, Wetland, Ice/Snow, and Barren (Brown et al., 2020; Zhu et al., 2014). Table 7 presents the estimated area (km2) of CONUS for four groupings of land-cover class changes with standard error for every year, 1986–2016. The eight LCMAP land-cover classes and class codes include Developed (1), Cropland (2), Grass/Shrub (3), Tree Cover (4), Water (5), Wetland (6), Ice/Snow (7), and Barren (8) (Brown et al., 2020; Zhu et al. 2014). The four groups include “Natural Resource Cycles”, “Increases in Developed and Built-up Land”, “Surface Water Expansion/Contraction”, and “Other”. Table 8 presents the cumulative (1985-2016) area of all specific land cover class changes, 56 in total.

The purpose of the SNF study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. Sites were chosen in uniform stands of aspen or spruce. The dominant species in the site constituted over 80 percent, and usually over 95 percent, of the total tree density and basal area. Aspen stands were chosen to represent the full range of age and stem density of essentially pure aspen, of nearly complete canopy closure, and greater than two meters in height. Spruce stands ranged from very sparse stands on bog sites, to dense, closed stands on more productive peatlands. Use of multiple plots within each site allowed estimation of the importance of spatial variation in stand parameters. Within each plot, all woody stems greater than two meters in height were recorded by species and the following dimensions were measured: diameter breast height, height of the tree, height of the first live branch, and depth of crown. For each plot, a two meter diameter subplot was defined at the center of each plot. Within this subplot, the percent of ground coverage by plants under one meter in height was determined by species. These data, averaged for the five plots in each site, are presented in this data set (i.e., SNF Forest Understory Cover Data (Table)) in tabular format, e.g. plant species with a count for that species at each site. The same data are presented in the SNF Forest Understory Cover Data data set but are arranged with a row for each species and site and a percent ground coverage for each combination.

This dataset is a compilation of the data export tables available on WUEdata for the 2020 Urban Water Management Plans (UWMPs).

• "2020_UWMP_Tables.xlsx" contains each of the 99 tables as a separate workbook (Last Updated 2024)
• The worksheets for each table are also individually stored separately as TSV files in "2020_UWMP_TSV_Export.zip" (Last Updated 2023)
• Column definitions, data types, and table locations are included in "2020_Data_Dictionary.xlsx"
• Shortened versions of the table names are used in the XLSX worksheet names and TSV filenames; "Table_Name_Key.xlsx" links the shortened names and original names
• For reference, the 2020 UWMP Guidebook that was provided to water suppliers is included in this dataset

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. Each file contains annual total releases for the past ten years by media (air, water or land), broken-down by province, industry or substance. Files are in .CSV format. The results can be further broken down using the pre-defined search available at the bottom of the NPRI Data Search webpage. The results returned by the NPRI search engine may differ from the numbers contained in the downloadable files. The online search engine’s results will display releases, disposals and transfers reported by facilities, but does not distinguish between media type (i.e. air, water, land). It also displays facilities reporting only under Ontario Regulation 127/01 and facilities submitting “did not meet criteria” reports. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html Supplemental Information More NPRI datasets and mapping products are available here: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/access.html Supporting Projects: National Pollutant Release Inventory (NPRI)

Average species environmental variables for each dataset, output of https://github.com/KyleTDavid/GlobalPloidy2022/blob/main/extract_geodata.r

This data release contains the boundaries of assessment units and input data for the assessment of undiscovered Deep Tuscaloosa Group sandstones conventional gas resources in the U.S. Gulf Coast in Louisiana and Mississippi. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. In addition to the shapefile, for U.S. assessments, allocation tables are provided that enumerate percentages assigned to various land categories. Machine-readable tables are also provided that contain the input and results for each assessment unit summarized in the USGS Fact Sheet. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.

The instrument maps and data tables aim to give an insight into the mapping process and datasets used to reconcile the historical data for the households and NPISH financial categories AF.6, AF.7 and AF.8 assets and liabilities.

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police. \r
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The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public. \r
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The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose. \r
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Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.\r
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The Crime Statistics Agency publishes location type data for all offences where a location type was recorded by Victoria Police. There are three main location types; Residential, Community and Other. These types are further broken down into Subdivisions which show an intermediate level of information, and further into Groups which show a finer level of detail\r
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Data Classification - https://www.crimestatistics.vic.gov.au/about-the-data/classifications \r
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Glossary and Data Dictionary - https://www.crimestatistics.vic.gov.au/about-the-data/glossary-and-data-dictionary\r

This data release contains the boundaries of assessment units and input data for the assessment of undiscovered conventional oil and gas resources in the Smackover formation in the U.S. Gulf Coast. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. In addition to the shapefile, for U.S. assessments, allocation tables are provided that enumerate percentages assigned to various land categories. Machine-readable tables are also provided that contain the input and results for each assessment unit summarized in the USGS Fact Sheet. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.

The displayed data on file types for iCloud shows results of an exclusive Statista survey conducted in the United States in 2018. Some ** percent of respondents answered the question ''What do you use your iCloud for?'' with ''Photos''.The Survey Data Table for the Statista survey Tech Giants and Digital Services in the United States 2019 contains the complete tables for the survey including various column headings.

Contains monthly data from the Assuring Transformation dataset. Data is available in Excel or CSV format. PLEASE NOTE: Some updates to the structure and numbering of the data tables and csv were applied from April 2021. This was primarily to group similar table types and content together. Additionally we have increased the amount of tables that have time series data retrospectively updated each month (green tabs). We welcome any feedback on this updated format.

The data tables from the OCSE Annual Report to Congress highlights financial and statistical child support achievements based on data reported by state and tribal child support agencies.

Units of Response: States

Type of Data: Administrative

Tribal Data: No

Periodicity: Annual

Demographic Indicators: Unavailable

SORN: https://www.federalregister.gov/documents/2015/04/02/2015-07440/privacy-act-of-1974-system-of-records-notice

Data Use Agreement: No

Data Use Agreement Location: Unavailable

Granularity: State

Spatial: United States

Geocoding: State

Frequencies of snake behaviors for each species and predatory model. (XLSX)

This data table provides the detailed data quality assessment scores for the Embedded Generation by Type dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks.We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.DisclaimerThe data quality assessment may not represent the quality of the current dataset that is published on the Open Data Portal. Please check the date of the latest quality assessment and compare to the 'Modified' date of the corresponding dataset. The data quality assessments will be updated on either a quarterly or annual basis, dependent on the update frequency of the dataset. This information can be found in the dataset metadata, within the Information tab. If you require a more up to date quality assessment, please contact the Open Data Team at opendata@spenergynetworks.co.uk and a member of the team will be in contact.

This statistic shows the results of a survey conducted in the United States in 2018 on the interest in alternative drive types. Some ** percent of respondents stated that they were not at all interested in alternative drive types. The Survey Data Table for the Statista survey Cars & Mobility in the United States 2018 contains the complete tables for the survey including various column headings.