• An image raster of Day Land Surface Temperature (LSTDAY) of September 2016;- An image raster of Normalized Difference Vegetation Index (NDVI) of September 2016;- A polygonal shapefile of disease distribution of 'West Nile Disease' in the world in 2016;- A punctual shapefile of outbreaks of 'West Nile Disease' in the world in 2016;- An excel file containing an extract from the 'west nile' outbreaks of 2016 with associated day and night temperature data and vegetation indices, each in a dedicated sheet of the excel file

Harmonized statistics at national and sub-national level provide the forest biomass, FAWS and increment. They provide: the forest area (ha), the forest area available for wood supply (FAWS) (ha), the forest area not available for wood supply biomass (FNAWS) (ha), the forest aboveground biomass as AGB stock (t) and AGB/ha (t/ha), the forest aboveground biomass stock available for wood supply (BAWS) (t) and the forest aboveground biomass stock not available for wood supply (BNAWS) (t) for the year 2020; and the forest area (ha), the Forest area Available for Wood Supply (FAWS) (ha), and the Gross Annual Increment (GAI), the Annual Natural Losses (ANL), the Net Annual Increment (NAI) for the forest area and FAWS area for the period 2010 - 2020 (reference year: 2015) in units of volume per year (m3/year) and volume per hectare per year (m3/ha/year). NB: In this collection, the same statistics are also provided as spatial data in three Shapefile (“Biomass_Statistics.shp”, “FAWS_Statistics.shp”, “Increment_Statistics.shp”), which which provide the biomass, FAWS and increment for different administrative or NUTS units.

This is a zipped GIS compatible shapefile of gravity data points used in the Milford, Utah FORGE project as of March 21st, 2016. The shapefile is native to ArcGIS, but can be used with many GIS software packages. Additionally, there is a .dbf (dBase) file that contains the dataset which can be read with Microsoft Excel. The Data was downloaded from the PACES (Pan American Center for Earth and Environmental Studies) hosted by University of Texas El Paso. A readme file is included in the archive with abbreviation explanations and units.

This dataset tracks the updates made on the dataset "2015 QHP Landscape SHOP Market Medical Excel" as a repository for previous versions of the data and metadata.

Data velocity ice in the glacier surface are critical for glacier dynamics models. Although not generally used as boundary conditions inhomogeneous (as, instead, is usually set boundary conditions of homogeneous Neumann type of zero traction on the surface) Dirichlet type, surface speeds are used to adjust free model parameters such as the coefficient B of the constitutive relation or the multiplicative factor that usually appears in the parameterization of Weertman type of basal sliding velocity, so to minimize the differences between the speeds observed and calculated by the model on the surface.

This dataset tracks the updates made on the dataset "QHP Landscape SHOP Market Dental Excel" as a repository for previous versions of the data and metadata.

The datasets in this zip file are in support of Intelligent Transportation Systems Joint Program Office (ITS JPO) report FHWA-JPO-16-385, "Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs — Evaluation Report for ATDM Program," https://rosap.ntl.bts.gov/view/dot/32520 and FHWA-JPO-16-373, "Analysis, modeling, and simulation (AMS) testbed development and evaluation to support dynamic mobility applications (DMA) and active transportation and demand management (ATDM) programs : Dallas testbed analysis plan," https://rosap.ntl.bts.gov/view/dot/32106 The files in this zip file are specifically related to the Dallas Testbed. The compressed zip files total 2.2 GB in size. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were converted to .pdf document files which are an open, archival format. These pdfs were then added to the zip file alongside the original .docx files. These files can be unzipped using any zip compression/decompression software. This zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .cvs text files which can be read using any text editor; .txt text files which can be read using any text editor; .docx document files which can be read in Microsoft Word and some other word processing programs; . xlsx spreadsheet files which can be read in Microsoft Excel and some other spreadsheet programs; .dat data files which may be text or multimedia; as well as GIS or mapping files in the fowlling formats: .mxd, .dbf, .prj, .sbn, .shp., .shp.xml; which may be opened in ArcGIS or other GIS software. [software requirements] These files were last accessed in 2017.

The global market size of Coffee Shop is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Coffee Shop Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Coffee Shop industry. The key insights of the report:
1.The report provides key statistics on the market status of the Coffee Shop manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Coffee Shop industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Coffee Shop Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Coffee Shop as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Coffee Shop market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

This dataset includes files of weather data recorded by the U.S. Coast Guard onboard the U.S. Coast Guard Cutter Healy during the Bering Sea Ecosystem Study-Bering Sea Integrated Ecosystem Research Program (BEST-BSIERP) 2009 0902 (late spring) cruise. BEST-BSIERP together are the Bering Sea project. These files are in Excel Spreadsheet format.

Sediment grab samples were taken in summer of 2017 and 2018 using a modified van Veen grab sampler. A sub-sample of the top two centimeters was taken for further lab analysis. Dried and homogenized splits of the samples were analyzed for chemical composition using an Innov-X Alpha series 4000 XRF (Innov-X Systems, Woburn, MA). The results of the measurements are presented as ppm. The XRF analytical protocol included the following elements: P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn As, Se, Br, Rb, Sr, Zr, Mo, Ag, Cd, Sn, Sb, I, Ba, Hg, Pb, Bi, Th, and U. However, only Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Cu, Zn, As, Br, Rb, Sr, Zr and Pb were consistently present at levels above background detection in surficial sediments collected in the LIS Phase II area. The data is presented here as an ESRI shapefile. There is an accompanying Excel spreadsheet. Funding was provided by the Long Island Sound Mapping Fund administered cooperatively by the EPA Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection (DEEP).

This dataset tracks the updates made on the dataset "QHP Landscape ID SHOP Market Medical Excel" as a repository for previous versions of the data and metadata.

This data is associated with the Nevada Play Fairway project and includes excel files containing raw 2-meter temperature data and corrections. GIS shapefiles and layer files contain ing location and attribute information for the data are included. Well data includes both deep and shallow TG holes, GIS shapefiles and layer files. Shapefile containing Granite Springs Valley well data

A large number of high-resolution geophysical surveys between Cape Hatteras and Georges Bank have been conducted by federal, state, and academic institutions since the turn of the century. A major goal of these surveys is providing a continuous view of bathymetry and shallow stratigraphy at the shelf edge in order to assess levels of geological activity during the current sea level highstand. In 2012, chirp seismic-reflection data was collected by the U.S. Geologial Survey aboard the motor vessel Tiki XIV near three United States mid-Atlantic margin submarine canyons. These data can be used to further our understanding of passive continental margin processes during the Holocene, as well as providing valuable information regarding potential submarine geohazards. For more information on the U.S. Geological Survey involvement in this effort, see https://cmgds.marine.usgs.gov/fan_info.php?fan=2012-005-FA.

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, along with the geospatial data describing the spatial units where water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. We collected water rights databases from 11 western United States states either by downloading them from publicly accessible web portals, or by contacting state water management representatives; detailed descriptions of where and when the data was collected is provided in the README.txt, as well as Lisk et al.(in review). This collection of data are those raw water rights. Each state formats their data differently, meaning that file types, field availability, and names vary from state to state. Note, the data provided here reflects the state of the water rights databases at the time we collected the data; updates have likely occurred in many states. Some pieces of information are common among all states. These are: priority date, volume or flow of water allowed by the right, stated water use of the right, and some means of identifying the geography and source of the water pertaining to the right - typically the coordinates of the Point of Diversion (PoD) of a waterbody or well. Arizona regulates water in a different way than the other 10 states. Outside of some relatively small critical agricultural areas called Active Management Areas (AMAs), Arizona does not maintain any water rights. However, the state does require registration of surface and groundwater pumping devices, which includes disclosing the mechanical specifics of the devices. We used these records as a proxy for water rights. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. In addition, the following states have requested specifically worded disclaimers to be included with their data. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." The available data is provided as a series of compressed files, which each containing the full data collected from each state. Some of the files have been renamed, to more easily know which state the data belongs to. The file renaming was also required as some files from different states had the same name. In other cases, the data for a state has been placed in a folder indicating which state it belongs to - as the state organized its data by selected subregions. Below is a brief description of the format of the collected data from each state. ArizonaRights_StatementOfClaimants: A folder containing a database of interconnected CSV files. The soc_erd.pdf file contains a visual flowchart of how the various files are connected, beginning with SOC_MAIN.csv in the center of the page. ArizonaRights_SurfaceWaterRightsData: A folder containing a database of a single Shapefile and 10 associated CSVs. SurfaceWater.pdf contains a visual flowchart of how the various files are connected, beginning with ADWR_SW_APPL_REGRY.csv. ArizonaRights_Well55Registry: A folder containing a database of a single Shapefile and 59 associated CSVs. Wells55.pdf contains a visual flowchart of how the various files are connected, beginning with WellRegistry.shp. CaliforniaRights_eWRIMS_directDatabase: A folder containing a collection of four "series" Microsoft Excel files, as either XLS or XLSX. The four "series": byCounty, byEntity (what type of legal entity holds the right), byUse (stated water use), and byWatershed, are various methods by which the California water rights are organized within the state's database. However, it was observed that by only collecting a single series, not all water rights were being provided. So, essentially, the majority of records within each "series" are copies of each other, with each "series" containing some unique records. ColoradoRights_NetAmounts: A folder containing 78 CSV files, with one file per Colorado Water District. IdahoRights_PointOfDiversion: A Shapefile containing the Points of Diversion for the entire state of Idaho. IdahoRights_PlaceOfUse: A Shapefile containing the Place of Use polygons for the entire state of Idaho. MontanaRights_WaterRights: A Geodatabase file containing the Points of Diversion and Places of Use for the entire state of Montana. The name of the Points of Diversion Feature Layer within the Geodatabase is "WRDIV", and the name of the Places of Use Feature Layer is "WRPOU". NevadaRights_POD_Sites: A Shapefile containing the Points of Diversion for the entire state of Nevada. NewMexicoRights_Points_of_Diversion: A Shapefile containing the Points of Diversion for the entire state of New Mexico. OregonRights_state_shp: A folder containing 36 Shapefiles and are split between "pod" (Point of Diversion) and "pou" (Place of Use) for each water management basin within Oregon. In other words, each basin has one "pod" file and one "pou" file. The "pod" files are point shapes, and the "pou" files are polygons. UtahRights_Points_of_Diversion: A Shapefile containing the Points of Diversion for the entire state of Utah. WashingtonRights_WaterDiversions_ECY_NHD: A Geodatabase file containing both the Points of Diversion for the entire state of Washington. The name of the Feature Layer within the Geodatabase is "WaterDiversions_ECY_NHD". WyomingRights: A folder containing four subdirectories, one for each Wyoming Water Division. Each Division directory includes a varying number of subdirectories for each Wyoming Water District. Each District folder contains two copies of the Point of Diversion records for that area, with one copying being in CSV and one copy in Microsoft Excel XLS format.

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration's National Marine Sanctuary Program, has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region since 1993. The area is approximately 3,700 square kilometers (km2) and is subdivided into 18 quadrangles. Seven maps, at a scale of 1:25,000, of quadrangle 6 (211 km2) depict seabed topography, backscatter, ruggedness, geology, substrate mobility, mud content, and areas dominated by fine-grained or coarse-grained sand. Interpretations of bathymetric and seabed backscatter imagery, photographs, video, and grain-size analyses were used to create the geology-based maps. In all, data from 420 stations were analyzed, including sediment samples from 325 locations. The seabed geology map shows the distribution of 10 substrate types ranging from boulder ridges to immobile, muddy sand to mobile, rippled sand. Substrate types are defined on the basis of sediment grain-size composition, surficial morphology, sediment layering, and the mobility or immobility of substrate surfaces. This map series is intended to portray the major geological elements (substrates, features, processes) of environments within quadrangle 6. Additionally, these maps will be the basis for the study of the ecological requirements of invertebrate and vertebrate species that utilize these substrates and guide seabed management in the region.

2024 General Election Voting Precincts County voting precincts are the geographic units established by county commissioners courts for the purpose of election administration. Precincts can be bounded by visible or nonvisible features. Council staff collect precinct boundary changes from county officials for each statewide primary and general election. Precincts24G.zip - 2024 general election (24G) voting precincts shapefile The precincts shapefile (.shp) is provided in a compressed file (.zip) format. Precincts24G_Districts.xlsx - Excel file with 24G precincts related to district plans for the 2024 elections The Excel file (.xlsx) relates 2024 general election voting precincts to congressional, state senate, state house, and State Board of Education districts. The file was created by converting each precinct polygon into a point location within the precinct and joining the points to district plans for the 2024 elections. The file contains the following fields: FIPS - Census County Code (txt) COUNTY - County Name (txt) PREC - Voting Precinct Name (txt) <--Note: This field is text PCTKEY - Unique Identifier (txt) PlanC2193 - Texas Congressional District (num) PlanH2316 - State House District (num) PlanS2168 - State Senate District (num) PlanE2106 - State Board of Education District (num) Previous vintages of collected precinct data from the 2020s are also available for download: Precincts24P.zip - 2024 primary election (24P) voting precincts shapefile Precincts24P_Districts.xlsx - Excel file with 24P precincts related to district plans for the 2024 elections Precincts22G.zip - 2022 general election (22G) voting precincts shapefile Precincts22G_Districts.xlsx - Excel file with 22G precincts related to district plans for the 2022 elections Precincts22P_20220518.zip - 2022 primary election (22P) voting precincts shapefile Precincts22P_Districts_20220518.xlsx - Excel file with 22P precincts related to district plans for the 2022 elections Precincts20G_2020.zip - 2020 general election (20G) voting precincts shapefile Precincts20G_Districts_2020.xlsx - Excel file with 20G precincts related to district plans for the 2020 elections The council's precinct collection should be used as a reference for determining the boundaries of county voting precincts. Please consult the appropriate county agency or county election official for additional information regarding voting precinct boundaries.

This dataset contains X-ray diffraction (XRD) data taken from wells and outcrops as part of the DOE GTO supported Utah FORGE project located near Roosevelt Hot Springs. It contains an Excel spreadsheet with the XRD data, a text file with sample site names, types, and locations in UTM, Zone 12, NAD83 coordinates, and a GIS shapefile of the sample locations with attributes.

This data set contains measurements of 228RA and 226Ra Radium Isotopes from the SBI Spring 2002 U.S. Coast Guard Cutter (USCGC) Healy Cruise (HLY-02-01). Data are provided as an Excel spreadsheet.

The LINE shapefile describes the navigation conducted from a boat during the DOKE Focal survey carried out in Nov-Dec 2016, Jan 2017, Jun-Jul 2017, Nov-Dec2017, Feb-March 2018 and Jun-Jul 2018 in three areas of the Falklands: A=Stanley Harbour, Port William, Berekely Sound; B=Choiseul Sound; C=Port Howard, Many Branch. The shapefile was generated from the excel summary file named 'DOKE_Focal_Survey_Summary_final'.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Contains a line shapefile and an excel spreadsheet lookup table. The line shapefile derived from the Geofabric Surface Water Network streams (source data) and spatially represents the reaches described in the accompanying lookup table so that surface water modelling based Hydrological Response Variables (HRVs) can be applied spatially along stream lengths. The interpolation is also applied to the Geofabric Cartographic Streams for mapping purposes. ## Dataset History Line segments from the Geofabric Network Streams and Cartogrpahic Streams source dataset were grouped into reach sections based on descriptions in the accompanying lookup table (supplied by Surface Water Modelling team) using the surface water node locations and described river junctions and other defined locations as spatial reference. Reaches were given a unique ID and dissolved into a multpart lines. There is also a Network version which further breaks the reaches into their Riverine Landscape classes. ## Dataset Citation Bioregional Assessment Programme (2017) HUN SW Modelling Reaches and HRV lookup 20170221 v02. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/af0272b4-3a44-44ad-adba-5345a2b15f41. ## Dataset Ancestors * Derived From HUN SW Model nodes 20170110 * Derived From HUN AWRA-L simulation nodes_v01 * Derived From National Surface Water sites Hydstra * Derived From Geofabric Surface Network - V2.1 * Derived From HUN AWRA-L simulation nodes v02 * Derived From HUN River Perenniality v01 * Derived From Geofabric Surface Network - V2.1.1