This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

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.

No description is available. Visit https://dataone.org/datasets/d948323297fcd717616aed6cfcffa18b for complete metadata about this dataset.

This dataset contains a shapefile and an excel document. The shapefile data includes the spatial data of ChengYu Urban Agglomeration and its attributes such as population, GDP, transportation, education, and public health. The excel document contains the traffic cost between cities for different modes of travel, such as expressway, high-speed railway, and general speed railway.

2024-2025 School Year Council staff collect school district boundary updates from the Texas Education Agency and county central appraisal districts for each school year. The school district shapefile should be used as a reference for determining the boundaries of school districts. The depiction and designation of the school district boundaries do not constitute a determination of jurisdictional authority or rights of ownership or entitlement, and they are not legal land descriptions. Please consult the appropriate county central appraisal district for additional information on school district boundaries. SchoolDistricts_SY2425.zip - 2024-2025 school year districts shapefile The school districts shapefile (.shp) is in a compressed file (.zip) format. RED635_SchoolDistrict_Population_SY2425 - Report of 2020 Census population by 2024-2025 school year districts The RED635 report is provided in PDF and Excel formats. Note: The 2024-2025 School Year school districts in the council's geographic file are not the same as the districts in the Census Bureau's 2020 TIGER/Line Shapefile. School district population data published by the Texas Legislative Council using the 2024-2025 School Year school districts will not correspond with the school district population data published by the Census Bureau.

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 is an Excel spreadsheet containing X-ray diffraction data from the Utah FORGE project. Please download the accompanying text file and/or GIS shapefile for location/sample site information.

The polygon dataset represents predicted indoor radon screening levels in counties across the United States. These data were provided by EPA’s Office of Radiation and Indoor Air as an Excel spreadsheet. In order to produce the Web mapping application, the Excel file was joined with a shapefile of U.S. county boundaries downloaded from the U.S. Census Bureau. Those two sets of data were then converted into a single polygon feature class inside a file geodatabase.

A GPS survey by Andrew Ruddell (AAD Glaciology) on 9 March 1999 at Casey Station, Antarctica. The survey was conducted along the road from Casey Station to Old Casey. The aim of the survey was to investigate the cause of the 'disappearance' of road gravel applied to the compacted snow road in the depression between Casey Station to Old Casey. This dataset consists of point data with an elevation (above mean sea level) attribute. The data, in Excel and shapefile formats, and Andrew's report are available for download (see Related URL below).

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is 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. The dataset consists of an excel spreadsheet and shapefile representing the locations of simulation nodes used in the AWRA-R model. Some of the nodes correspond to gauging station locations or dam locations whereas other locations represent river confluences or catchment outlets which have no gauging. These are marked as "Dummy". ## Purpose Locations are used as pour points in oder to define reach areas for river system modelling. ## Dataset History Subset of data for the Hunter that was extracted from the Bureau of Meteorology's hydstra system and includes all gauges where data has been received from the lead water agency of each jurisdiction. Simulation nodes were added in locations in which the model will provide simulated streamflow. There are 3 files that have been extracted from the Hydstra database to aid in identifying sites in each bioregion and the type of data collected from each on. These data were used to determine the simulation node locations where model outputs were generated. The 3 files contained within the source dataset used for this determination are: Site - lists all sites available in Hydstra from data providers. The data provider is listed in the #Station as _xxx. For example, sites in NSW are _77, QLD are _66. Some sites do not have locational information and will not be able to be plotted. Period - the period table lists all the variables that are recorded at each site and the period of record. Variable - the variable table shows variable codes and names which can be linked to the period table. Relevant location information and other data were extracted to construct the spreadsheet and shapefile within this dataset. ## Dataset Citation Bioregional Assessment Programme (XXXX) HUN AWRA-R simulation nodes v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/fda20928-d486-49d2-b362-e860c1918b06. ## Dataset Ancestors * Derived From National Surface Water sites Hydstra

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).

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

The SMARTDEST DATASET WP3 v1.0 includes data at sub-city level for 7 cities: Amsterdam, Barcelona, Edinburgh, Lisbon, Ljubljana, Turin, and Venice. It is made up of information extracted from public sources at the local level (mostly, city council open data portals) or volunteered geographic information, that is, geospatial content generated by non-professionals using mapping systems available on the Internet (e.g., Geofabrik). Details on data sources and variables are included in a ‘metadata’ spreadsheet in the excel file. The same excel file contains 5 additional spreadsheets. The first one, labelled #1, was used to perform the analysis on the determinants of the geographical spread of tourism supply in SMARTDEST case study’s cities (in the main document D3.3, section 4.1), The second one (labelled #2) offers information that would allow to replicate the analysis on tourism-led population decline reported in section 4.3. As for spreadsheets named #3-AMS, #4-BCN, and #5-EDI, they refer to data sources and variables used to run follow-up analyses discussed in section 5.1, with the objective of digging into the causes of depopulation in Amsterdam, Barcelona, and Edinburgh, respectively. The column ‘row’ can be used to merge the excel file with the shapefile ‘db_task3.3_SmartDest’. Data are available at the buurt level in Amsterdam (an administrative unit roughly corresponding to a neighbourhood), census tract level in Barcelona and Ljubljana, for data zones in Edinburgh, statistical zones in Turin, and località in Venice.

The dataset includes an Excel workbook and a shapefile. The Excel workbook has two spreadsheets. The first spreadsheet contains the final stage/volume/area table for the area of interest in Malheur Lake. The second spreadsheet contains calculations to create the stage/volume/area table. The shapefile is a polygon representing the area of interested in Malheur Lake,OR. The area of interest was manually delineated to include the annually-inundated extent and the directly adjacent lake riparian areas.

This shapefile contains ground solar installations (no rooftop solar) for the year 2021. The NLCD 2019 data layer was used to determine the land cover class for the areas the solar fields replaced.

The dataset, presented as a MS Excel file and an ArcGIS Shapefile, includes a wildfire risk index and its components and subcomponents, as well as the results of a clustering process based on the main components of the wildfire risk index. The analysis units are the civil parishes comprised within the NUTS2 Centro territorial unit in central Portugal, identified by name in the column ParishName of the Excel file.

All variables are identified in the Excel file Data, and all are included as attributes to the parish polygons in the shapefile.

The aim of the NPWS Seabird Foraging Radii Project was to create a number of polygon shapefiles describing the geographic foraging range of 20 seabird species identified by the National Parks and Wildlife Service Birds unit. The polygons were centred around Natura 2000 SPA (Special Protection Areas) centroids created by the Birds Unit. The foraging radii polygons were created to represent mean, mean-max and maximum foraging range journeys undertaken by the bird species of interest. The foraging range polygon shapefiles created during the process to geographically describe seabird foraging activity were merged into a single polygon shapefile.. This Project generated following datasets: An Excel spreadhseet 1 point shapefile representing the centroid location of Special Protected Area's (SPA) for all seabird species listed in the SBFR21_Foraging_range.xls One polygon shapefile representing the geographic extent of seabird foraging radii at mean, mean-max and maximum extent. The foraging radii were applied to centroids within the SPA network where a given seabird species was listed. Several species of seabird have foraging ranges outside Irish territorial limits.

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 stored in a jar. Dried sub-samples samples were analyzed for grain size. First, the samples were treated with hydroperoxide to remove organic components. Then, the sample was passed through a series of standard sieves representing Phi sizes with the smallest being 64 µm. The content of each sieve was dried and weighed. If there was sufficient fine material (< 64 µm), this fine fraction was further analyzed using a Sedigraph system. The results of sieving and sedigraph analysis have been combined and the percentages for gravel, sand, silt and clay were determined following the Wentworth scale. In addition, other statistics including mean, median, skewness and standard deviation are calculated using the USGS GSSTAT program. The results of the LDEO/Queens College grain size analysis have been combined with data collected by the LISMARC group and analyzed by USGS. Based on the different percentages, sediment classifications are determined following the Schlee and the Falk systems. These results are also translated into the CMACS systems. 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 contains groundwater geochemistry from several wells in North Milford Valley, Utah, in the region of the Utah FORGE project (Phase 2c). Readme file that discusses the data contained in the Excel spreadsheet. Data include GPS coordinates (UTM, Lat-Long), sampling temperature, pH, Li, Na, K, Ca, Mg, SiO2, B, Cl, F, SO4, HCO3, oxygen, and hydrogen isotopes. Analyses were performed at the Utah State Laboratory and the University of Minnesota.

The zipped archive includes Excel and csv format spreadsheets, a shapefile map with well locations, and a readme text file with additional information. The zip is updated data from October 2021.

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.

City of New Orleans commercial and residential addresses. ESRI shapefile format, GIS software required for viewing geometry (http://www.esri.com/software/arcgis/arcreader/). Tabular data (DBF), included as part of the shapefile, are viewable in Excel. Scheduled update: weekly