This site provides free access to Iowa geographic map data, including aerial photography, orthophotos, elevation maps, and historical maps. The data is available through an on-line map viewer and through Web Map Service (WMS) connections for GIS. The site was developed by the Iowa State University Geographic Information Systems Support and Research Facility in cooperation with the Iowa Department of Natural Resources, the USDA Natural Resources Conservation Service, and the Massachusetts Institute of Technology. This site was first launched in March 1999.

The eAtlas delivers its mapping products via two Web Mapping Services, a legacy server (from 2008-2011) and a newer primary server (2011+) to which all new content it added. This record describes the legacy WMS.

This service delivers map layers associated with the eAtlas project (http://eatlas.org.au), which contains map layers of environmental research focusing on the Great Barrier Reef. The majority of the layers corresponding to Glenn De'ath's interpolated maps of the GBR developed under the MTSRF program (2008-2010).

This web map service is predominantly maintained for the legacy eAtlas map viewer (http://maps.eatlas.org.au/geoserver/www/map.html). All the these legacy map layers are available through the new eAtlas mapping portal (http://maps.eatlas.org.au), however the legends have not been ported across.

This WMS is implemented using GeoServer version 1.7 software hosted on a server at the Australian Institute of Marine Science.

For ArcMap use the following steps to add this service: 1. "Add Data" then choose GIS Servers from the "Look in" drop down. 2. Click "Add WMS Server" then set the URL to "http://maps.eatlas.org.au/geoserver/wms?"

Note: this service has around 460 layers of which approximately half the layers correspond to Standard Error maps, which are WRONG (please ignore all *Std_Error layers.

This services is operated by the Australian Institute of Marine Science and co-funded by the MTSRF program.

All of the ERS mapping applications, such as the Food Environment Atlas and the Food Access Research Atlas, use map services developed and hosted by ERS as the source for their map content. These map services are open and freely available for use outside of the ERS map applications. Developers can include ERS maps in applications through the use of the map service REST API, and desktop GIS users can use the maps by connecting to the map server directly.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: API access page For complete information, please visit https://data.gov.

The statewide dataset contains a combination of land cover mapping from 2016 aerial imagery and land use derived from standardized assessor parcel information for Massachusetts. The data layer is the result of a cooperative project between MassGIS and the National Oceanic and Atmospheric Administration’s (NOAA) Office of Coastal Management (OCM). Funding was provided by the Mass. Executive Office of Energy and Environmental Affairs.

This land cover/land use dataset does not conform to the classification schemes or polygon delineation of previous land use data from MassGIS (1951-1999; 2005).In this map service layer hosted at MassGIS' ArcGIS Server, all impervious polygons are symbolized by their generalized use code; all non-impervious land cover polygons are symbolized by their land cover category. The idea behind this method is to use both cover and use codes to provide a truer picture of how land is being used: parcel use codes may indicate allowed or assessed, not actual use; land cover alone (especially impervious) does not indicate actual use.

See the full datalayer description for more details.This map service is best displayed at large (zoomed in) scales. Also available are a Feature Service and a Tile Service (cache). The tile cache will display very quickly in in ArcGIS Online, ArcGIS Desktop, and other applications that can consume tile services.

Dataset generated for the "On server-side file access pattern matching" paper (Boito et al., HPCS 2019).

The traces were obtained following the methodology described in the paper. In addition to the two data sets discussed in the paper, we are also making available an extra data set of server traces.

Traces from I/O nodes

• IOnode_traces/output/commands has the list of commands used to generate them. Each test is identified by a label, and the test_info.csv file contains the mapping of labels to access patterns. Some files include information about experiments with 8 I/O nodes, but these were removed from the data set because they had some errors.
• IOnode_traces/output contains .map files that detail the mapping of clients to I/O nodes for each experiment, and .out files, which contain the output of the benchmark.
• IOnode_traces/ contains one folder per experiment. Inside this folder, there is one folder per I/O node, and inside these folders there are tracefiles for the read and write portions of the experiments. Due to a mistake during the integration between IOFSL and AGIOS, read requests appear as "W", and writes as "R". Once accounted for when processing the traces, that has no impact on results.
• pattern_length.csv contains the average pattern length for each experiment and operation (average number of requests per second), obtained with the get_pattern_length.py script.

Each line of a trace looks like this:

277004729325 00000000eaffffffffffff1f729db77200000000000000000000000000000000 W 0 262144

The first number is an internal timestamp in nanoseconds, the second value is the file handle, and the third is the type of the request (inverted, "W" for reads and "R" for writes). The last two numbers give the request offset and size in bytes, respectively.

Traces from parallel file sytem data servers

These traces are inside the server_traces/ folder. Each experiment has two concurrent applications, "app1" and "app2", and its traces are inside a folder named accordingly:

NOOP\_app1\_(identification of app1)\_app2\_(identification of app2)\_(repetition)\_pvfstrace/

Each application is identified by:

(contig/noncontig)\_(number and size of requests per process)\_(number of processes)\_(number of client machines)\_(nto1/nton regarding the number of files)

Inside each folder there are eight trace files, two per data server, one for the read portion and another for the write portion. Each line looks like this:

[D 02:54:58.386900] REQ SCHED SCHEDULING, handle: 5764607523034231596, queue_element: 0x2a11360, type: 0, offset: 458752, len: 32768

The part between [] is a timestamp, "handle" gives the file handle, "type" is 0 for reads and 1 for writes, "offset" and "len" (length) are in bytes.

• server_traces/pattern_length.csv contains the average pattern length for each experiment and operation, obtained with the server_traces/count_pattern_length.py script.

Extra traces from data servers

These traces were not used for the paper because we do not have performance measurements for them with different scheduling policies, so it would not be possible to estimate the results of using the pattern matching approach to select scheduling policies. Still, we share them in the extra_server_traces/ folder in the hope they will be useful. They were obtained in the same experimental campaign than the other data server traces, and have the same format. The difference is that these traces are for single-application scenarios.

The Geographic Names Information System (GNIS) actively seeks data from and partnerships with Government agencies at all levels and other interested organizations. The GNIS is the Federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types. See http://geonames.usgs.gov for additional information.

The eAtlas delivers its NetCDF (Network Common Data Form) data file using a THREDDS Data Server (TDS), which provides metadata and data access using OPeNDAP, OGC WMS and WCS, HTTP, and other remote data access protocols.

The services delivers data, matadata and map layers associated with the eAtlas project (https://eatlas.org.au). The data focus on environmental research on the Great Barrier Reef and its neighbouring coast, the Wet Tropics rainforests and Torres Strait. The map layers served by the WMS service can be used to create environmental maps when combine with other dataset such as the layers provided by the eAtlas Web Mapping Service (https://eatlas.org.au/data/uuid/71127e4d-9f14-4c57-9845-1dce0b541d8d).

This web map service is predominantly set up and maintained for delivery of visualisations through the eAtlas mapping portal (https://maps.eatlas.org.au). Other portals are free to use this service with attribution, provided you inform us with an email so we can let you know of any changes to the service.

This THREDDS Data Server software is hosted on a Amazon Web Services (AWS) server. Associated with each NetCDF dataset, there is a list of corresponding WMS layers, one for each data variable, which can be discovered by inspecting the corresponding GetCapabilities document. This XML document lists all the layers, a short descriptions and available rendering styles. Most WMS clients should be able to read those documents allowing easy access to all the variables for a given NetCDF dataset.

This services is operated by the Australian Institute of Marine Science and co-funded by the National Environmental Science Program Tropical Water Quality (NESP TWQ) Hub.

This map service is a one-stop location to view and explore Kentucky geologic map data and related-data (geologic outcrops, photos, and diagrams), Kentucky water wells and springs, Kentucky oil and gas wells. All features are provided by the Kentucky Geological Survey via ArcGIS Server services. This map service displays the 1:500,000-scale geologic map of Kentucky at scales smaller than 1:100,000, and 1:24,000-scale geological quadrangle data at larger scales. The 1:500,000-scale geologic map data were derived from the 1988 Geologic Map of Kentucky, which was compiled by Martin C. Noger (KGS) from the 1981 Geologic Map of Kentucky (Scale 1:250,000) by McDowell and others (USGS). The 1:24,000-scale geologic map data and the fault data were compiled from 707 Geological Survey 7.5-minute geologic quadrangle maps, which were digitized during the Kentucky Geological Survey Digital Mapping Program (1996-2006).The basemap data is provided via ArcGIS Server services hosted by the Kentucky Office of Geographic Information.Some tools are provided to help explore the map data:- Query tool: use this tool to search on the KGS database of lithologic descriptions. Most descriptions are derived from the 707 1:24,000 geological quadrangle maps. Once a search is completed, every unit that contains the search parameters is highlighted on the map service.- ID tools: users can identify and get detailed info on geologic units and other map features using either the point, area, or buffer identification tools.A few notes on this service:- the legend is dynamic for the viewed extent. It is provided via a database call using the current map extent.- the oil and gas and water wells are ArcGIS Server services that update dynamically from the KGS database.- the geologic map and faults are dynamic ArcGIS Server map services.- the user can link to other geologic data for the viewed extent using the links provided in the "Geologic Info" tab.- you can query the entire KGS lithologic description database and highlight the relevant geologic units based on the query.

World framework data from Intergraph Corporation

This GIS dataset was prepared by the Suffolk County Department of Economic Development and Planning for the Suffolk County Department of Health Services as part of the Suffolk County Subwatersheds Wastewater Plan titled Suffolk County Management Program for the Reduction of Nitrogen Loading from Wastewater Sources. The Department of Economic Development & Planning was asked to provide “parcel-specific buildout land use” for all of Suffolk County. In order to provide parcel-specific buildout land use, the land use for each and every distinct tax map parcel in the Suffolk County Real Property Tax Service Agency Tax Map Album had to be checked and updated. Following this task, parcel-specific land available for residential development was determined, and then residential buildout was calculated.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Ash Can dataset.

SOMIS v2.0 allows navigating the European Soil Database; is an updated version of the the SOMIS v1.0. This map server publishes all the maps derived from attributes which are present in the Soil Geographical Database of Eurasia at scale 1:1,000,000 (version 4 beta) and the PedoTransfer Rules Data Base (version 2.0). These two databases are components of the European Soil Data Base (distribution version v2.0). It allows users to navigate the European Soils. All maps are dominant value maps

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.

Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.

Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.html

Data from the Department of License and Inspections, Planning, Parks and Recreation, Heath, etc, can be viewed on this interactive map. Preset maps can be selected containing datasets from the following categories: Bike Network, Business, Construction, Gun Permit Appeals, Rental Licenses, Vacancy, Violations, and Zoning. Users can also overlay districts on the map, such as Census Tracts or Sanitation Districts. Additional datasets will be added over time. API access is available through the ArcGIS Server Map Services API interface. Trouble downloading or have questions about this City dataset? Visit the OpenDataPhilly Discussion Group

This data set provides maps of snow cover and sea ice for the Northern Hemisphere from February 1997 to the present from the United States National Ice Center (USNIC). USNIC analysts produce these using the Interactive Multisensor Snow and Ice Mapping System (IMS). Maps are derived from a variety of data products including satellite imagery and in situ data. The data are provided in ASCII text, GeoTIFF, and NetCDF formats in three different resolutions: 1 km, 4 km, and 24 km.Note: The IMS product is considered an operational product; however, USNIC, who creates this product, does not guarantee availability or timely delivery of data via the NIC Web server. NSIDC, as the data archive, does not guarantee availability of this product via the NSIDC Web server. These servers should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. Users with real-time operational needs should visit the USNIC Web site and contact the USNIC Liaison to request access to their operational server.

The map extension for CKAN facilitates the creation of interactive map visualizations for datasets containing geospatial information. It achieves this by utilizing server-generated tiles optimized for handling large datasets. This approach allows users to effectively visualize and explore datasets with millions of data points directly within CKAN. Note that this extension depends on ckanext-dataspatial for creating geospatial columns and enabling spatial query functionality. However it is important to note that this extension is no longer actively maintained. Key Features: Tiled Map Views: Enables the creation of map views based on server-generated tiles, optimized for visualizing large datasets. Configuration Options: Offers a range of configurable options to customize the appearance and behavior of map views, including tile server host and port, tile layer URL and opacity, initial zoom levels, and default styling options. Styling for Plot Markers: Allows customization of plot marker styles, including fill color, line color, and marker size. Note that marker size has performance implications and cannot be overridden per-view. Gridded Views Support: Provides styling options for gridded views, including base color, marker size, and grid resolution. Note that marker size and grid resolution have performance implications and cannot be overridden per-view. Heatmap Visualization: Enables the creation of heatmap visualizations with configurable intensify and gradient colors. Automatic Geometry Column Creation: Automatically creates and populates geometry columns in the dataset as required when users select latitude and longitude fields. This simplifies the process of preparing data for map visualizations. Technical Integration: The map extension integrates with CKAN by adding a plugin. Once enabled, users can add tiled map views from the resource management page. The plugin automatically creates and populates geometry columns as required, simplifying the process of setting up geospatial visualizations. Configuration options can be adjusted through the CKAN .ini file. It interfaces with a separate tile server (downloadable from a specified URL) to generate and serve map tiles. Benefits & Impact: The map extension provides a means to render large databases visually and interactively that would otherwise be exceedingly difficult. By offloading tile generation to a separate server, the extension bypasses client-side performance limitations typical with raw point data. This permits complex geospatial data to be rendered within the context of CKAN without the resource limitations of less efficient rendering approaches.

The Barrow Area Information Database (BAID) data collection is comprised of geospatial data for the research hubs of Barrow, Atqasuk and Ivotuk on Alaska's North Slope. Over 9600 research plots and instrument locations are included in the BAID research sites database. Updates to the project tracking database are ongoing through field mapping of new research locations and extant sampling sites dating back to the 1940s. Many ancillary data layers are also compiled to facilitate research activities and science communication. These geospatial data sets have been compiled through BAID and related NSF efforts. Geospatial data unique to this project are currently browseable via the BAID archive and include shapefiles of research information (sampling sites and instrumentation, the NOAA-CMDL clean air sector), administrative units (Barrow Environmental Observatory Science Research District plus adjacent federal lands, village districts, zoning, tax parcels, and the Ukpeagvik Inupiat Corporation boundary), infrastructure (power poles, snow fences, roads), erosion data for Elson Lagoon and imagery (declassified military imagery, air photo mosaics, IKONOS, Landsat, Quickbird, SAR and flight line indexes). Related data sets can be browsed via BAID’s web mapping tools and downloaded via the “Related links” section below. In addition, the BAID Internet Map Server (BAID-IMS) provides browse access to a number of additional layers which are available for download through catalog pages at the National Snow and Ice Data Center (NSIDC), the Alaska Geospatial Data Clearinghouse at USGS and the Alaska State Geo-Spatial Data Clearinghouse. Some layers are proprietary and are only available for browse access in BAID-IMS through special agreement. BAID provides a suite of user interfaces (Internet Map Server, Google Earth and Adobe Flex) and Open Geospatial Consortium web services for accessing the research plots and instrument locations. For more information on...

Report of zoning of enforceable urban planning documents. The data contains the regulatory zoning digitised from the parcelary Bd of the IGN© or the express PCi (based on the geometric elements of the Bd Parcellaire or the express PCi for communal boundaries). The data awarded contain for each digitalised area the name of the zoning in the document and a simplification in 5 levels of aggregation N natural area Agricultural area U urbanised area AUC area to urbanise AUC urbanised area blocked For any application of a regulatory nature, refer to the original documents. This data conforms to the CNIG 2017 standard (http://cnig.gouv.fr/?page_id=2732) in its structure and incorporates data from the POS generalisation table (N_ZONAGE_PLU_S_034) of DDTM 34, developed internally since 1998. This table can be used as a graphical medium for digitalising the zoning of future documents that will need to integrate the Urbanism geoportal. Wms server of the GPU: http://wxs-gpu.mongeoportail.ign.fr/externe/vkd1evhid6jdj5h4hkhyzjto/wms/v?request=GetCapabilities GPU wfs server: http://wxs-gpu.mongeoportail.ign.fr/externe/39wtxmgtn23okfbbs1al2lz3/wfs

The list of study sites, meteorological stations and locations of interest that are shown on the Bonanza Creek Long Term Ecological Research site (BNZ LTER) internet map server (IMS, available at http://www.lter.uaf.edu/ims_intro.cfm) is generated from the LTER study sites database. The information is converted into a shapefile and posted to the IMS. Some study sites shown on the main LTER website will not appear on the IMS because they do not have location coordinates. In all cases the most up-to-date information will be found on the (study sites website ).

The spatial information represented on the IMS is available to the public according to the restrictions outlined in the LTER data policy. The dataset represented here consists of the map layers shown on the IMS. The information consists of shapefiles in Environmental Systems Research Institute (ESRI) format. Users of this dataset should be aware that the contents are dynamic. Portions of the information shown on the IMS are derived from the Bonanza Creek LTER databank and are constantly being updated.

This dataset contains WebMercator tiles which contain gray-scale shaded relief (hill shades), and nothing else. The tiles have a resolution of 256×256px, suitable for web mapping libraries such as Leaflet. The hill shades are generated from SRTM altitude data, which cover the land area between 60° northern and 58° southern latitude, and which lies in the public domain. Map material without political or infrastructural features can be desirable, for example, in use cases where historical data is visualized on a map. The concrete motivation for generating this map material was the Dhimmis & Muslims project (project page, home page, GitHub, DaRUS dataset), which analyzed peaceful coexistence of religious groups in the medieval Middle East. A particular goal with creating the dataset was to have map material available under a permissive license for screenshots and publications, instead of relying on proprietary mapping services such as Mapbox. Teaser image: The hillshades of Cyprus on zoom level 9. This image is hosted externally by GitHub, but is also present in the repository as teaser.png. Coverage. The dataset covers zoom level 0 (entire world in one tile) to 12 (entire world in 4096×4096 tiles). The total size of the dataset is 22,369,621 tiles. However, of those, 19,753,304 tiles (88.3%) are empty, either because the landscape there is fully flat (i.e., on water), or because they lie fully outside the latitude range covered by the SRTM altitude data. The empty tiles are not stored. Instead, a singular placeholder file is stored in the repository, alongside a list of the empty tiles. During extraction, the placeholder empty tile can be symbolically linked in the file system to all the places where it is needed. The total size of the non-empty tiles is about 103GB. Files. Besides the placeholder file and the list of empty tiles, the repository also contains a manifest file. This file lists all non-empty tiles by the ZIP file they are contained in. The tiles themselves are grouped into ZIP files by the following schema: All tiles from levels 0 to 5 are contained in one ZIP file. All tiles of level N, N≥6 are contained in a ZIP file which is named after the tile of level N-6 (block level) that contains the tile in question, named tiles_.zip. Hence, all tiles of level 6 are contained in a singular ZIP file named tiles_6_0_0_0.zip. The tiles of level 7 are split up into four group ZIP files named tiles_7_1_{0,1}_{0,1}.zip, the tiles of level 8 into 16 group ZIP files named tiles_8_2_{0..3}_{0..3}.zip, and so on. Both the manifest file and the commands to generate the distribution of tiles on ZIP files can be generated using the linked software repository. Usage. The tile ZIP files can be downloaded and extracted. By serving the extracted directory structure in a web server, a slippy map tile server can be created. The linked software repository also contains a command-line utility that generates the required shell commands to download the ZIP files, extract them, and softlink (ln -s) the empty tiles to the appropriate places. This command-line utility can also optionally read in a GeoJSON file of an area of interest. In this case, only tiles within that area are downloaded in a higher zoom level, whereas tiles completely outside the area are only downloaded to a lower zoom level; both zoom levels are also configurable. See the documentation in the repository and the command-line utility’s help (-h) output for more details.