Topographic sheets (t-sheets) produced by the National Ocean Service (NOS) during the 1800s provide the position of past shorelines. The shoreline data can be vectorized into a geographic information system (GIS) and compared to modern shoreline data to calculate estimates of long-term shoreline rates of change. Many t-sheets were scanned and digitized by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline website (https://shoreline.noaa.gov/data/datasheets/t-sheets.html). However, some t-sheets were not scanned by NOAA and are only available via the National Archives and Records Administration (NARA). The data included within this data release were previously unavailable or not published in digital format. These data were produced to provide a more comprehensive record of shoreline position for Fire Island and Great South Bay, New York, to aid geologic and coastal hazards studies. This data release includes previously unavailable georeferenced t-sheets and digital vector shorelines for the Fire Island and Great South Bay, New York, coastline from 1834, 1838, and 1874/1875. The original t-sheets were scanned by the NARA-authorized vendor and sent to the Unites States Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) as non-georeferenced digital raster files. Upon arrival at the SPCMSC, USGS staff performed the following procedures: rasters were georeferenced, projected to a modern datum, and shorelines were digitized to create a vector polyline depicting the historical shoreline position. The t-sheets included in this data release are: 1) T-479a, T-479b, T-1 (Parts 2 and 3) (1834); 2) T-58 (Parts 1 and 2) (1838); 3) T-1374a, T-1374b, T-1375a, T-1375b (1874); and 4) T-1402 (1875). All shorelines, including the ocean-facing barrier island shoreline, back-barrier island shoreline, mainland and islands were digitized. Please read the full metadata for details on data collection, dataset variables, and data quality.

The Department of City Planning aggregates information about 30,000+ facilities and program sites that are owned, operated, funded, licensed, or certified by a City, State, or Federal agency in the City of New York into a central database called the City Planning Facilities Database (FacDB). These facilities generally help to shape quality of life in the city’s neighborhoods, and this dataset is the basis for a series of planning activities. This public data resource allows all New Yorkers to understand the breadth of government resources in their neighborhoods. The data is also complemented with a new interactive web map that enables users to easily filter the data for their needs. Users are strongly encouraged to read the database documentation, particularly with regard to analytical limitations. Questions about this database can be directed to dcpopendata@planning.nyc.gov All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Analysis of ‘Facilities Database - Shapefile’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a0709aa5-f873-45b3-87f1-fdb65d4ddf0d on 13 February 2022.

--- Dataset description provided by original source is as follows ---

The City Planning Facilities Database (FacDB) aggregates information about 35,000+ public and private facilities and program sites that are owned, operated, funded, licensed or certified by a City, State, or Federal agency in the City of New York. It captures facilities that generally help to shape quality of life in the city’s neighborhoods, including schools, day cares, parks, libraries, public safety services, youth programs, community centers, health clinics, workforce development programs, transitional housing, and solid waste and transportation infrastructure sites. To facilitate analysis and mapping, the data is available in coma-separated values (CSV) file format, ESRI Shapefile, and GeoJSon. The data is also complemented with a new interactive web map that enables users to easily filter the data for their needs. Users are strongly encouraged to read the database documentation, particularly with regard to analytical limitations.

For data dictionary, please follow this link

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

--- Original source retains full ownership of the source dataset ---

Shapefiles for Ethiopia's Administrative boundaries: Regions, Zones and Woredas

What is the HyM_GR2M product?It's a product that contains monthly discharge estimations for 3594 subbasins and river reaches throughout the Peruvian territory, from January 1981 to March 2020. Discharge data is generated from a water balance model at a national scale, forced by the meteorological PISCO dataset, and using a semi-distributed GR2M model adaptation.How to read data?Shapefiles of subbasins and river reaches are provided. Each shapefile's attribute table has a field named GR2M_ID with a unique identifying number for each element, so discharge time-series could be easily assigned. Additionally and R script is attached to read the discharge netCDF file.Files- discharge.nc- Read_discharge.R- Subbasins_HyM_GR2M (shapefile)- Streams_HyM_GR2M (shapefile)

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous

Geoscape Administrative Boundaries is Australia’s most comprehensive national collection of boundaries, including government, statistical and electoral boundaries. It is built and maintained by Geoscape Australia using authoritative government data. Further information about contributors to Administrative Boundaries is available here.

This dataset comprises seven Geoscape products:

• Localities
• Local Government Areas (LGAs)
• Wards
• Australian Bureau of Statistics (ABS) Boundaries
• Electoral Boundaries
• State Boundaries and
• Town Points

Updated versions of Administrative Boundaries are published on a quarterly basis.

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Notable changes in the May 2025 release

• Victorian Wards have seen almost half of the dataset change now reflecting the boundaries from the 2024 subdivision review. https://www.vec.vic.gov.au/electoral-boundaries/council-reviews/ subdivision-reviews.

  • There have been spatial changes (area) greater than 1 km2 to 66 wards in Victoria.

• One new locality ‘Kenwick Island’ has been added to the local Government area ‘Mackay Regional’ in Queensland.

  • There have been spatial changes(area) greater than 1 km2 to the local government areas 'Burke Shire' and 'Mount Isa City' in Queensland.

• There have been spatial changes(area) greater than 1 km2 to the localities ‘Nicholson’, ‘Lawn Hill’ and ‘Coral Sea’ in Queensland and ‘Calguna’, ‘Israelite Bay’ and ‘Balladonia’ in Western Australia.

• An update to the NT Commonwealth Electoral Boundaries has been applied to reflect the redistribution of the boundaries gazetted on 4 March 2025.

• Geoscape has become aware that the DATE_CREATED and DATE_RETIRED attributes in the commonwealth_electoral_polygon MapInfo TAB tables were incorrectly ordered and did not match the product data model. These attributes have been re-ordered to match the data model for the May 2025 release.

IMPORTANT NOTE: correction of issues with the 22 November 2022 release

• On 28 November 2022, the Administrative Boundaries dataset originally released on 22 November 2022 was amended and re-uploaded after Geoscape identified some issues with the original data for 'Electoral Boundaries'.
• As a result of the error, some shapefiles were published in 3D rather than 2D, which may affect some users when importing data into GIS applications.
• The error affected the Electoral Boundaries dataset, specifically the Commonwealth boundary data for Victoria and Western Australia, including 'All States'.
• Only the ESRI Shapefile formats were affected (both GDA94 and GDA2020). The MapInfo TAB format was not affected.
• Because the datasets are zipped into a single file, once the error was fixed by Geoscape all of Administrative Boundaries shapefiles had to be re-uploaded, rather than only the affected files.
• If you downloaded either of the two Administrative Boundary ESRI Shapefiles between 22 November and 28 November 2022 and plan to use the Electoral Boundary component, you are advised to download the revised version dated 28 November 2022. Apologies for any inconvenience.

Further information on Administrative Boundaries, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on Administrative Boundaries, including software solutions, consultancy and support.

Note: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

The Australian Government has negotiated the release of Administrative Boundaries to the whole economy under an open CCBY 4.0 licence.

Users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International license (CC BY 4.0).

Preferred attribution for Adapted Material:

Incorporates or developed using Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International licence (CC BY 4.0).

What to Expect When You Download Administrative Boundaries

Administrative Boundaries is large dataset (around 1.5GB unpacked), made up of seven themes each containing multiple layers.

Users are advised to read the technical documentation including the product change notices and the individual product descriptions before downloading and using the product.

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous

License Information

New political and administrative boundaries Shapefile of Nepal. Downloaded and republished from the Survey Department website.

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.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

Building footprint polygons are updated weekly by ECGIS. They provide a general reference of where buildings in Eaton County are located. These are not survey-grade.

Shapefiles of canopy disturbances for the 50-ha Smithsonian ForestGEO plot on Barro Colorado Island, Panama, for 46 successive time intervals (47 dates) between 2 October 2014 and 28 November 2019. We defined a canopy disturbance as a substantial decrease in canopy height in a contiguous patch of canopy occurring over one measurement interval. We identified canopy disturbances through a combination of analysis of the canopy surface model changes and visual interpretation of the orthomosaics. We first differenced surface elevation models for successive dates to obtain a raster of the canopy height changes for the associated interval. We then pre-delineated major canopy disturbances by filtering for areas in which canopy height decreased more than 10 m in contiguous areas of at least 25 m2, and that had an area-to-perimeter ratio greater than 0.6. We note that 25 m2 is the minimum gap area used in previous studies of this site by Brokaw (1982) and Hubbell et al. (1999). The area-to-perimeter condition removes artifacts associated with slight shifts in the measured positions of individual trees from one image set to another, whether due to wind or alignment errors (note that this criterion involves a combination of shape and size). Finally, we systematically examined 1-ha square subplots for each pair of successive dates and edited the pre- delineated polygons, removed false positives, and added visible new canopy disturbances that were not previously delineated (whether because they were too small in area or in canopy height drop). We also classified disturbances as being due to treefalls (a whole previously live tree fell, creating a clearly visible gap on the forest floor, or the whole live crown disappeared), branchfalls (a portion of a live crown broke), or standing dead trees disintegrating based on visual inspection of the orthomosaics. Before and after orthomosaic classifications are shown in Figure S2 of the associated Biogeosciences article by Araujo et al.

These data are licensed under CC BY, meaning use of the data is allowed so long as attribution is given via citation. These data should be cited either as an individual dataset or as part of the larger collection:

Araujo, Raquel F., Samuel Grubinger, Milton Garcia, Jonathan P. Dandois, and Helene C. Muller-Landau. 2021. Shapefiles of canopy disturbances for the 50-ha plot on Barro Colorado Island, Panama, for 2014-2019. Smithsonian Figshare. DOI:10.25573/data.14417915

or

Araujo, Raquel F., Samuel Grubinger, Milton Garcia, Jonathan P. Dandois, and Helene C. Muller-Landau. 2021. Collection of datasets: Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Smithsonian Figshare. DOI: 10.25573/data.c.5389043

These datasets were used in the following peer-reviewed journal article:

Araujo, R. F., S. Grubinger, C. H. S. Celes, R. I. Negrón-Juárez, M. Garcia, J. P. Dandois, and H. C. Muller-Landau. 2021. Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Biogeosciences.

The code used to analyze these data for this article are available in GitHub, at https://github.com/Raquel-Araujo/gap_dynamics_BCI50ha

Author contribution for datasets for 2014-2015: Helene C. Muller-Landau conceived the research, wrote the grant proposal that funded the research, and designed data collection. Jonathan Dandois constructed the drones, led drone data collection, performed photogrammetry processing, and did preliminary horizontal alignment. Samuel Grubinger finalized horizontal and vertical alignment and identified canopy disturbances. Raquel F. Araujo revised canopy disturbances and classified them as branchfalls, treefalls, or standing dead trees.

Author contribution for datasets for 2016-2019: Helene C. Muller-Landau conceived the research and designed the data collection. Milton Garcia led drone data collection and processed drone imagery. Raquel F. Araujo performed horizontal and vertical alignment, identified canopy disturbances, and classified disturbances as branchfalls, treefalls, or standing dead trees.

Acknowledgments: We thank Marino Ramirez, Pablo Ramos, Paulino Villareal and others for assistance with drone data collection; and Milton Solano for assistance with data processing and organization. We gratefully acknowledge the financial support of the Smithsonian Institution Competitive Grants Program for Science; the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; and the Smithsonian Tropical Research Institute fellowship program. Kristina Anderson-Teixeira, Steph... Visit https://dataone.org/datasets/urn%3Auuid%3Acb9ab343-82e7-46e3-a524-f4d34096a15e for complete metadata about this dataset.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Abstract

The dataset was compiled by the Bioregional Assessment Programme from multiple sources referenced within the dataset and/or metadata. The processes undertaken to compile this dataset are described in the History field in this metadata statement.The Gippsland Basin Bioregional Assessment groundwater model uses this extent to derive its data sets and to plot the outputs from the model. The shapefile is used to cut any inputs that are on a larger scale and to fit them into the groundwater model. The extent includes both onshore and offshore Gippsland Basin extents to incorporate offshore oil and gas industry extractions and the influence of these on onshore aquifers. This extent is the same as that used in the Victorian Onshore Natural Gas Water Science Studies undertaken to determine the potential impact of future exploration and extraction of oil and gas (conventional and non-conventional) on water resources in the Gippsland Basin.

Purpose

This shapefile is used to cut and present other datasets to the groundwater model extent.

Dataset History

This shapefile delineates the extent of the groundwater model for the Gippsland Basin Bioregional Assessment and is based on the same extent used for the Victorian onshore natural gas water science studies undertaken in 2015. This study required the development of a groundwater model for the Gippsland Basin (both on and offshore) to investigate and understand the potential impacts of future onshore gas developments and to understand the possible impacts of a potential onshore natural gas industry on groundwater and surface waters within the Gippsland region. The groundwater model (Beverley et al., 2015) was constructed in MODFLOW-2005 and adopted a uniform spatial resolution of 400m with a spatial extent of 6,698,000 ha, of which 3,629,000 ha exists onshore and 3,069,000 ha offshore. This model is available online under the Victorian onshore natural gas website.

Dataset Citation

Victorian Department of Environment, Land, Water and Planning (2015) Extent of Study Area GIP. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/4509d2d0-28b7-4775-a788-f120b26e64ec.

Global prevalence of non-perennial rivers and streamsJune 2021prepared by Mathis L. Messager (mathis.messager@mail.mcgill.ca)Bernhard Lehner (bernhard.lehner@mcgill.ca)1. Overview and background 2. Repository content3. Data format and projection4. License and citations4.1 License agreement4.2 Citations and acknowledgements1. Overview and backgroundThis documentation describes the data produced for the research article: Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5In this study, we developed a statistical Random Forest model to produce the first reach-scale estimate of the global distribution of non-perennial rivers and streams. For this purpose, we linked quality-checked observed streamflow data from 5,615 gauging stations (on 4,428 perennial and 1,187 non-perennial reaches) with 113 candidate environmental predictors available globally. Predictors included variables describing climate, physiography, land cover, soil, geology, and groundwater as well as estimates of long-term naturalised (i.e., without anthropogenic water use in the form of abstractions or impoundments) mean monthly and mean annual flow (MAF), derived from a global hydrological model (WaterGAP 2.2; Müller Schmied et al. 2014). Following model training and validation, we predicted the probability of flow intermittence for all river reaches in the RiverATLAS database (Linke et al. 2019), a digital representation of the global river network at high spatial resolution.The data repository includes two datasets resulting from this study:1. a geometric network of the global river system where each river segment is associated with:i. 113 hydro-environmental predictors used in model development and predictions, andii. the probability and class of flow intermittence predicted by the model.2. point locations of the 5,516 gauging stations used in model training/testing, where each station is associated with a line segment representing a reach in the river network, and a set of metadata.These datasets have been generated with source code located at messamat.github.io/globalirmap/.Note that, although several attributes initially included in RiverATLAS version 1.0 have been updated for this study, the dataset provided here is not an established new version of RiverATLAS. 2. Repository contentThe data repository has the following structure (for usage, see section 3. Data Format and Projection; GIRES stands for Global Intermittent Rivers and Ephemeral Streams):— GIRES_v10_gdb.zip/ : file geodatabase in ESRI® geodatabase format containing two feature classes (zipped) |——— GIRES_v10_rivers : river network lines |——— GIRES_v10_stations : points with streamflow summary statistics and metadata— GIRES_v10_shp.zip/ : directory containing ten shapefiles (zipped) Same content as GIRES_v10_gdb.zip for users that cannot read ESRI geodatabases (tiled by region due to size limitations). |——— GIRES_v10_rivers_af.shp : Africa |——— GIRES_v10_rivers_ar.shp : North American Arctic |——— GIRES_v10_rivers_as.shp : Asia |——— GIRES_v10_rivers_au.shp : Australasia|——— GIRES_v10_rivers_eu.shp : Europe|——— GIRES_v10_rivers_gr.shp : Greenland|——— GIRES_v10_rivers_na.shp : North America|——— GIRES_v10_rivers_sa.shp : South America|——— GIRES_v10_rivers_si.shp : Siberia|——— GIRES_v10_stations.shp : points with streamflow summary statistics and metadata— Other_technical_documentations.zip/ : directory containing three documentation files (zipped)|——— HydroATLAS_TechDoc_v10.pdf : documentation for river network framework|——— RiverATLAS_Catalog_v10.pdf : documentation for river network hydro-environmental attributes|——— Readme_GSIM_part1.txt : documentation for gauging stations from the Global Streamflow Indices and Metadata (GSIM) archive— README_Technical_documentation_GIRES_v10.pdf : full documentation for this repository3. Data format and projectionThe geometric network (lines) and gauging stations (points) datasets are distributed both in ESRI® file geodatabase and shapefile formats. The file geodatabase contains all data and is the prime, recommended format. Shapefiles are provided as a copy for users that cannot read the geodatabase. Each shapefile consists of five main files (.dbf, .sbn, .sbx, .shp, .shx), and projection information is provided in an ASCII text file (.prj). The attribute table can be accessed as a stand-alone file in dBASE format (.dbf) which is included in the Shapefile format. These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed.All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326).4. License and citations4.1 License agreement This documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.4.2 Citations and acknowledgements.Citations and acknowledgements of this dataset should be made as follows:Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5 We kindly ask users to cite this study in any published material produced using it. If possible, online links to this repository (https://doi.org/10.6084/m9.figshare.14633022) should also be provided.

This vector shapefile is a polygon shapefile outlining the extent of the "NWT" project area, for the Niwot Ridge Long Term Ecological Research (LTER) project. The shapefile also covers the Green Lakes Valley portion of the Boulder Creek Critical Zone Observatory (CZO). Other datasets available in this series includes orthorectified aerial photograph mosaics (for 1953, 1972, 1985, approximately 1990, 1999, 2000, 2002, 2004, 2006 and 2008), digital elevation models (DEM's), and accessory map layers. Together, the DEM's and imagery will be of interest to students, research scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

Seattle Parks and Recreation GIS Map Layer Shapefile - Tennis Courts Point

Shapefile - This Seattle Parks and Recreation ARCGIS park feature map layer was exported from SPU ARCGIS and converted to a shapefile then manually uploaded to data.seattle.gov via Socrata.

OR

Web Services - Live "read only" data connection ESRI web services URL: http://gisrevprxy.seattle.gov/arcgis/rest/services/DPR_EXT/ParksExternalWebsite/MapServer/46

description: Seattle Parks and Recreation GIS Map Layer Shapefile - Football Field Point Shapefile - This Seattle Parks and Recreation ARCGIS park feature map layer was exported from SPU ARCGIS and converted to a shapefile then manually uploaded to data.seattle.gov via Socrata. OR Web Services - Live "read only" data connection ESRI web services URL: http://gisrevprxy.seattle.gov/arcgis/rest/services/DPR_EXT/ParksExternalWebsite/MapServer/17; abstract: Seattle Parks and Recreation GIS Map Layer Shapefile - Football Field Point Shapefile - This Seattle Parks and Recreation ARCGIS park feature map layer was exported from SPU ARCGIS and converted to a shapefile then manually uploaded to data.seattle.gov via Socrata. OR Web Services - Live "read only" data connection ESRI web services URL: http://gisrevprxy.seattle.gov/arcgis/rest/services/DPR_EXT/ParksExternalWebsite/MapServer/17

Seattle Parks and Recreation GIS Map Layer Shapefile - Lacrosse Field

Shapefile - This Seattle Parks and Recreation ARCGIS park feature map layer was exported from SPU ARCGIS and converted to a shapefile then manually uploaded to data.seattle.gov via Socrata.

OR

Web Services - Live "read only" data connection ESRI web services URL: http://gisrevprxy.seattle.gov/arcgis/rest/services/DPR_EXT/ParksExternalWebsite/MapServer/26

Description This dataset contains both tabular and geospatial data of eight great powers' overseas military bases, including China, the United States, the United Kingdoms, Russia, Japan, India, the United Arab Emirates, and France up until November 2020. An interactive view of this dataset: Link Source All data were collected from multiple public sources and specified in each data point in the Excel file and Shapefile. For metadata, such as data description and available methods for geospatial data processing, please read the readme.pdf. Terms of use This dataset features in a collection of geospatial data "Geo-mapping databases for the Belt and Road Initiative". To cite this work, available citation styles can be found here: https://doi.org/10.6084/m9.figshare.c.6076193