The data in this archive in in a zipped R data binary format, https://cran.r-project.org/doc/manuals/r-release/R-data.html. These data can be read by using the open source and free to use statistical software package R, https://www.r-project.org/. The data are organized following the figure numbering in the manuscript, e.g. Figure 1a is fig1a, and contains the same labeling as the figures including units and variable names. For a full explanation of the figure, please see the captions in the manuscript. To open this data file, use the following commands in R. load(‘JKelly_NH4NO3_JGR_2018.rdata’) To list the contents of the file, use the following command in R ls() The data for each figure is contained in the data object with the figures name. To list the data, simply type the name of the figure returned from the ls() command. The original model output and emissions used for this study are located on the ASM archived storage at /asm/ROMO/finescale/sjv2013. These data are in NetCDF format with self contained metadata with descriptive headers containing variable names, units, and simulation times. This dataset is associated with the following publication: Kelly, J., C. Parworth, Q. Zhang, D. Miller, K. Sun, M. Zondlo , K. Baker, A. Wisthaler, J. Nowak , S. Pusede , R. Cohen , A. Weinheimer , A. Beyersdorf , G. Tonnesen, J. Bash, L. Valin, J. Crawford, A. Fried , and J. Walega. Modeling NH4NO3 Over the San Joaquin Valley During the 2013 DISCOVER‐AQ Campaign. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 123(9): 4727-4745, (2018).

The Berlin police stations are shown. They are described by attributes of the INSPIRE Utilities and Government Services data model.

The Government Enterprise Architecture for NZ, GEANZ (pronounced as Genes) is an architectural DNA to modernise, simplify, and standardise the public service while making the most of government investments. The GEANZ Technologies model forms part of the GEANZ 2024 Framework which is under development and can be found on this link: https://catalogue.data.govt.nz/dataset/geanz-2024-framework. It is also used to provide categories to find standards and guidance in the GEANZ Standards and Guidance Catalogue https://catalogue.data.govt.nz/dataset/geanz-standards-and-guidance-catalogue. This data set is the GEANZ Technologies model. The GEANZ Technologies model is replacing the GEANZ Applications and Software Services Reference Taxonomy and the GEANZ Infrastructure Taxonomy as part of the GEANZ 2024 Framework that can be found here https://catalogue.data.govt.nz/dataset/gea-nz-reference-taxonomies. Models, diagrams, and csv files will be published here to support the open collaborative development of the model. Contains BETA Release July 2024 and GAMMA Release September 2024 updates. For more information contact the Digital Public Service (DPS) branch of Department of Internal Affairs (DIA) at GCDO@dia.govt.nz, or jim.clendon@dia.govt.nz or 0274527463. Jim is coordinating this work.

Contains data related to Arsenate and Arsenite injections into chlorinated distribution system simulator. Contains data related to model to predict arsenate and arsenite aqueous and wall concentrations within a chlorinated water distribution system. This dataset is associated with the following publication: Burkhardt, J., J. Szabo, S. Klosterman, J. Hall, and R. Murray. Modeling Fate and Transport of Arsenic in a Chlorinated Distribution System. ENVIRONMENTAL MODELLING AND SOFTWARE. Elsevier Science Ltd, New York, NY, USA, 93(1): 322-331, (2017).

The data set contains the survey questions used to evaluate the Open Government Data-Citizen Engagement Model (OGD-CEM). Two versions of the questionnaire were uploaded: one in English and another one in Indonesian. The questionnaire was used to collect data from international open data users to understand the factors that influence their intention to engage with OGD. It is a supplement of the dissertation titled "Citizen Engagement with Open Government Data: A Model for Analyzing Factors Influencing Citizen Engagement."

The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Water quality data can be downloaded in Excel, CSV, TSV, and KML formats. Fourteen site types are found in the WQP: aggregate groundwater use, aggregate surface water use, atmosphere, estuary, facility, glacier, lake, land, ocean, spring, stream, subsurface, well, and wetland. Water quality characteristic groups include physical conditions, chemical and bacteriological water analyses, chemical analyses of fish tissue, taxon abundance data, toxicity data, habitat assessment scores, and biological index scores, among others. Within these groups, thousands of water quality variables registered in the EPA Substance Registry Service (https://iaspub.epa.gov/sor_internet/registry/substreg/home/overview/home.do) and the Integrated Taxonomic Information System (https://www.itis.gov/) are represented. Across all site types, physical characteristics (e.g., temperature and water level) are the most common water quality result type in the system. The Water Quality Exchange data model (WQX; http://www.exchangenetwork.net/data-exchange/wqx/), initially developed by the Environmental Information Exchange Network, was adapted by EPA to support submission of water quality records to the EPA STORET Data Warehouse [USEPA, 2016], and has subsequently become the standard data model for the WQP. Contributing organizations: ACWI The Advisory Committee on Water Information (ACWI) represents the interests of water information users and professionals in advising the federal government on federal water information programs and their effectiveness in meeting the nation's water information needs. ARS The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief in-house scientific research agency, whose job is finding solutions to agricultural problems that affect Americans every day, from field to table. ARS conducts research to develop and transfer solutions to agricultural problems of high national priority and provide information access and dissemination to, among other topics, enhance the natural resource base and the environment. Water quality data from STEWARDS, the primary database for the USDA/ARS Conservation Effects Assessment Project (CEAP) are ingested into WQP via a web service. EPA The Environmental Protection Agency (EPA) gathers and distributes water quality monitoring data collected by states, tribes, watershed groups, other federal agencies, volunteer groups, and universities through the Water Quality Exchange framework in the STORET Warehouse. NWQMC The National Water Quality Monitoring Council (NWQMC) provides a national forum for coordination of comparable and scientifically defensible methods and strategies to improve water quality monitoring, assessment, and reporting. It also promotes partnerships to foster collaboration, advance the science, and improve management within all elements of the water quality monitoring community. USGS The United States Geological Survey (USGS) investigates the occurrence, quantity, quality, distribution, and movement of surface waters and ground waters and disseminates the data to the public, state, and local governments, public and private utilities, and other federal agencies involved with managing the United States' water resources. Resources in this dataset:Resource Title: Website Pointer for Water Quality Portal. File Name: Web Page, url: https://www.waterqualitydata.us/ The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Links to Download Data, User Guide, Contributing Organizations, National coverage by state.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 (NAD83). All bare earth elevation values are in meters and are referenced to the North American Vertical Datum of 1988 (NAVD88). Each tile is distributed in the UTM Zone in which it lies. If a tile crosses two UTM zones, it is delivered in both zones. The one-meter DEM is the highest resolution standard DEM offered in the 3DEP product suite. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

Shown are the locations of the publicly funded day-care centers in Berlin with information on the day-care centers. They are described by attributes of the INSPIRE Utilities and Government Services data model.

That neither the assumptions nor the predictions of standard government formation models entirely correspond to empirical findings has led some to conclude that theoretical accounts of government formation should be reconsidered from the bottom up. We take up this challenge by presenting a zero-intelligence model of government formation. In our model, three or more parties that care about office and policy make random government proposals. The only constraints that we impose on government formation correspond to the two binding constitutional constraints that exist in all parliamentary systems: an incumbent government always exists and all governments must enjoy majority legislative support. Despite its deliberately limited structure, our model predicts distributions over portfolio allocation, government types, and bargaining delays that approach those observed in the real world. Our analysis suggests that many formation outcomes may result from the institutional foundation of parliamentary democracies, independent of the strategic behavior of party leaders.

High resolution (1-2m spacing) digital elevation models (DEMs) covering portions of the state. The DEMs are derived from LIDAR data and depict the bare earth terrain in raster format. Multiple agencies (Federal, State, and County) provided the data. The DEMs can be downloaded through the NYS Orthos Online app (http://orthos.dhses.ny.gov/).

This table lists city officials that benefited from a lobbyist expenditure. You can recreate the City Clerk's lobbyist database by downloading all lobbyist datasets on the data portal and view the complete data model of the lobbyist database here: https://services.austintexas.gov/edims/document.cfm?id=293053

NYC 1foot Digital Elevation Model: A bare-earth, hydro-flattened, digital-elevation surface model derived from 2010 Light Detection and Ranging (LiDAR) data. Surface models are raster representations derived by interpolating the LiDAR point data to produce a seamless gridded elevation data set. A Digital Elevation Model (DEM) is a surface model generated from the LiDAR returns that correspond to the ground with all buildings, trees and other above ground features removed. The cell values represent the elevation of the ground relative to sea level. The DEM was generated by interpolating the LiDAR ground points to create a 1 foot resolution seamless surface. Cell values correspond to the ground elevation value (feet) above sea level. A proprietary approach to surface model generation was developed that reduced spurious elevation values in areas where there were no LiDAR returns, primarily beneath buildings and over water. This was combined with a detailed manual QA/QC process, with emphasis on accurate representation of docks and bare-earth within 2000ft of the water bodies surrounding each of the five boroughs.

Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_DigitalElevationModel.md

The National Household Model (NHM) is delivered by the Department for Business, Energy and Industrial Strategy (BEIS). It was developed on behalf of BEIS by the Centre for Sustainable Energy (CSE) as an open-source tool for projecting the effects of policy and other legislative changes on the energy and emissions of the UK domestic housing stock by the Department of Energy and Climate Change (DECC). In order to be able to set up scenarios model users need to learn a specialist scenario language based on S-expressions (such as those used in the Lisp language) using the instruction manual found on the BEIS GitHub site. This will allow them to run the model which acts on the data in the various UK housing surveys. UK Data Archive stores the datasets for this model on behalf of the Department for Communities and Local Government (DCLG). Users need to register at UK Data Archive in order to access the raw data and run a conversion program to create a full dataset. The NHM team encourages feedback on all aspects of the model and documentation.

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and warning efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to a variety of vertical datums and horizontal datum of World Geodetic System of 1984 (WGS84). Cell size for the DEMs ranges from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).

The locations of Berlin's publicly funded daycare centers are shown with information about the daycare centers. They are described by attributes of the INSPIRE data model "Utilities and Government Services".

This dataset lists each municipal question a lobbyist reports. You can recreate the City Clerk's lobbyist database by downloading all lobbyist datasets on the data portal and view the complete data model of the lobbyist database here: https://www.austintexas.gov/edims/document.cfm?id=293053

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This index enables you to identify freely available digital bathymetric surface models covering the South West Pacific owned by LINZ. This data provides a 3-dimensional model of the surface of the seafloor. These surface models have been created by LINZ from single- or multi-beam data collected in the South West Pacific since 2005. The polygons in the index show the extent of these gridded data models, and include descriptive information, such as the age and quality of the data. The gridded surface models are not downloadable from the LINZ Data Service, but can be requested. Please refer to the LINZ Bathymetric Index Data Dictionary for further information about the attributes of this dataset, and formats in which the data is available. How to order the data: Requests for the models should be sent to customersupport@linz.govt.nz with “Hydro Bathy Data” in the subject line. Requests must, as a minimum, specify the id and surf_name of the models of interest and the data format (see the options in Section 1.4 of the Bathymetric Data Dictionary). LINZ has also created 3-dimensional bathymetric surface models for the South West Pacific from data provided by third parties, which is subject to different licencing terms and conditions. View our "SW Pacific Bathymetric Surface Model Index – Third Party" dataset to request this data.

This index enables you to identify freely available digital bathymetric surface models owned by LINZ. This data provides a 3-dimensional model of the surface of the seafloor.

These surface models have been created by LINZ from publically funded single- or multi-beam data collected in the Southern Ocean/Ross Sea since 2004. The polygons in the index show the extent of these gridded data models, and include descriptive information, such as the age and quality of the data.

The gridded surface models are not downloadable from the LINZ Data Service, but can be provided to you on request.

Please refer to the LINZ Bathymetric Index Data Dictionary for further information about the attributes of this dataset, and formats in which the data is available.

How to order the data: Requests for the models should be sent to hydro@linz.govt.nz with “Hydro Bathy Data” in the subject line. Requests must, as a minimum, specify the id and surf_name of the models of interest and the data format (see the options in Section 1.4 of the Bathymetric Data Dictionary).

This table lists each real property associated with a lobbyist's municipal question. You can recreate the City Clerk's lobbyist database by downloading all lobbyist datasets on the data portal and view the complete data model of the lobbyist database here: https://services.austintexas.gov/edims/document.cfm?id=293053