This is list of data elements and their attributes that are used by data assets at the Federal Highway Administration.

This dataset provides a brief explanation of the names and values for the data elements and attributes in RxNorm and the UMLS.

The National Bridge Inventory Elements dataset is as of June 20, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 620,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal and Tribal lands. The element data present a breakdown of the condition of each structural and bridge management element for each bridge on the National Highway System (NHS). The Specification for the National Bridge Inventory Bridge Elements contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519106. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1519106

The National Tunnel Inventory Elements dataset was compiled on September 02, 2025 and published on August 26, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Tunnel Inventory (NTI) is a collection of information (database) describing the more than 500 of the Nation's tunnels located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible tunnels on Federal lands. The element data present a breakdown of the condition of each structural and civil element for each tunnel on the National Highway System (NHS). A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529051

This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection. An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code. The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011). Methods Data:

Raw files were downloaded from ftp1.cloudsat.cira.colostate.edu in directory 2B-GEOPROF.R04 Processed files are in netcdf format

Processing:

Data were processed and analyzed using IDL. See CloudSat_code_README.txt for details The initial processing was done while I was a graduate student at the Univerisity of Miami working on my masters from 2006-2009 Code is available at https://github.com/erileydellaripa/CYGNSS_code

Data file description:

Once the tar.gz file is unpacked, the EO attributes are provided in the EO_masterlistYYYY.nc files, where YYYY corresponds to the different years. I transferred the EO attributes from IDL .save files to netcdf files for sharing. A description of each EO attribute is provide in the README.md and if you do an ncdump -h in a terminal window.

The attributes are organized in 1D arrays, where the element of each array corresponds to a unique EO and the total size of the array corresponds to the total number of EOs identified.

Data are processed from the start of CloudSat 15 June 2006 till 17 January 2013 for the EO attributes.

In total, there are 15,181,193 EOs.

There was a battery failure 17 April 2011. CloudSat resumed collecting data 27 October 2011, but only during the day.

References:

Riley, E. M., B. E. Mapes, and S. N. Tulich, 2011: Clouds Associated with the Madden-Julian Oscillation: A New Perspective from CloudSat. J. Atmos. Sci., 68, 3032-3051, https://doi.org/10.1175/JAS-D-11-030.1.

Riley, E. M., and B. E. Mapes, 2009: Unexpected peak near -15°C in CloudSat echo top climatology. Geophys. Res. Lett., 36, L09819, https://doi.org/10.1029/2009GL037558.

Riley, E. M., 2009: A global survey of clouds by CloudSat. M.S. thesis, Division of Meteorology and Physical Oceanography, University of Miami, 134 pp, https://scholarship.miami.edu/esploro/outputs/991031447848002976.

Dataset contains basic changed data of RÚIAN, e.g. basic descriptive data of territorial elements and units of territorial registration, for which at least one of their attributes has changed on the selected day. Dataset contains no spatial location (polygons and definition lines) and centroids of RÚIAN elements. The file contains following elements (in case they have been changed): state, cohesion region, higher territorial self-governing entity (VÚSC), municipality with extended competence(ORP), authorized municipal office (POU), region (old ones – defined in 1960), county, municipality, municipality part, town district (MOMC), Prague city district (MOP), town district of Prague (SOP), cadastral units and basic urban units (ZSJ), streets, building objects and address point. Up-to-date data is specified for each element: code, centroid (if exists) and all available descriptive attributes including the code of superior element. Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on RÚIAN (Register of Territorial Identification, Addresses and Real Estates). Files are created every day (in case any change of any element occurred). Data is provided in RÚIAN exchange format (VFR), which is based on XML language and fulfils the GML 3.2.1 standard (according to ISO 19136:2007). Dataset is compressed (ZIP) for downloading. More in the Act No. 111/2009 Coll., on the Basic Registers, in Decree No. 359/2011 Coll., on the Basic Register of Territorial Identification, Addresses and Real Estates.

The land system changes dataset includes five maps of land system maps for tipping elements on Earth for the years 2000, 2010, 2020, and 2100, under two climate scenarios: reference and climate pledges. The dataset was produced by integrating the GCAM model with a modified version of CLUMondo. It offers a spatial resolution of 1 km and a thematic resolution comprising 30 categories, which combine three density types and ten land cover types. The land system types corresponding to the values in the raster data attribute table are detailed in the Data Records section of our submitted manuscript.

The dataset contains relevant and irrelevant image tags of Web pages of 125 different domains. The image dataset contains the web domain, file number, the text of image HTML element, attributes of image elements, the size attributes, the parent HTML element of the image, and relevancy of the image. Each Web domain contains 100 Web pages with varying number of image elements.

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.

This Namoi (NAM) dataset contains v5 of the Asset database (NAM_asset_database_20160218.mdb), a Geodatabase version for GIS mapping purposes (NAM_asset_database_20160218_GISOnly.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NIC-NAM-130-WaterDependentAssetRegister-AssetList-v20160218.xlsx), a data dictionary (NAM_asset_database_doc_20160218.doc), a folder (Indigenous_doc) containing documentation associated with Indigenous water asset project, and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below.

The Asset database for the Namoi subregion on 18 February 2016 supersedes the previous version of the Asset database v4 "Asset database for the Namoi subregion on 15 January 2015" GUID: c32e70ad-9357-4297-a5dd-e1f1e1f5255f. Updates in this v5 database include:

(1) Total number of registered water assets was increased by 7 due to:

(a) The 6 assets changed M2 test to "Yes" and 1 assets changed reason from the review done by Ecologist group. The original data is included the database as the table tbl_NAM _Species_TEC_decisions_reveiw_23112015

(b) One indigenous water asset from OWS were added. The data and documents from OWS are included in subdirectory Indigenous_doc

(c)The draft new Water Dependent Asset Register file (BA-NIC-NAM-130-WaterDependentAssetRegister-AssetList-v20160218.xlsx) was created

(2) The databases, especially spatial database, were changed such as (a) spatial data are saved in a separated file geodatabase, (b) duplicated attributes fields in spatial data were removed and only ID field is kept in the spatial data. The user can use AID or ElementID to join the table in personal geodatabase with relevant spatial data

The user can join the Table Assetlist (in NAM_asset_database_20160218.mdb) to the spatial data (GM_NAM_AssetList_ln, GM_NAM_AssetList_poly and GM_NAM_AssetList_pt in NAM_asset_database_20160218_GISOnly.gdb) from ArcMap by AID to get those attributes for assets. The user can join the Table Elementlist (in NAM_asset_database_20160218.mdb) to the spatial data (GM_NAM_ElementsList_ln, GM_NAM_ElementsList_poly and GM_NAM_ElementsList_pt in NAM_asset_database_20160218_GISOnly.gdb) from ArcMap by ElementID to get those attributes for elements . Element_to_asset (in NAM_asset_database_20160218.mdb) can join to above the spatial data or above two joined results for more information.

Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of " NAM_asset_database_doc_20160218.doc", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "NAM_asset_database_doc_20160218.doc doc" located in the zip file.

The public version of this asset database can be accessed via the following dataset: Asset database for the Namoi subregion on 18 February 2016 Public (https://data.gov.au/data/dataset/3134fa6b-f876-46dd-b26b-88d46d424185).

Purpose

The Asset List Database was developed to spatially identify water dependent assets found within the Namoi subregion.

The public version of this asset database can be accessed via the following dataset: Asset database for the Namoi subregion on 18 February 2016 Public (https://data.gov.au/data/dataset/3134fa6b-f876-46dd-b26b-88d46d424185).

Dataset History

On 20 April 2015 the title of this database was changed from "Namoi_AssetList_Database_v4_20150115".

This dataset replicates the spatial and tabular content and structure of the previous version of the Namoi asset list ("Asset list for Namoi - CURRENT"; ID: 538c717c-c04a-4720-8bcd-96fbdf7f0d80) with the exception that decisions made by the Namoi Project Team concerning Materiality Test 2 (water dependency) have been incorporated into the AssetList table, which are used to define the water dependent asset register.

Date \t Notes

22/07/2014\tInitial database for asset related tables and feature classes, and imported element data from element list database

5/09/2014\tUpdated database with updated WSP/GWMP/RegRiv assets/elements; additional WSP plus point water volume data and additional RegRiv plus point water volume data

18/11/2014\tMerge some assets with non standard classification to standard classification

18/11/2014\tadd additional point groundwater economic data ( 121 new elements)

18/11/2014\tadd additional point surface water economic data (49 new elements)

15/01/2015\tIncorporate materiality decisions (M2) from project team into AssetList table

18/02/2016\t"(1)Total number of registered water assets was increased by 7 due to:

            (a) The 6 assets changed M2 test to "Yes" and 1 assets changed reason from the review done by Ecologist 

           group. The original data is included the database as the table tbl_NAM _Species_TEC_decisions_reveiw_23112015

           (b) One indigenous water asset from OWS were added. The data and documents from OWS are included in 

           subdirectory Indigenous_doc

           (c)The draft new Water Dependent Asset Register file (BA-NIC-NAM-130-WaterDependentAssetRegister-

            AssetList-v20160218.xlsx) was created

           (2) The databases, especially spatial  database, were changed such as (a) spatial data are saved in a separated file 

            geodatabase, (b) duplicated attributes fields in spatial data were removed and only ID field is kept in the spatial

            data."

Dataset Citation

Bioregional Assessment Programme (2016) Asset database for the Namoi subregion on 18 February 2016. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/22061f2c-e86d-4ca8-9860-c349c2513fd8.

Dataset Ancestors

• Derived From Asset list for Namoi - CURRENT

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From NSW Office of Water Surface Water Licences in NIC linked to locations v1 (22 April 2014)

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Asset database for the Namoi subregion on 15 January 2015

• Derived From Missing SW Licensing Data in the Namoi PAE 20140711

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

• Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases

• Derived From NSW Office of Water Groundwater licences extract linked to spatial locations NIC v3 (13 March 2014)

• Derived From [Australia - Species of National Environmental Significance

Dataset contains full descriptive data and spatial location of territorial elements and units of territorial registration, for which at least one of their attributes has changed on the selected day. Data is generated in 3 files for the whole Czech Republic. In all files all available descriptive attributes are listed for each element including centroid (if it exists). File ST_ZKSG contains following elements: state, cohesion region, higher territorial self-governing entity (VÚSC), municipality with extended competence(ORP), authorized municipal office (POU), region (old ones – defined in 1960), county, municipality, municipality part, municipality district/city part (MOMC - for territorialy structured statutory cities), Prague city district (MOP), town district of Prague (SOP) for Prague, cadastral units and basic urban units (ZSJ), futhermore generalised boundaries for all elements (if it does not exist, then original boundaries). File ST_ZKSH for the whole state contains following elements: state, cohesion region, VÚSC, ORP, POU, region (old – defined in 1960), and county, municipality, part of municipality, MOMC, MOP, SOP, cadastral unit, ZSJ, parcels (including their polygons), building objects , streets (inluding their definition lines) and address points, futhermore original boundaries for all higher elements. File ST_ZKSO contains changes of pictures of flags and emblems of municipalities and MOMC. Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on RÚIAN (Register of Territorial Identification, Addresses and Real Estates). Files are created every day (in case any change of any element occurred). Data is provided in RÚIAN exchange format (VFR), which is based on XML language and fulfils the GML 3.2.1 standard (according to ISO 19136:2007). Dataset is compressed (ZIP) for downloading. More in the Act No. 111/2009 Coll., on the Basic Registers, in Decree No. 359/2011 Coll., on the Basic Register of Territorial Identification, Addresses and Real Estates.

The sheets within this excel file document participant's scoring of ecosystem function potential of listed Biophilic Design Elements. Based on participant scoring, each listed Biophilic Deisng Element is assessed for ecosystem service attributes

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This data set holds the publicly-available version of the database of water-dependent assets that was compiled for the bioregional assessment (BA) of the Gloucester subregion as part of the Bioregional Assessment Technical Programme. Though all life is dependent on water, for the purposes of a bioregional assessment, a water-dependent asset is an asset potentially impacted by changes in the groundwater and/or surface water regime due to coal resource development. The water must be other than local rainfall. Examples include wetlands, rivers, bores and groundwater dependent ecosystems.

Under the Bioregional Assessment Technical Programme (BATP), a spatial assets database was developed for each bioregion and / or subregion. A single asset is represented spatially in the asset database by single or multiple spatial features (point, line or polygon). Individual points, lines or polygons are termed elements.

The spatial elements that represent assets were identified by regional natural resource management organisations and collated by ERIN into state databases using the Water Asset Information Tool (WAIT). These data were supplemented with additional information from appropriate Australian and state and territory government databases. The materiality of each asset was tried against a series of tests and the consequent decisions were also included in the asset database. Each asset database is therefore a rich collation of information about the assets and how they have been assessed.

All assets and elements also have associated attribute data; these are stored in attribute tables, including associated lookup tables (LUTs). Some data in the asset database are not associated with spatial data; typically these data relate to the database itself e.g. versioning information

This dataset contains the unrestricted publicly-available components of spatial and non-spatial (attribute) data of the (restricted) Asset database for the Gloucester subregion on 12 February 2016 (72a47bec-1393-49d6-b379-0e48551d26a9). The database is provided primarily as an ESRI File geodatabase (.gdb), which is able to be opened in readily available open source software such as QGIS. Other formats include the Microsoft Access database (.mdb in ESRI Personal Geodatabase format), industry-standard ESRI Shapefiles and tab-delimited text files of all the attribute tables.

The restricted version of the Gloucester Asset database has a total count of 4029 Elements ( including 11 aspatial elements) and 229 Assets (including 11 aspatial Assets ) . In the public version of the Asset Gloucester database 789 (19%)> Elements (spatial features) have been removed from the Element List and spatial Element Layer(s) and 42 Assets (19%) have been removed from the spatial Asset Layer(s)

The elements/assets removed from the restricted Asset Database are from the following data sources:

1) Species Profile and Threats Database (SPRAT) Metadata only) (7276dd93-cc8c-4c01-8df0-cef743c72112)

2) Australia, Register of the National Estate (RNE) (Internal 878f6780-be97-469b-8517-54bd12a407d0)

3) Communities of National Environmental Significance Database - RESTRICTED - Metadata only (c01c4693-0a51-4dbc-bbbd-7a07952aa5f6)

4) Fish Biodiversity Hotspot sampling data

These important assets are included in the bioregional assessment, but are unable to be publicly distributed by the Bioregional Assessment Programme due to restrictions in their licensing conditions. Please note that many of these data sets are available directly from their custodian.For more precise details please see the associated explanatory Data Dictionary document enclosed with this dataset.

Purpose

Used for Gloucester subregion for bioregional assessments

Dataset History

The public version of the asset database retains all of the unrestricted components of the Asset database for the Gloucester subregion on 12 February 2016

• any material that is unable to be published or redistributed to a third party by the BA Programme has been removed from the database. The data presented corresponds to the assets published Gloucester subregion product 1.3: Description of the water-dependent asset register and asset list for the Gloucester subregion on 12 February 2016, and the associated Water-dependent asset register and asset list for the Gloucester subregion on 12 February 2016.

Individual spatial features or elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). In accordance to BA submethodology M02: Compiling water-dependent assets, individual spatial elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2), which are assets that are considered to be water dependent.

Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the assessment team and incorporated into the AssetList table in the Asset database.

Development of the Asset Register from the Asset database:

Decisions for M0 (fit for BA purpose), M1 (PAE) and M2 (water dependent) determine which assets are included in the "asset list" and "water-dependent asset register" which are published as Product 1.3.

The rule sets are applied as follows:

M0\tM1\tM2\tResult

No\tn/a\tn/a\tAsset is not included in the asset list or the water-dependent asset register

(≠ No)\tNo\tn/a\tAsset is not included in the asset list or the water-dependent asset register

(≠ No)\tYes\tNo\tAsset included in published asset list but not in water dependent asset register

(≠ No)\tYes\tYes\tAsset included in both asset list and water-dependent asset register

Assessment teams are then able to use the database to assign receptors and impact variables to water-dependent assets and the development of a receptor register as detailed in BA submethodology M03: Assigning receptors to water-dependent assets and the receptor register is then incorporated into the asset database.

At this stage of its development, the Asset database for the Gloucester subregion on 12 February 2016 Public, which this document describes, does contain receptor information, but it was removed from this public version

The source metadata was updated to meet the purpose of the Bioregional Assessment Programme

Dataset Citation

Bioregional Assessment Programme (2014) Asset database for the Gloucester subregion on 12 February 2016 Public v02. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/5def411c-dbc4-4b75-b509-4230964ce0fa.

Dataset Ancestors

• Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW)

• Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014

• Derived From Asset database for the Gloucester subregion on 21 August 2015

• Derived From Gloucester digitised coal mine boundaries

• Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Asset database for the Gloucester subregion on 12 September 2014

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved

• Derived From Asset database for the Gloucester subregion on 29 October 2015

• Derived From Geofabric Surface Cartography - V2.1

• Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

• Derived From [National Groundwater Dependent Ecosystems (GDE) Atlas (including

This data release contains input data for hydrologic simulations of the Alaska Domain application of the U.S. Geological Survey (USGS) Precipitation Runoff Modelling System (PRMS) as implemented in the National Hydrologic Model (NHM) infrastructure (Regan and others, 2018). The NHM Alaska Domain parameter database consists of 114 parameter files in ASCII format (CSV), two files needed to run the Alaska Domain PRMS (control.fy19deliverable and fy19_deliv.param), two xml files (dimensions.xml and parameters.xml) containing descriptive information about the parameters, and a table that defines each parameter (AK_paramDB_datadictionary.csv). The Entity and Attribute element of this metadata record describe the data dictionary (AK_paramDB_datadictionary.csv). Please refer to the Supplemental Information element of this metadata record for references cited.

💁‍♀️Please take a moment to carefully read through this description and metadata to better understand the dataset and its nuances before proceeding to the Suggestions and Discussions section.

Dataset Description:

This dataset compiles the tracks from all of Beyoncé's albums available on Spotify, showcasing the evolution of one of the most influential artists in the music industry. It represents a comprehensive array of genres, influences, and musical styles that Beyoncé has explored throughout her career. Each track in the dataset is detailed with a variety of features, popularity, and metadata. This dataset serves as an excellent resource for music enthusiasts, data analysts, and researchers aiming to explore the impact of Beyoncé's music, identify trends in her musical evolution, or develop music recommendation systems based on empirical data.

Scope of the Data:

The focus of this dataset is on providing a comprehensive view of Beyoncé's musical releases on Spotify, specifically tailored to showcase her creative output. To this end, the dataset includes tracks from the following album types: - Albums: Full-length albums released by Beyoncé, encapsulating a range of her musical styles and eras. - Singles: Standalone single releases, highlighting key songs that have been released independently of her full albums. It's important to note that this dataset deliberately excludes compilation albums. Compilations, which often contain a mixture of tracks from various artists or previously released tracks by Beyoncé, are not included to maintain a focus on her original releases and to provide a clearer picture of her artistic evolution.

Data Collection and Processing:

Obtaining the Data: The data was obtained directly from the Spotify Web API, specifically focusing on albums and tracks by Beyoncé. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing: To process and structure the data, Python scripts were developed using data science libraries such as pandas for data manipulation and spotipy for API interactions, specifically for Spotify data retrieval.

Workflow: - Authentication - API Requests - Data Cleaning and Transformation - Saving the Data

Attribute Descriptions:

• artist_name: the name of the artist (Beyoncé and collaborators)
• track_name: the title of the track
• is_explicit: Indicates whether the track contains explicit content
• album_release_date: The date when the track was released
• genres: A list of genres associated with Beyoncé
• danceability: A measure from 0.0 to 1.0 indicating how suitable a track is for - dancing based on a combination of musical elements
• valence: A measure from 0.0 to 1.0 indicating the musical positiveness conveyed by a track
• energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity
• loudness: The overall loudness of a track in decibels (dB)
• acousticness: A measure from 0.0 to 1.0 whether the track is acoustic
• instrumentalness: Predicts whether a track contains no vocals
• liveness: Detects the presence of an audience in the recordings
• speechiness: Detects the presence of spoken words in a track
• key: The key the track is in. Integers map to pitches using standard Pitch Class notation
• tempo: The overall estimated tempo of a track in beats per minute (BPM)
• mode: Modality of the track
• duration_ms: The length of the track in milliseconds
• time_signature: An estimated overall time signature of a track
• popularity: A score between 0 and 100, with 100 being the most popular

Possible Data Projects:

• Trend Analysis in Beyonce's Musical Evolution
• Mood and Musical Elements in Beyonce's Tracks
• Beyonce's Influence on the Music Industry Analysis

Disclaimer and Responsible Use:

This dataset, derived from Spotify focusing on Beyoncé's albums and tracks, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations. - Compliance with Terms of Service: Users should adhere to Spotify's Terms of Service and Developer Policies when utilizing this dataset. - Copyright Notice: The dataset presents music track information including names and artist details for analytical purposes and does not convey any rights to the music itself. Users must ensure that their use does not infringe on the copyright holders' rights. Any analysis, distribution, or derivative work should respect the intellectual property rights of all involved parties and comply with applicable laws. - No Warranty Disclaimer: The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others. - Ethical Use: Users are encouraged to consider the ethical implications of their analyses and the potential impact...

Artificial intelligence (AI) has come to human life in various forms. As external activities decrease after COVID-19, people treat voice intelligent assistant (VIA) as a friend or secretary. This study suggests a conceptual model to identify the contributors to the continuance intention of VIA users. Data is collected from 262 users who use VIAs in their daily life for validating the model. The current research conducted partial least square structural equation modeling to validate the proposed model. The findings indicated that cabin fever syndrome significantly affects both utilitarian motivation and hedonic motivation. Loneliness in COVID-19 causes utilitarian motivation. The results verified that utilitarian motivation is the determinant of perceived usefulness, interaction, and voice attractiveness. The findings of the study figured out that hedonic motivation has a significant influence on interaction, parasocial interaction, and voice attractiveness. The analysis uncovered that i...

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.

This database is an initial Asset database for the Central West subregion on 29 April 2015. This dataset contains the spatial and non-spatial (attribute) components of the Central West subregion Asset List as one .mdb files, which is readable as an MS Access database and a personal geodatabase. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Central West are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Central West subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "CEN_asset_database_doc_20150429.doc ", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "CEN_asset_database_doc_20150429.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted.

Dataset History

This is initial asset database.

The Bioregional Assessments methodology (Barrett et al., 2013) defines a water-dependent asset as a spatially distinct, geo-referenced entity contained within a bioregion with characteristics having a defined cultural indigenous, economic or environmental value, and that can be linked directly or indirectly to a dependency on water quantity and/or quality.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. Elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2) - assets considered to be water dependent.

Elements may be represented by a single, discrete spatial unit (polygon, line or point), or a number of spatial units occurring at more than one location (multipart polygons/lines or multipoints). Spatial features representing elements are not clipped to the preliminary assessment extent - features that extend beyond the boundary of the assessment extent have been included in full. To assist with an assessment of the relative importance of elements, area statements have been included as an attribute of the spatial data. Detailed attribute tables contain descriptions of the geographic features at the element level. Tables are organised by data source and can be joined to the spatial data on the "ElementID" field

Elements are grouped into Assets, which are the objects used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy.

The "Element_to_asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the project team leader and incorporated into the Assetlist table in the Asset database. The Asset database is then re-registered into the BA repository.

The Asset database dataset (which is registered to the BA repository) contains separate spatial and non-spatial databases.

Non-spatial (tabular data) is provided in an ESRI personal geodatabase (.mdb - doubling as a MS Access database) to store, query, and manage non-spatial data. This database can be accessed using either MS Access or ESRI GIS products. Non-spatial data has been provided in the Access database to simplify the querying process for BA project teams. Source datasets are highly variable and have different attributes, so separate tables are maintained in the Access database to enable the querying of thematic source layers.

Spatial data is provided as an ESRI file geodatabase (.gdb), and can only be used in an ESRI GIS environment. Spatial data is represented as a series of spatial feature classes (point, line and polygon layers). Non-spatial attribution can be joined from the Access database using the AID and ElementID fields, which are common to both the spatial and non-spatial datasets. Spatial layers containing all the point, line and polygon - derived elements and assets have been created to simplify management of the Elementlist and Assetlist tables, which list all the elements and assets, regardless of the spatial data geometry type. i.e. the total number of features in the combined spatial layers (points, lines, polygons) for assets (and elements) is equal to the total number of non-spatial records of all the individual data sources.

Dataset Citation

Department of the Environment (2013) Asset database for the Central West subregion on 29 April 2015. Bioregional Assessment Derived Dataset. Viewed 08 February 2017, http://data.bioregionalassessments.gov.au/dataset/5c3f9a56-7a48-4c26-a617-a186c2de5bf7.

Dataset Ancestors

• Derived From Macquarie Marshes Vegetation 1991-2008 VIS_ID 3920

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Travelling Stock Route Conservation Values

• Derived From NSW Wetlands

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Ramsar Wetlands of Australia

• Derived From Native Vegetation Management (NVM) - Manage Benefits

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Climate Change Corridors (Dry Habitat) for North East NSW

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From [New South Wales NSW Regional CMA Water Asset

The Oregon Fish Passage Barrier Data Standard (OFPBDS) dataset contains barriers to fish passage in Oregon watercourses. Barriers include the following types of natural or artificial structures: bridges, cascades, culverts, dams, debris jams, fords, natural falls, tide gates, and weirs. The OFPBDS dataset does not include structures which are not associated with in-stream features (such as dikes, levees or berms). Barriers are structures which do, or potentially may, impede fish movement and migration. Barriers can be known to cause complete or partial blockage to fish passage, or they can be completely passable, or they may have an unknown passage status. The third publication of the OFPBDS dataset (Version 3) complies with version 1.1 of the data standard. New optional attributes have been added to describe fish passage barrier feature modifications, to describe supplementary information (via a comments field) and also to linear reference the barrier features to the National Hydrography Dataset. Linear referencing attributes for the Pacific Northwest Hydrography have been retained in this version of the publication datasets, however they are no longer part of the data standard and will be removed from the next dataset publication version. Version 3 of the OFPBDS dataset contains over 30,000 barrier features from seventeen separate sources including: Oregon Department of Fish and Wildlife (ODFW), Oregon Department of Transportation (ODOT), Oregon Department of Water Resources (OWRD), Oregon Department of Forestry (ODF), Oregon Watershed Enhancement Board (OWEB), US Bureau of Land Management (BLM), US Forest Service, Nez Perce Tribe, Benton SWCD, Washington county and watershed councils representing the Rogue, Umpqua, Siuslaw, Santiam, Calapooia, Clackamas and Scapoose basins. The Data Steward obtained fish passage barrier data from multiple data originators between 2008 and 2011, collaborated with them to develop inclusion / exclusion criteria and dataset specific crosswalks for converting data from its original data structure to the structure of the OFPBDS. The data were then converted into the OFPBDS format and analyzed for duplication with existing OFPBDS barrier features. Where duplicates were identified, depending upon the scenario, one feature was either chosen over the other or in some cases attributes from different sources are combined. Source information is retained for each feature. The data were then loaded into the OFPBDS database. Barrier features were linear referenced (Framework Hydro only which is outside of the standard) and the corresponding optional attribute elements were populated. The data conversion, duplication reconciliation and linear referencing protocols are documented in the Oregon Fish Passage Barrier Data Management Plan. A separate dataset containing fish passage barrier features that have been completely removed (e.g. dam removals and culvert replacements) will be published simultaneously with version 3 of the OFPBDS dataset. The OFPBDS database does not represent a comprehensive record of fish passage barriers in Oregon. Attributes (including key attributes such as fish passage status) are often incomplete. Consistency in attribution also varies among data originators. Field verification of barrier features and their attributes will be an important component to making this dataset comprehensive, current and accurate. Fish passage status is a key attribute. Many barrier features - including all ODOT barriers - have an unknown passage status. For other features, the passage status may have changed since documented. Note that this metadata file is best viewed in ArcCatalog with the FGDC Classic Stylesheet. Documentation for the OFPBDS can be found online at http://www.oregon.gov/DAS/EISPD/GEO/docs/bioscience/OregonFishPassageBarrierDataStandardv1dot1.pdf.

The Geological Atlas of the Western Canada Sedimentary Basin was designed primarily as a reference volume documenting the subsurface geology of the Western Canada Sedimentary Basin. This GIS dataset is one of a collection of shapefiles representing part of Chapter 27 of the Atlas, Geological History of the Williston Basin and Sweetgrass Arch, Figure 6, Lithotectonic Basement Elements. Shapefiles were produced from archived digital files created by the Alberta Geological Survey in the mid-1990s, and edited in 2005-06 to correct, attribute and consolidate the data into single files by feature type and by figure.