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.

The difference between QLD DNRM groundwater entitlements v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database. Also, when processing the surface water licences, groundwater licences were found amongst the surface water licences and these have now been included in the groundwater licences instead.

Purpose

This dataset is for the purposes of including water entitlements (water extractions) as Economic Assets into the Asset Database

Dataset History

The difference between QLD DNRM groundwater entitlements - v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database.

Where purpose = domestic; or domestic & stock; or stock then it was classed as 'basic water right'. Where it is listed as both a domestic/stock and a licensed use such as irrigation, it was classed as a 'water access right.' All other take and use were classed as a 'water access right'.

Also, when processing the surface water licences, groundwater licences were found and these have now been included in the groundwater licences instead.

The worksheet '1 Lic per bore' has been adapted from the original dataset (extract from the QLD Dept of Natural Resource and Mines licensing data from the water management system).

'1 lic per bore' worksheet repeats the licence information against each bore it is assigned to. The lat/long in the worksheet is where DNRM have assigned the spatial location to the licence; however the 'hydroID' column can be joined to the NGIS to assign locations and depths to individual bores. Additional columns are also the number of bores per licence and the volume per bore (total entitlement / # bores). This means the data can be used per licence as an aggregate, or per bore. Per bore will help with assigning where the aquifers extraction is occurring, but will be quite detailed. This dataset allows the user to assign the licence to individual bores without double counting the entitlement. Linking to the bores will also allow us to allocate a depth/ aquifer to the licence

The worksheet 'all data' is the original dataset obtained from DNRM:

This dataset is an extract from the QLD Dept of Natural Resource and Mines licensing data from the water management system. The dataset includes basic right (domestic & stock) licences as well as groundwater licences to take & use groundwater (eg. irrigation, town supply etc). Groundwater licences can have an allocation as megalitres or a right to irrigate a certain number of hectares. There is no basic conversion to determine how many megalites/hectare. The volume allocated for domestic and stock also varies based on the management area. This information can be found resource operations plan or water sharing rules.

http://www.nrm.qld.gov.au/water/management/water_sharing_rules/index.html

For licences that do not have a lat/long, property details are included. This could be tied to a cadastre layer.

Dataset Citation

Bioregional Assessment Programme (2014) QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/68bbd3fb-6e2a-4088-a3dd-55cc44c2f0d6.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

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Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Soil is a key natural resource that provides the foundation of basic ecosystem services. Soil determines the types of farms and forests that can grow on a landscape. Soil filters water. Soil helps regulate the Earth's climate by storing large amounts of carbon. Activities that degrade soils reduce the value of the ecosystem services that soil provides. For example, since 1850 35% of human caused green house gas emissions are linked to land use change. The Soil Science Society of America is a good source of of additional information.Bulk density is an important property of soil. Soil is a mixture of mineral particles, organic material and open spaces known as pores. The size and distribution of the pores affect how water is stored and how nutrients move through the soil. When a soil is compacted it looses pore space and bulk density increases resulting in lower water storage and higher runoff.Dataset SummaryThis layer provides access to a 30 arc-second (roughly 1 km) cell-sized raster with attributes related to the density of soil derived from the Harmonized World Soil Database v 1.2. The values in this layer are for the dominant soil in each mapping unit (sequence field = 1).Attribute values for topsoil (0-30 cm) and subsoil (0-100 cm) are provided for bulk density (derived from available analyzed data) and reference bulk density (statistical estimate based on soil texture). The data are in units of kg/dm3. Topsoil Reference Bulk DensityTopsoil Bulk Density Subsoil Reference Bulk DensitySubsoil Bulk DensityThe layer is symbolized with the Topsoil Bulk Density field.The document Harmonized World Soil Database Version 1.2 provides more detail on the difference between bulk density and reference bulk density.Other attributes contained in this layer include:Soil Mapping Unit Name - the name of the spatially dominant major soil groupSoil Mapping Unit Symbol - a two letter code for labeling the spatially dominant major soil group in thematic mapsData Source - the HWSD is an aggregation of datasets. The data sources are the European Soil Database (ESDB), the 1:1 million soil map of China (CHINA), the Soil and Terrain Database Program (SOTWIS), and the Digital Soil Map of the World (DSMW).Percentage of Mapping Unit covered by dominant componentObstacles to Roots - Depth to obstacles to roots in 6 classes. Only in the European Soil Database, not available for other regions.More information on the Harmonized World Soil Database is available here.Other layers created from the Harmonized World Soil Database are available on ArcGIS Online:World Soils Harmonized World Soil Database – ChemistryWorld Soils Harmonized World Soil Database - Exchange CapacityWorld Soils Harmonized World Soil Database – GeneralWorld Soils Harmonized World Soil Database – HydricWorld Soils Harmonized World Soil Database – TextureThe authors of this data set request that projects using these data include the following citation:FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Living Atlas Discussion GroupSoil Data Discussion GroupThe Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

AUSTIN POLICE DEPARTMENT DATA DISCLAIMER Please read and understand the following information. This dataset contains a record of incidents that the Austin Police Department responded to and wrote a report. Please note one incident may have several offenses associated with it, but this dataset only depicts the highest level offense of that incident. Data is from Report On dates of January 1 through December 31, 2021. This dataset is updated weekly. Understanding the following conditions will allow you to get the most out of the data provided. Due to the methodological differences in data collection, different data sources may produce different results. This database is updated weekly, and a similar or same search done on different dates can produce different results. Comparisons should not be made between numbers generated with this database to any other official police reports. Data provided represents only calls for police service where a report was written. Totals in the database may vary considerably from official totals following investigation and final categorization. Therefore, the data should not be used for comparisons with Uniform Crime Report statistics. The Austin Police Department does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided. Pursuant to section 552.301 (c) of the Government Code, the City of Austin has designated certain addresses to receive requests for public information sent by electronic mail. For requests seeking public records held by the Austin Police Department, please submit by utilizing the following link: https://apd-austintx.govqa.us/WEBAPP/_rs/(S(0auyup1oiorznxkwim1a1vpj))/supporthome.aspx

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 …Show full descriptionAbstract 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. Asset database for the Hunter subregion on 24 February 2016 (V2.5) supersedes the previous version of the HUN Asset database V2.4 (Asset database for the Hunter subregion on 20 November 2015, GUID: 0bbcd7f6-2d09-418c-9549-8cbd9520ce18). It contains the Asset database (HUN_asset_database_20160224.mdb), a Geodatabase version for GIS mapping purposes (HUN_asset_database_20160224_GISOnly.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20160224.xlsx), a data dictionary (HUN_asset_database_doc_20160224.doc), and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below. This version should be used for Materiality Test (M2) test. The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separate file geodatabase joined by AID/ElementID. 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. A report on the WAIT process for the Hunter is 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 Hunter subregion are found in the "AssetList" table of the database. 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 "HUN_asset_database_doc_20160224.doc ", located in this filet. 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 "HUN_asset_database_doc_20160224.doc" located in this file. Some of the source data used in the compilation of this dataset is restricted. The public version of this asset database can be accessed via the following dataset: Asset database for the Hunter subregion on 24 February 2016 Public 20170112 v02 (https://data.gov.au/data/dataset/9d16592c-543b-42d9-a1f4-0f6d70b9ffe7) Dataset History OBJECTID VersionID Notes Date_ 1 1 Initial database. 29/08/2014 3 1.1 Update the classification for seven identical assets from Gloucester subregion 16/09/2014 4 1.2 Added in NSW GDEs from Hunter - Central Rivers GDE mapping from NSW DPI (50 635 polygons). 28/01/2015 5 1.3 New AIDs assiged to NSW GDE assets (Existing AID + 20000) to avoid duplication of AIDs assigned in other databases. 12/02/2015 6 1.4 "(1) Add 20 additional datasets required by HUN assessment project team after HUN community workshop (2) Turn off previous GW point assets (AIDs from 7717-7810 inclusive) (3) Turn off new GW point asset (AID: 0) (4) Assets (AIDs: 8023-8026) are duplicated to 4 assets (AID: 4747,4745,4744,4743 respectively) in NAM subregion . Their AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that NAM assets. (5) Asset (AID 8595) is duplicated to 1 asset ( AID 57) in GLO subregion . Its AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that GLO assets. (6) 39 assets (AID from 2969 to 5040) are from NAM Asset database and their attributes were updated to use the latest attributes from NAM asset database (7)The databases, especially spatial database, were changed such as duplicated attributes fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to the spatial data" 16/06/2015 7 2 "(1) Updated 131 new GW point assets with previous AID and some of them may include different element number due to the change of 77 FTypes requested by Hunter assessment project team (2) Added 104 EPBC assets, which were assessed and excluded by ERIN (3) Merged 30 Darling Hardyhead assets to one (asset AID 60140) and deleted another 29 (4) Turned off 5 assets from community workshop (60358 - 60362) as they are duplicated to 5 assets from 104 EPBC excluded assets (5) Updated M2 test results (6) Asset Names (AID: 4743 and 4747) were changed as requested by Hunter assessment project team (4 lower cases to 4 upper case only). Those two assets are from Namoi asset database and their asset names may not match with original names in Namoi asset database. (7)One NSW WSP asset (AID: 60814) was added in as requested by Hunter assessment project team. The process method (without considering 1:M relation) for this asset is not robust and is different to other NSW WSP assets. It should NOT use for other subregions. (8) Queries of Find_All_Used_Assets and Find_All_WD_Assets in the asset database can be used to extract all used assts and all water dependant assts" 20/07/2015 8 2.1 "(1) There are following six assets (in Hun subregion), which is same as 6 assets in GIP subregion. Their AID, Asset Name, Group, SubGroup, Depth, Source and ListDate are using values from GIP assets. You will not see AIDs from AID_from_HUN in whole HUN asset datable and spreadsheet anymore and you only can see AIDs from AID_from_GIP ( Actually (a) AID 11636 is GIP got from MBC (B) only AID, Asset Name and ListDate are different and changed) (2) For BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx, (a) Extracted long ( >255 characters) WD rationale for 19 assets (AIDs: 8682,9065,9073,9087,9088,9100,9102,9103,60000,60001,60792,60793,60801,60713,60739,60751,60764,60774,60812 ) in tab "Water-dependent asset register" and 37 assets (AIDs: 5040,8651,8677,8682,8650,8686,8687,8718,8762,9094,9065,9067,9073,9077,9081,9086,9087,9088,9100,9102,9103,60000,60001,60739,60742,60751,60713,60764,60771, 60774,60792,60793,60798,60801,60809,60811,60812) in tab "Asset list" in 1.30 Excel file (b) recreated draft BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx (3) Modified queries (Find_All_Asset_List and Find_Waterdependent_asset_register) for (2)(a)" 27/08/2015 9 2.2 "(1) Updated M2 results from the internal review for 386 Sociocultural assets (2)Updated the class to Ecological/Vegetation/Habitat (potential species distribution) for assets/elements from sources of WAIT_ALA_ERIN, NSW_TSEC, NSW_DPI_Fisheries_DarlingHardyhead" 8/09/2015 10 2.3 "(1) Updated M2 results from the internal review * Changed "Assessment team do not say No" to "All economic assets are by definition water dependent" * Changed "Assessment team say No" : to "These are water dependent, but excluded by the project team based on intersection with the PAE is negligible" * Changed "Rivertyles" to "RiverStyles"" 22/09/2015 11 2.4 "(1) Updated M2 test results for 86 assets from the external review (2) Updated asset names for two assets (AID: 8642 and 8643) required from the external review (3) Created Draft Water Dependent Asset Register file using the template V5" 20/11/2015 12 2.5 "Total number of registered water assets was increased by 1 (= +2-1) due to: Two assets changed M2 test from "No" to "Yes" , but one asset assets changed M2 test from "Yes" to "No" from the review done by Ecologist group." 24/02/2016 Dataset Citation Bioregional Assessment Programme (2015) Asset database for the Hunter subregion on 24 February 2016. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/a39290ac-3925-4abc-9ecb-b91e911f008f. Dataset Ancestors Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514 Derived From Travelling Stock Route Conservation Values Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From NSW Wetlands Derived From Climate Change Corridors Coastal North East NSW Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Climate Change Corridors for Nandewar and New England Tablelands Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Hunter subregion on 27 August 2015 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Hunter CMA GDEs (DRAFT DPI pre-release) Derived From Camerons Gorge Grassy White Box Endangered Ecological Community (EEC) 2008 Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516 Derived From Fauna Corridors for North East NSW Derived From Asset database for the Hunter subregion on 12 February 2015 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases,

The activities of a diverse array of sediment-dwelling fauna are known to mediate carbon remineralisation, biogeochemical cycling and other important properties of marine ecosystems, but the contributions that different seabed communities make to the global inventory have not been established. Here we provide a comprehensive georeferenced database of measured values of bioturbation intensity (Db), burrow ventilation rate (42 species) and the mixed depth (L) of marine soft sediments compiled from the scientific literature (1970-2017). These data provide reference information that can be used to inform and parameterise global, habitat specific and/or species level biogeochemical models that will be of value within the fields of geochemistry, ecology, climate, and palaeobiology. We include metadata relating to the source, timing and location of each study, the methodology used, and environmental and experimental information. The dataset presents opportunity to interrogate current ecological theory, refine functional typologies, quantify uncertainty and/or test the relevance and robustness of models used to project ecosystem responses to change.

Abstract

This dataset was derived from data provided by the NSW Office of Water. You can find a link to the source dataset in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

The difference between NSW Office of Water GW licences - GLO v3 and v4 is that an unique ID has been added to the dataset. This is so it can be ingested into the asset database.

The aim of this dataset was to be able to map each groundwater works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use.

This has not been clipped to the Gloucester PAE, therefore the number of economic assets/ relevant licences will drastically reduce once this occurs.

Dataset History

The difference between NSW Office of Water GW licences - GLO v3 and v4 is that an unique ID has been added to the dataset. This is so it can be ingested into the asset database.

Data has not been clipped to the PAE.

No volume has been included for domestic & stock as it is a basic right. Therefore an arbitrary volume could be applied to account for D&S use.

Dataset Citation

Bioregional Assessment Programme (2014) NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/98341388-5705-4c42-9f7a-186e16647614.

Dataset Ancestors

• Derived From NSW Office of Water Groundwater Entitlements Spatial Locations

• Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014

• Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014

INFORMATION ABOUT THE CONTENT OF THIS DATABASE

This database comprises wave, beach and runup parameters measured on different beaches around the world. It is a compilation of data published in previous works, with the aim of making all data available in one single repository. More information about methods of data acquisition and data processing can be found in the original papers that describe each experiment. To know how to cite each of the dataset provided here, please check section 3. Please make sure to cite the appropriate publication when using the data. Collecting the data is hard work and needs to be acknowledged. 1. Files content: All data files contain the same structure: Column 1 – R2%: 2-percent exceedance value for runup [m]; Column 2 – Set: setup [m]; Column 3 – Stt: total swash excursion [m]; Column 4 – Sinc: incident swash [m]; Column 5 – Sig: infragravity swash [m]; Column 6 – Hs*: significant deep-water wave height [m]; Column 7 – Tp: peak wave period [s]; Column 8 – tanβ: foreshore beach slope; Column 9 – D50**: Median sediment size [mm] NaN values may be found when the data were not available in the original dataset. *Hs values from field measurements were deshoaled from the depth of measurement to a depth equals to 80m, assuming normal approach and linear theory (we followed the approach presented in Stockdon et al., where great care is paid to make the data comparable). **D50 values were obtained from reports and papers describing the beaches. 2. List of datasets Stockdon et al. 2006: Data recompiled from 10 experiments carried out in 6 beaches (US and NL coasts). Files’ names correspond to the beach and year of the experiments: Original data: available using the link https://pubs.usgs.gov/ds/602/ Senechal et al. 2011: This dataset comprises the measurements carried out in Truc Vert beach, France. The file’s name includes the name of the beach and the year of the experiment. Original data: a table with the full content of the parameters measured during the experiment can be found in Senechal et al. (2011). Guedes et al. 2011: This dataset comprehends data measured at Tairua beach (New Zeland coast). The file’s name indicates the name of the beach and the year of the experiment. Original data: this web. Guedes et al. 2013: This dataset comprehends data measured at Ngarunui beach (Raglan - New Zeland coast). The file’s name represents the name of the beach and the year of the experiment. Original data: this web. Gomes da Silva et al. 2018: Dataset measured during two field campaigns in Somo beach, Spain, in 2016 and 2017. The files names represent that name of the beach and the year of the experiment. Original data: https://data.mendeley.com/datasets/6yh2b327gd/4

Power et al. 2019: Dataset compiled from previous works, comprising field and laboratory measurements: Poate et al. (2016): field; Nicolae-Lerma et al. (2016): field; Atkinson et al. (2017): field; Mase (1989): Laboratory; Baldock and Huntley (2002): Laboratory; Howe (2016): Laboratory; Original data:www.sciencedirect.com/science/article/pii/S0378383918302552

Due to the character limit of this description, please refer to the https://coastalhub.science/wave-runup-read-me for the references list.

In 2023, the number of data compromises in the United States stood at 3,205 cases. Meanwhile, over 353 million individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2022, healthcare, financial services, and manufacturing were the three industry sectors that recorded most data breaches. The number of healthcare data breaches in the United States has gradually increased within the past few years. In the financial sector, data compromises increased almost twice between 2020 and 2022, while manufacturing saw an increase of more than three times in data compromise incidents. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

This dataset comes from the Open National Address Base project initiated by OpenStreetMap France.

For more information on this project: http://openstreetmap.fr/blogs/cquest/bano-banco

Origin of data

• OpenStreetMap
• data available in opendata
• address data collected on the cadastral site (source DGFiP 2014)

Distribution format

These files are available in shapefile format, in WGS84 projection (EPSG :4326) as well as in CSV format and experimentally as github project.

Description of content

For each address:

• id (unique): code_insee + codefantoir + number
• number: street number with suffix (e.g.: 1, 1BIS, 1D)
• street: street name
• post_code: 5-character postcode
• city: name of the municipality
• source: OSM = data directly from OpenStreetMap, OD = data from local opendata sources, CAD = data directly from the cadastre, C+O = cadastre data enriched by OSM (road name for example)
• lat: latitude in WGS84 decimal degrees
• lon: longitude in WGS84 decimal degrees

updates, corrections

To update and correct BANO data, simply make improvements directly in OpenStreetMap, they will be taken into account in the next update cycle.

A one-stop collaborative reporting/correction window will soon be set up to simplify the process of improving the content of the database. To participate in its co-construction, do not hesitate to contact us!

For any questions concerning the project or this dataset, you can contact bano@openstreetmap.fr

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Contains the output grids used to generate the Modflow alluvium exceedance probability maps for the Gloucester subregion. The dataset represents the maximum drawdown under baseline conditions, under the coal resource development pathway (CRDP) conditions and the difference between both, the additional coal resource development (ACRD).

This is described in product 2.6.2 Groundwater numerical modelling (Peeters et al. 2016).

Peeters L, Dawes W, Rachakonda P, Pagendam D, Singh R, Pickett T, Frery E, Marvanek S, and McVicar T (2016) Groundwater numerical modelling for the Gloucester subregion. Product 2.6.2 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia.

Dataset History

The dataset is derived from outputs from the Modflow model and uncertainty analysis performed for the Gloucester subregion. Model outputs were computed to ESRI format grids for map production.

This is described in product 2.6.2 Groundwater numerical modelling (Peeters et al. 2016).

Peeters L, Dawes W, Rachakonda P, Pagendam D, Singh R, Pickett T, Frery E, Marvanek S, and McVicar T (2016) Groundwater numerical modelling for the Gloucester subregion. Product 2.6.2 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia.

Dataset Citation

Bioregional Assessment Programme (XXXX) GLO MF Alluvium exceedance probabilites v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/0039f296-c7c7-410d-bb37-a1f4b1eb5976.

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 Groundwater Economic Assets GLO 20150326

• 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 Geology 100K NSW selected mapsheets for Sydney Bioregion

• Derived From GLO Geological Model Extracted Horizons Final Grid XYZ V01

• 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 BA SYD GLO - AGL Avon Wards River alluvium extent v1

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

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

• Derived From GLO MFdmax v01

• Derived From R-scripts for uncertainty analysis v01

• Derived From New South Wales 2 kilometers Residential Exclusions Zone

• Derived From Geofabric Surface Cartography - V2.1

• Derived From BA SYD selected GA TOPO 250K data plus added map features

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

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

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

• Derived From EIS Gloucester Coal 2010

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

• Derived From Geological Maps Combined for NSW

• Derived From GEODATA TOPO 250K Series 3

• Derived From Asset database for the Gloucester subregion on 28 May 2015

• Derived From Gloucester Deep Wells Completion Reports - Geology

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014

• Derived From EIS for Rocky Hill Coal Project 2013

• Derived From GLO AEM dmax v01

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

• Derived From Asset database for the Gloucester subregion on 8 April 2015

• Derived From GLO DEM 1sec SRTM MGA56

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

• Derived From GLO RMS Model Depth Structure Eroded v01

• Derived From Asset database for the Gloucester subregion on 29 August 2014

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

• Derived From Groundwater Modelling Report for Stratford Coal Mine

• Derived From AGL Gloucester Gas Project AECOM report location map features

• Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public)

• Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013

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

• Derived From GLO Deep Well Locations and Depths of Formations V01

• Derived From GLO MF Model v02

• Derived From Freshwater Fish Biodiversity Hotspots

• Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014

• Derived From GLO AEM Model v02

• Derived From Australia - Species of National Environmental Significance Database

• Derived From Gloucester - Additional assets from local councils

• Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal

• Derived From Avon Wards River alluvium v03

• Derived From [NSW Office of Water Groundwater Entitlements

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data used to produce the predicted Total Dissolved Solids map for the Hutton Aquifer and equivalents in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014).

There are four layers in the Hutton Aquifer and equivalents Total Dissolved Solids map data

A. Location of hydrochemistry samples (Point data, Shapefile)

B. Predicted Concentration (Filled contours , Shapefile)

C. Predicted Concentration Contours (Contours, Shapefile)

D. Prediction Standard Error (Filled contours , Shapefile)

The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map.

The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data.

Concentrations are Total Dissolved Solids mg/L.

Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude.

The Hutton Aquifer and equivalents Total Dissolved Solids map is one of four hydrochemistry maps for the Hutton Aquifer and equivalents and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014).

This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81709.

\t

References:

Hitchon, B. and Brulotte, M. (1994): Culling criteria for ‘standard’ formation water analyses; Applied Geochemistry, v. 9, p. 637–645

Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. \[available from www.ga.gov.au using catalogue number 79790\]

Dataset History

SOURCE DATA:

Data was obtained from a variety of sources, as listed below:

1.\tWater quality data from the Queensland groundwater database, Department of Environment and Resource Management

2.\tGeological Society of Queensland water chemistry database (1970s to 1980s). Muller, PJ, Dale, NM (1985) Storage System for Groundwater Data Held by the Geological Survey of Queensland. GSQ Record 1985/47. Queensland.

3.\tGeoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p.

4.\tFeitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia

5.\tWater quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government

6.\tGeoscience Australia (2010) Hydrogeochemical collection. A compilation of quality controlled groundwater data taken from well completion reports from QLD and NSW.

7.\tWater quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government

BOUNDARIES:

Data covers the extent of the Hutton Aquifer and equivalents as defined in Great Artesian Basin - Hutton Aquifer and equivalents - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81682).

METHOD:

Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries and clipped further where there was no data within 100 km.

QAQC:

Prior to data analysis all hydrochemistry data was assessed for reliability by Quality Assurance/Quality Control (QA/QC) procedures. A data audit and verification were performed using various quality checking procedures including identification and verification of outliers.

The ionic balance of each analysis was checked, and where the ionic charge balance differed by greater than 10%, these analyses were deemed unacceptable and were not considered for future analysis.

Data that passed the initial QA/QC procedures were checked against borehole construction and stratigraphic records to determine aquifer intercepts. Data were discarded in cases where there was no recorded location information or screen interval/depth information (to cross reference with borehole stratigraphy).

Groundwater chemistry data was sourced from multiple studies, government databases, and companies. Many of the studies used sub-sets of the same data. All duplicates were removed before mapping and analysis. The differences between data sources had to be reconciled to ensure that maximum value of the data was retained and for errors in the transcription to be avoided. This precluded any automated processing system. Random checks were routinely made against the source data to ensure quality of the process. Some source data was in the form of thousands of consecutive rows and required python scripts or detailed table manipulations to correctly re-format the information and re-produce records with all the well data, its location and hydrochemical data for a particular sample date on one row in the collated Excel spreadsheet. Alkalinity measurements, in particular, were often reported differently between studies and even within the same database and required conversion to a common unit. All data before 1960 was discarded.

The study uses a data collection compiled from petroleum well completion reports from QLD and NSW. This data underwent a thorough QC process to ensure that drilling mud contaminated samples were excluded, based on the procedure described by Hitchon, B. & Brulotte, M. (1994). Less than 5% of the samples compiled passed the QC procedure, but these provide invaluable insight into the chemistry of very deep parts of the aquifers (typically 1 - 2km deep).

Where multiple samples have been taken at the same well, an average of the analyses was used in the kriging but outliers were removed. Outliers were identified by looking for large differences between predicted and measured samples. Excessively high values compared to predicted values and typical measurements at the same bore were discarded.

Dataset Citation

Geoscience Australia (2015) Hutton Aquifer and equivalents Total Dissolved Solids map: Data. Bioregional Assessment Source Dataset. Viewed 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/f5f16389-d97e-46b3-bd43-83255acf257d.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Data used to produce the predicted Total Dissolved Solids map for the Cadna-owie - Hooray Aquifer in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014).

There are four layers in the Cadna-owie - Hooray Aquifer Total Dissolved Solids map data

A. Location of hydrochemistry samples (Point data, Shapefile)

B. Predicted Concentration (Filled contours , Shapefile)

C. Predicted Concentration Contours (Contours, Shapefile)

D. Prediction Standard Error (Filled contours , Shapefile)

The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map.

The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data.

Concentrations are Total Dissolved Solids mg/L.

Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude.

The Cadna-owie - Hooray Aquifer Total Dissolved Solids map is one of 14 hydrochemistry maps for the Cadna-owie - Hooray Aquifer and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et. al., 2014).

This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81693.

\t

References:

Hitchon, B. and Brulotte, M. (1994): Culling criteria for ‘standard’ formation water analyses; Applied Geochemistry, v. 9, p. 637–645

Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. \[available from www.ga.gov.au using catalogue number 79790\]

Dataset History

SOURCE DATA:

Data was obtained from a variety of sources, as listed below:

1.\tWater quality data from the Queensland groundwater database, Department of Environment and Resource Management

2.\tGeological Society of Queensland water chemistry database (1970s to 1980s). Muller, PJ, Dale, NM (1985) Storage System for Groundwater Data Held by the Geological Survey of Queensland. GSQ Record 1985/47. Queensland.

3.\tGeoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p.

4.\tFeitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia

5.\tWater quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government

6.\tGeoscience Australia (2010) Hydrogeochemical collection. A compilation of quality controlled groundwater data taken from well completion reports from QLD and NSW.

7.\tWater quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government

BOUNDARIES:

Data covers the extent of the Cadna-owie-Hooray Aquifer and Equivalents as defined in Great Artesian Basin - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81678)

METHOD:

Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries.

QAQC:

Prior to data analysis all hydrochemistry data was assessed for reliability by Quality Assurance/Quality Control (QA/QC) procedures. A data audit and verification were performed using various quality checking procedures including identification and verification of outliers.

The ionic balance of each analysis was checked, and where the ionic charge balance differed by greater than 10%, these analyses were deemed unacceptable and were not considered for future analysis.

Data that passed the initial QA/QC procedures were checked against borehole construction and stratigraphic records to determine aquifer intercepts. Data were discarded in cases where there was no recorded location information or screen interval/depth information (to cross reference with borehole stratigraphy). One exception was chemistry data obtained from the NSW Governments Triton database. Groundwater chemistry data obtained from bore records in the Triton database that was also identified as GAB bores in the NSW Governments Pinneena database were assumed to be in the Pilliga Sandstone and were allocated to the Cadna-owie Hooray equivalent aquifer, despite many not recording depth information.

Groundwater chemistry data was sourced from multiple studies, government databases, and companies. Many of the studies used sub-sets of the same data. All duplicates were removed before mapping and analysis. The differences between data sources had to be reconciled to ensure that maximum value of the data was retained and for errors in the transcription to be avoided. This precluded any automated processing system. Random checks were routinely made against the source data to ensure quality of the process. Some source data was in the form of thousands of consecutive rows and required python scripts or detailed table manipulations to correctly re-format the information and re-produce records with all the well data, its location and hydrochemical data for a particular sample date on one row in the collated Excel spreadsheet. Alkalinity measurements, in particular, were often reported differently between studies and even within the same database and required conversion to a common unit. All data before 1960 was discarded.

The study uses a data collection compiled from petroleum well completion reports from QLD and NSW. This data underwent a thorough QC process to ensure that drilling mud contaminated samples were excluded, based on the procedure described by Hitchon, B. & Brulotte, M. (1994). Less than 5% of the samples compiled passed the QC procedure, but these provide invaluable insight into the chemistry of very deep parts of the aquifers (typically 1 - 2km deep).

Where multiple samples have been taken at the same well, an average of the analyses was used in the kriging but outliers were removed. Outliers were identified by looking for large differences between predicted and measured samples. Excessively high values compared to predicted values and typical measurements at the same bore were discarded.

Dataset Citation

Geoscience Australia (2015) GABATLAS - Cadna-owie - Hooray Aquifer Total Dissolved Solids map: Data. Bioregional Assessment Source Dataset. Viewed 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/5044a067-35d1-4d6d-98a6-17974aa9226a.

Porosity and permeability data form part of Geoscience Australia’s Reservoir, Facies and Shows (RESFACS) database, which contains depth-based information regarding porosity and permeability measured or interpreted from core, sidewall core and well-log analysis of rocks intersected by offshore petroleum wells. Porosity and permeability are rock properties related to the number, size, and connectivity of openings in the rock. More specifically, porosity of a rock is a measure of its ability to hold a fluid within pore-spaces and the permeability is a measure of the ease of flow of a fluid through a porous solid. Data entered into the porosity and permeability tables are primarily sourced from the Basic and Interpretive volumes of Well Completion Reports (WCR) provided by the petroleum industry to the Commonwealth under the Offshore Petroleum and Greenhouse Gas Storage Act (OPGGSA) 2006 and the previous Petroleum (submerged Lands) Act (PSLA) 1967. Data is also sourced from sedimentologic evaluations and petrophysical studies by Geoscience Australia and its predecessor organisations, the Australian Geological Survey Organisation (AGSO) and the Bureau of Mineral Resources (BMR), as well as from state and territory geological organisations, and scientific publications. The database structure has evolved over time and will keep changing as different types of relevant data become available and the delivery platform changes. Data hosted within Geoscience Australia’s Oracle petroleum wells database was initially delivered through the Petroleum Wells web page, http://dbforms.ga.gov.au/www/npm.well.search, which is in the process of being decommissioned . The porosity and permeability data will now be available to view and download through the Geoscience Australia Portal Core, https://portal.ga.gov.au/. Use Porosity and Permeability as your search term to find the relevant data.

This dataset is downloaded from the Library Search and Compare dashboard on the IMLS website. Data is divided by years. Each row of the spreadsheet is an individual library. Indicators covered include visits, funding, resources/databases, programs and staff.

The records in this dataset are general marine and coastal records of different taxonomic groups submitted to the National Biodiversity Data Centre.

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 dataset contains the aquifer assignment results for the Queensland part of the Clarence-Moreton Basin. The data were organized by hydrostratigraphic units. Assigning a bore to a specific aquifer is underpinned by the screened interval data and the aquifer boundaries. In many cases, it is impossible to assign the screened interval of a bore to a single aquifer as bores are either screened across different aquifers or there is insufficient information on stratigraphy and screened intervals. Bores were assigned to aquifers by comparing their screen intervals and depth with aquifer boundary data. The required information was extracted from the "Casing", "Aquifer", and "Stratigraphy" tables of the DNRM database.

Dataset History

The following steps were followed during the aquifer assignment:

1. Determine the boundary of the aquifer of interest. The 'Aquifer' table in the DNRM database registers aquifers that a bore intersects when it is drilled and records the upper and lower extents of aquifers. This information was used to identify the aquifer boundary at any specific location. When boundary information was missing the 'Stratigraphy' table was used to identify aquifer boundaries instead.

   Determine the screen interval of bores. Refer to theThe 'Casing' table contains the screen information for most bores in the database. The codes 'PERF', 'SCRN' and 'ENDD' in the column 'MATERIAL' indicate water entry locations. The code 'OPEN' indicates that a bore is uncased at some depths; if bores intersect an aquifer, then they are considered as water supply points. These codes were used to find the screen interval of a bore. When multiple screens exist, the bore is assumed to be screened across the entire length of the individual screens.

2. Determine the screen code. A bore may tap into an aquifer in four ways depending on its screen location in aquifers. Four codes (I, T, B and E) were used to indicate the different spatial relationship of a bore with its targeted aquifer. When screen information is lacking, bores with their lower ends located in an aquifer are assumed to be tapped to that aquifer and were assigned a screen code 'BOI'.

3. Filter bores for a specific area using a shape file or coordinates. If only a part of the aquifer is of interest, then the output bores can be filtered based on their locations.

4. Cross-check the final datasets against expert knowledge and spatial context of aquifers. As errors are common in such databases, some errors will still persist despite extensive data quality checks. However, such errors are often highlighted during data interpretation and visual representation and can subsequently be corrected through an iterative process.

Dataset Citation

Bioregional Assessment Programme (2014) CLM - Bore assignments QLD. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/f8937dd8-b3a0-490e-a452-9dc56fe03914.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014

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

2484 Global exporters importers export import shipment records of Dummy controller with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains extractions of selected Assest and Elements formatted/manipulated to facilitate requested presentation in report maps. Manipulations include principally a) clipping selected element/asset polygons to the SSB PAE and extracting centroids of small element/asset polygons to enable representation on small scale report maps with point marker symbols. Purpose To enable customised symbolisation on BA report map images Dataset History Selected economic elements were taken from the source dataset and geometry changed to facilitate representation in report maps. Ie polygon elements too small to be discernible at the report map scale had their polygon centroids extracted so they could be symbolised with point marker symbols. Also the location of Thompson's Creek dam was located in Google earth and this was added as a feature to the existing dam assets as the authour wanted this shown even though it was not included in this version of the Assets database. Dataset Citation Bioregional Assessment Programme (2016) SSB Economic asset mapping 20160118. Bioregional Assessment Derived Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/1da97a0f-6be6-4550-9c40-d73241c3df79. Dataset Ancestors Derived From Asset database for the Sydney Basin bioregion on 18 December 2015 Derived From NSW Wetlands Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From Travelling Stock Route Conservation Values Derived From State Environmental Planning Policy no. 26 - Littoral Rainforest 19860101 Derived From Surface Water Entitlements in Sydney sliver between different PAEs NSW Office of Water 20150717 Derived From Geofabric Surface Network - V2.1 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From SSB Preliminary Assessment Extent inputs draft Derived From Groundwater Entitlement NSW Office of Water 20150526 PersCom removed Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Bioregional Assessment areas v04 Derived From Key Environmental Assets - KEA - of the Murray Darling Basin Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From Gippsland Project boundary Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Natural Resource Management (NRM) Regions 2010 Derived From SSB Preliminary Assessment Extent v01 Derived From Sydney Catchment Authority Water Licences and Approvals Package May 2012 Derived From GW Economic Elements Sydney Basin 20150730 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From NSW Office of Water SW Offtakes Processed - North & South Sydney, v3 12032014 Derived From Operating Coal Mines in New South Wales as on 24 July 2013 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Threatened migratory shorebird habitat mapping DECCW May 2006 Derived From Native Vegetation Management (NVM) - Manage Benefits Derived From GEODATA TOPO 250K Series 3 Derived From Old Growth Forest Mapping Broad, Central, 1996. VIS_ID 4122 2015 20150116 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Macquarie Perch NSW DPI Fisheries 20150313 Derived From Geological Provinces - Full Extent Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Commonwealth Heritage List Spatial Database (CHL) Derived From Cumberland Subregion BIO Map Biodiversity Corridors of Regional Significance 20150804 Derived From NSW SW Share Components NSW Office of Water 20150717 Derived From An Estuarine Inventory for New South Wales, Australia VIS_ID 2224 20100723 Derived From Atlas of Living Australia NSW ALA Portal 20140613 Derived From Bioregional Assessment areas v03 Derived From Purple Spotted Gudgeon NSW DPI Fisheries 20150317 Derived From Identification of Culturally Significant Groundwater Dependent Ecosystems CSIRO 2010 Derived From Southeast NSW Native Vegetation Classification and Mapping - SCIVI VIS_ID 2230 20030101 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From State Environmental Planning Policy no. 14 - Coastal Wetlands 19891027 Derived From NSW Office of Water Surface Water Offtakes - North & South Sydney v1 24102013 Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From SW Economic Elements Sydney Basin 20150730 Derived From Australia World Heritage Areas Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Illawarra Region BIO Map - Core Areas 20150430 Derived From NSW Office of Water identified GDEs Derived From NSW Office of Water SW Offtakes Processed - North & South Sydney, v2 07032014 Derived From National Groundwater Management Zones BOM 20150730 Derived From Illawarra Region BIO Map Corridors 20150430 Derived From Cumberland subregion BIO Map Core Areas 20150804 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Fitzroy Falls Spiny Crayfish NSW DPI Fisheries 20150316 Derived From Australia - Species of National Environmental Significance Database Derived From Map of Critically Endangered Ecological Communities NSW Version 3 20150925 Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From Victoria - Seamless Geology 2014 Derived From Asset database for the Sydney Basin bioregion on 03 August 2015 Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From NSW Wild Rivers Office of Environment and Heritage (OEH) 20091001

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies. The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.