The Gross Domestic Product (GDP) in Australia was worth 1728.06 billion US dollars in 2023, according to official data from the World Bank. The GDP value of Australia represents 1.64 percent of the world economy. This dataset provides - Australia GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data. \r \r From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.\r \r G-NAF Core will be updated on a quarterly basis along with G-NAF.\r \r Further information about contributors to G-NAF is available here.\r \r With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.\r \r Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here\r \r Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.\r \r Changes in the February 2025 release\r \r * Nationally, the February 2025 update of G-NAF shows an overall increase of 47,284 addresses (0.30%). The total number of addresses in G-NAF now stands at 15,706,733 of which 14,867,032 or 94.65% are principal.\r \r * In the February 2025 release of G-NAF, over 300 addresses in Morra, Western Australia have been updated. About 150 addresses have changed locations and 160 properties now have street numbers instead of lot numbers. Some properties are still using lot-numbers, resulting in two addressees. This issue will be resolved in the May 2025 update of G-NAF.\r \r * In the February release, Geoscape has re-classified geocode types of ‘Property Access Point Setback’ (PAPS) to be ‘Property Access Point’ (PAP) in South Australia where the geocode falls within a road casement as the geocode is not set back into a land parcel. This update has changed approximately 57,000 geocodes to PAP from their previous classification of PAPS, while there are some 14,000 PAPS geocodes that remain unchanged.\r \r \r * Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.\r \r Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.\r \r Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.\r \r

License Information\r

\r Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)\r \r The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added. \r \r The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.\r \r End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).\r \r Users must also note the following attribution requirements:\r \r Preferred attribution for the Licensed Material:\r \r

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.\r \r Preferred attribution for Adapted Material:\r \r Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.\r \r

What to Expect When You Download G-NAF\r

\r G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.\r \r

Money Supply M0 in Australia increased to 364.95 AUD Billion in January from 352.50 AUD Billion in December of 2024. This dataset provides - Australia Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Update March 2025### From 20 March 2025, the dataset update frequency has change from monthly to weekly every Thursday. ###Update April 2022### We have replaced the .xlsx file resources for all …Show full description###Update March 2025### From 20 March 2025, the dataset update frequency has change from monthly to weekly every Thursday. ###Update April 2022### We have replaced the .xlsx file resources for all our datasets. This was required due to the API and web page search functionality no longer being supported for .xlsx files on the Data.Gov platform. ###Update April 2019 - addition of license authorisations to Credit Licensee dataset ### From 4 April 2019, the Credit Licensee dataset will include license authorisations. See the updated helpfile for further information. ###Dataset summary### ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and wellbeing by ensuring that Australia's financial markets are fair and transparent, and supported by confident and informed investors and consumers. Australian Credit Licensees are required to maintain their details on ASIC's registers. Information contained on the Credit Licensee Register is made available to the public to search via the ASIC Connect website. Selected data from the register will be uploaded each week to www.data.gov.au. The data made available will be a snapshot of the register at a point in time. Legislation prescribes the type of information ASIC is allowed to disclose to the public. The information in the downloadable dataset includes: Register Name Credit licensee number Credit licensee name Date licence was issued Date licence was ceased (if applicable) Credit licensee status Credit licensee ABN or ACN(if applicable) AFSL number Credit licence status history Principal business locality Principal business state/territory Principal business postcode Latitude coordinates Longitude coordinates EDRS code Business name (if applicable) License authorisations Additional information about Credit Licensees can be found via ASIC's website. To view some information you may be charged a fee. More information about searching ASIC's registers.

Please Note: As announced by the Minister for Immigration and Border Protection on 25 June 2017, the Department of Immigration and Border Protection (DIBP) retired the paper-based Outgoing Passenger Cards (OPC) from 1 July 2017. The information previously gathered via paper-based outgoing passenger cards is now be collated from existing government data and will continue to be provided to users. Further information can be accessed here: http://www.minister.border.gov.au/peterdutton/Pages/removal-of-the-outgoing-passenger-card-jun17.aspx.

Due to the retirement of the OPC, the Australian Bureau of Statistics (ABS) undertook a review of the OAD data based on a new methodology. Further information on this revised methodology is available at: http://www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/3401.0Appendix2Jul%202017?opendocument&tabname=Notes&prodno=3401.0&issue=Jul%202017&num=&view=

A sampling methodology has been applied to this dataset. This method means that data will not replicate, exactly, data released by the ABS, but the differences should be negligible.

Due to ‘Return to Source’ limitations, data supplied to ABS from non-DIPB sources are also excluded.

Overseas Arrivals and Departures (OAD) data refers to the arrival and departure of Australian residents or overseas visitors, through Australian airports and sea ports, which have been recorded on incoming or outgoing passenger cards. OAD data describes the number of movements of travellers rather than the number of travellers. That is, multiple movements of individual persons during a given reference period are all counted. OAD data will differ from data derived from other sources, such as Migration Program Outcomes, Settlement Database or Visa Grant information. Travellers granted a visa in one year may not arrive until the following year, or may not travel to Australia at all. Some visas permit multiple entries to Australia, so travellers may enter Australia more than once on a visa. Settler Arrivals includes New Zealand citizens and other non-program settlers not included on the Settlement Database. The Settlement Database includes onshore processed grants not included in Settler Arrivals.

These de-identified statistics are periodically checked for privacy and other compliance requirements. The statistics were temporarily removed in March 2024 in response to a question about privacy within the emerging technological environment. Following a thorough review and risk assessment, the Department of Home Affairs has republished the dataset.

Government Spending in Australia increased to 147679 AUD Million in the fourth quarter of 2024 from 146700 AUD Million in the third quarter of 2024. This dataset provides - Australia Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Update March 2025 ### From 11 March 2025, the dataset will be updated to include 1 new field, Date of Deregistration, (see help file for details). ###Update August 2018 - frequency change to …Show full description###Update March 2025 ### From 11 March 2025, the dataset will be updated to include 1 new field, Date of Deregistration, (see help file for details). ###Update August 2018 - frequency change to Company dataset ### From 7 August 2018, the Company dataset will be updated weekly every Tuesday. As a result, the information might not be accurate at the time you check the Company dataset. ASIC-Connect updates information in real time, therefore, please consider accessing information on that platform if you need up to date information. ###Dataset summary### ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and wellbeing by ensuring that Australia’s financial markets are fair and transparent, supported by confident and informed investors and consumers. Australian companies are required to keep their details up to date on ASIC's Company Register. Information contained in the register is made available to the public to search via ASIC's website. Select data from the ASIC's Company Register will be uploaded each week to www.data.gov.au. The data made available will be a snapshot of the register at a point in time. Legislation prescribes the type of information ASIC is allowed to disclose to the public. The information included in the downloadable dataset is: Company Name Australian Company Number (ACN) Type Class Sub Class Status Date of Registration Date of Deregistration (Available from 11 March 2025) Previous State of Registration (where applicable) State Registration Number (where applicable) Modified since last report – flag to indicate if data has been modified since last report Current Name Indicator Australian Business Number (ABN) Current Name Current Name Start Date Additional information about companies can be found via ASIC's website. Accessing some information may attract a fee. More information about searching ASIC's registers.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Australia was 1,002 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Australia's Land Borders is a product within the Foundation Spatial Data Framework (FSDF) suite of datasets. It is endorsed by the ANZLIC - the Spatial Information Council and the Intergovernmental Committee on Surveying and Mapping (ICSM) as a nationally consistent and topologically correct representation of the land borders published by the Australian states and territories.

The purpose of this product is to provide: (i) a building block which enables development of other national datasets; (ii) integration with other geospatial frameworks in support of data analysis; and (iii) visualisation of these borders as cartographic depiction on a map. Although this dataset depicts land borders, it is not nor does it suggests to be a legal definition of these borders. Therefore it cannot and must not be used for those use-cases pertaining to legal context.

This product is constructed by Geoscience Australia (GA), on behalf of the ICSM, from authoritative open data published by the land mapping agencies in their respective Australian state and territory jurisdictions. Construction of a nationally consistent dataset required harmonisation and mediation of data issues at abutting land borders. In order to make informed and consistent determinations, other datasets were used as visual aid in determining which elements of published jurisdictional data to promote into the national product. These datasets include, but are not restricted to: (i) PSMA Australia's commercial products such as the cadastral (property) boundaries (CadLite) and Geocoded National Address File (GNAF); (ii) Esri's World Imagery and Imagery with Labels base maps; and (iii) Geoscience Australia's GEODATA TOPO 250K Series 3. Where practical, Land Borders do not cross cadastral boundaries and are logically consistent with addressing data in GNAF.

It is important to reaffirm that although third-party commercial datasets are used for validation, which is within remit of the licence agreement between PSMA and GA, no commercially licenced data has been promoted into the product. Australian Land Borders are constructed exclusively from published open data originating from state, territory and federal agencies.

This foundation dataset consists of edges (polylines) representing mediated segments of state and/or territory borders, connected at the nodes and terminated at the coastline defined as the Mean High Water Mark (MHWM) tidal boundary. These polylines are attributed to convey information about provenance of the source. It is envisaged that land borders will be topologically interoperable with the future national coastline dataset/s, currently being built through the ICSM coastline capture collaboration program. Topological interoperability will enable closure of land mass polygon, permitting spatial analysis operations such as vector overly, intersect, or raster map algebra. In addition to polylines, the product incorporates a number of well-known survey-monumented corners which have historical and cultural significance associated with the place name.

This foundation dataset is constructed from the best-available data, as published by relevant custodian in state and territory jurisdiction. It should be noted that some custodians - in particular the Northern Territory and New South Wales - have opted out or to rely on data from abutting jurisdiction as an agreed portrayal of their border. Accuracy and precision of land borders as depicted by spatial objects (features) may vary according to custodian specifications, although there is topological coherence across all the objects within this integrated product. The guaranteed minimum nominal scale for all use-cases, applying to complete spatial coverage of this product, is 1:25 000. In some areas the accuracy is much better and maybe approaching cadastre survey specification, however, this is an artefact of data assembly from disparate sources, rather than the product design. As the principle, no data was generalised or spatially degraded in the process of constructing this product.

Some use-cases for this product are: general digital and web map-making applications; a reference dataset to use for cartographic generalisation for a smaller-scale map applications; constraining geometric objects for revision and updates to the Mesh Blocks, the building blocks for the larger regions of the Australian Statistical Geography Standard (ASGS) framework; rapid resolution of cross-border data issues to enable construction and visual display of a common operating picture, etc.

This foundation dataset will be maintained at irregular intervals, for example if a state or territory jurisdiction decides to publish or republish their land borders. If there is a new version of this dataset, past version will be archived and information about the changes will be made available in the change log.

What is IPGOD?\r

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.\r \r \r

How do I use IPGOD?\r

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.\r \r \r

IP Data Platform\r

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform\r \r

References\r

\r The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.\r \r * Patents\r * Trade Marks\r * Designs\r * Plant Breeder’s Rights\r \r \r

Updates\r

\r

Tables and columns\r

\r Due to the changes in our systems, some tables have been affected.\r \r * We have added IPGOD 225 and IPGOD 325 to the dataset!\r * The IPGOD 206 table is not available this year.\r * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.\r \r

Data quality improvements\r

\r Data quality has been improved across all tables.\r \r * Null values are simply empty rather than '31/12/9999'.\r * All date columns are now in ISO format 'yyyy-mm-dd'.\r * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.\r * All tables are encoded in UTF-8.\r * All tables use the backslash \ as the escape character.\r * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

This dataset has been developed by the Australian Government as an authoritative source of indigenous location names across Australia. It is sponsored by the Spatial Policy Branch within the Department of Communications and managed solely by the Department of Human Services.

The dataset is designed to support the accurate positioning, consistent reporting, and effective delivery of Australian Government programs and services to indigenous locations.

The dataset contains Preferred and Alternate names for indigenous locations where Australian Government programs and services have been, are being, or may be provided. The Preferred name will always default to a State or Territory jurisdiction's gazetted name so the term 'preferred' does not infer that this is the locally known name for the location. Similarly, locational details are aligned, where possible, with those published in State and Territory registers.

This dataset is NOT a complete listing of all locations at which indigenous people reside. Town and city names are not included in the dataset. The dataset contains names that represent indigenous communities, outstations, defined indigenous areas within a town or city or locations where services have been provided.

This key layer is the index to the input datasets collated into the estimated Pre-1750 (pre-clearing, pre-European) theme of the NVIS Version 5.1 dataset (from which the NVIS Major Vegetation Groups …Show full descriptionThis key layer is the index to the input datasets collated into the estimated Pre-1750 (pre-clearing, pre-European) theme of the NVIS Version 5.1 dataset (from which the NVIS Major Vegetation Groups and Subgroups were derived). The layer is intended to document and describe the input datasets and assist in dynamic analyses of data quality and gaps in the NVIS Pre-1750 theme dataset. Important information in the spatial feature class includes the dataset spatial extent (i.e. its "footprint"), dataset number and the source of the underlying dataset. Where more than one dataset have been combined within a region (mainly NSW & NT), a complex of footprints is the result. A lookup table provides important information, such as dataset title, scale and the on-ground range of dates of the vegetation survey. Attributes in the lookup table NVIS5_1_LUT_KEY_DSETn (from the NVIS DATASET and VERSION_USAGE Oracle tables, where n = an incremental number for minor version updates) need to be attached to this spatial data via DATA_SET_NUMBER. See metadata for this LUT for more details.This data has been licensed under the Creative Commons Attribution 4.0 International Licence. More information can be found at https://creativecommons.org/licenses/by/4.0/ You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. CC - Attribution (CC BY) You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Suitable for understanding and mapping various NVIS data source and quality attributes. Applications involving futher use of the data may need to resolve the minor, but numerous, sliver and overlap issues. In an attempt to mitigate this issue, artificial vector layers were created for SA and ACT by cutting present native vegetation into the Carnahan estimated pre-1750 data for these areas. This assumes no change in vegetation type - just whether the type remains (i.e. hasn't been cleared). No vector estimated pre-1750 layers are available for NSW and Tasmania. However, estimated pre-1750 layers have been developed under the Major Vegetation Group and Major Vegetation Subgroup products, using the same logic as above. © Commonwealth of Australia (Department of the Environment and Energy) 2018

AbstractForests of Australia (2023) is a continental spatial dataset of forest extent, by national forest categories and types, assembled for Australia's State of the Forests Report. It was developed from multiple forest, vegetation and land cover data inputs, including contributions from Australian, state and territory government agencies and external sources.A forest is defined in this dataset as "An area, incorporating all living and non-living components, that is dominated by trees having usually a single stem and a mature or potentially mature stand height exceeding two metres and with existing or potential crown cover of overstorey strata about equal to or greater than 20 per cent. This includes Australia's diverse native forests and plantations, regardless of age. It is also sufficiently broad to encompass areas of trees that are sometimes described as woodlands".The dataset was compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for the National Forest Inventory (NFI), a collaborative partnership between the Australian and state and territory governments. The role of the NFI is to collate, integrate and communicate information on Australia's forests. State and territory government agencies collect forest data using independent methods and at varying scales or resolutions. The NFI applies a national classification to state and territory data to allow seamless integration of these datasets. Multiple independent sources of external data are used to fill data gaps and improve the quality of the final dataset.The NFI classifies forests into three national forest categories (Native Forest, Commercial plantation, and other forest) and then into various forest types. Commercial plantations presented in this dataset were sourced from the National Plantation Inventory (NPI) spatial dataset (2021), also produced by ABARES.Another dataset produced by ABARES, the Catchment scale land use of Australia CLUM dataset (2020), was used to identify and mask out land uses that are inappropriate to map as forest.The Forests of Australia (2023) dataset is produced to fulfil requirements of Australia's National Forest Policy Statement and the Regional Forests Agreement Act 2002 (Cwth) and is used by the Australian Government for domestic and international reporting.Previous versions of this dataset are available on the Forests Australia website spatial data page and the Australian Government open government data portaldata.gov.au.CurrencyDate modified: 30 November 2023Modification frequency: Every 5 yearsData extentSpatial extentNorth: -8.2°South: -44.4°East: 157.2°West: 109.5°Source informationData, Metadata, Maps and Interactive views are available from ABARES website.Forests of Australia (2023) – Descriptive metadata.The data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). ABARES is providing this data to the public under a Creative Commons Attribution 4.0 license.Lineage statementPresented on this page is a summarised lineage on the development of state and territory datasets for Forests of Australia (2023). The dataset has been produced using the Multiple Lines of Evidence (MLE) method for publication in the Australia’s State of the Forests Report – 2023 update. Detailed lineage information can be found here.Forests of Australia (2023) is a continental spatial dataset of forest extent, by national forest categories and types, assembled for Australia's State of the Forests Report – 2023 update. It was developed from multiple forest, vegetation and land cover data inputs, including contributions from Australian, state and territory government agencies and external sources.For each state or territory, except for the ACT where there was no new data, intersection of the Forests of Australia (2018) dataset with a forest cover dataset supplied by the jurisdiction, and with other available and appropriate independent forest cover datasets, identified:High confidence areas – areas where all the examined datasets agreed with the Forests of Australia (2018) dataset that the areas were forest or non-forest. No further assessment was required for these areas.Moderate confidence areas – areas where the Forests of Australia (2018) dataset agreed with the forest cover dataset supplied by state or territory, and with external or independent datasets, that the areas were forest or non-forest. These areas were identified as potential errors and needed further analysis in order to determine the correct allocation (forest or non-forest). The required analyses and validation were conducted by ABARES, in consultation with relevant state and territory agencies, using various ancillary data including high-resolution imagery such as World Imagery by ESRI, Bing Maps and Google Earth Pro.Low confidence areas – areas where the Forests of Australia (2018) dataset disagreed with the forest cover dataset supplied by state or territory, and with external or independent datasets, that the areas were forest or non-forest. All such areas were identified as potential errors and needed further analysis in order to determine the correct allocation (forest or non-forest). The required analyses and validation were conducted by ABARES, in consultation with relevant state and territory agencies, using various ancillary data including high-resolution imagery such as World Imagery by ESRI, Bing Maps and Google Earth Pro.External or independent datasets used include:H_Woody_Fuzzy_2_Class dataset is based on the NGGI dataset produced by DCCEEW from Landsat data and was developed to support New South Wales Natural Resources Commission’s (NRC) Monitoring, Evaluation and Reporting Program. NRC applied Fuzzy Logic and Probability modelling to the NGGI dataset to derive annual layers distinguishing between forest and non-forest at 25 m raster resolution. Each of five annual layers, 2015 to 2019, was resampled to a 100 m raster by classifying as forest the 100 m pixels that had more than half their area as forest as determined from 25 m pixels. The five annual layers were combined and every pixel in the combination that had been classified as forest in any year during 2015-2019 period was allocated as forest (and the balance non-forest). This approach was taken to prevent areas where the crown cover had reduced temporarily below 20%, through events such as fire, harvesting, drought or disease, from being incorrectly classified as non-forest.State-wide Land and Tree Study (SLATS) dataset is based on data collected by the Landsat satellite. This dataset was available for Queensland only. Foliage Projective Cover (FPC) values of 11 or greater (equivalent to crown cover 20% or greater) were considered as forest candidates in this SLATS dataset. The National Vegetation Information System (NVIS) version 6.0 dataset was used to identify areas in this SLATS dataset that met the height requirements of the forest definition used by the National Forest Inventory.The National Greenhouse Gas Inventory (NGGI) dataset is produced from Landsat satellite Thematic Mapper™, Enhanced Thematic Mapper Plus (ETM+) and Operational Land Image (OLI) images for the Australian Government Department of the Climate Change, Energy, the Environment and Water (DCCEEW), and identifies woody vegetation of height or potential height greater than 2 metres, crown cover greater than 20%, and with a minimum patch size of 0.2 hectares (DISER, 2021a) . The dataset is compiled using time-series data since 1972 and is produced at a 25 m × 25 m resolution. The NGGI dataset used was developed from the five annual layers (2016-2020, inclusive) from the ‘National Forest and sparse woody vegetation data (Version 5.0) spatial dataset produced using the algorithms for land-use change allocation developed for the National Inventory Reports (DISER, 2021b). Each layer of the original 25 m resolution, three-class (forest, sparse woody and non-forest) dataset was resampled to a binary (forest and non-forest) 100 m raster by classifying as forest the 100 m pixels that had more than half their area as forest; the sparse woody and non-forest classes were combined into a non-forest class. The five annual layers were then combined and every pixel in the combination that had been classified as forest in any year during 2016-2020 period was allocated as forest (and the balance non-forest). This approach was taken to prevent areas where the crown cover had reduced temporarily below 20%, through events such as fire, harvesting, drought or disease, from being incorrectly classified as non-forest.All input datasets were converted to 100m rasters (ESRI GRID format), aligning with relevant standard NFI state or territory masks (also known as NFI SNAP grids), in Albers projection. Where the input dataset was in polygon format, the Polygon to Raster tool was used to convert the polygon dataset to raster format, using the Maximum_Combined_Area option.Validation assessment results were incorporated to give improved and high-confidence forest cover datasets for each state or territory.Look-up tables translating the state or territory forest cover data to NFI forest types were used where provided. Where this information was not provided, it was derived by ABARES from translating Levels 5 and 6 of the National Vegetation Information System (NVIS) version 6.0 attribute information to NFI forest types.This dataset has been converted from GeoTIFF to Multidimensional Cloud Raster Format (CRF) to facilitate publishing to the Digital Atlas of Australia (DAA).Date of extraction: February 2024.Data dictionaryAttribute nameDescriptionVALUEIdentifier of every unique combination of the following attributes: STATE, FOR_SOURCE, FOR_CODE, FOR_TYPE, FOR_CAT, HEIGHT and COVER.COUNTNumber of cells that belong to a particular VALUE. For this dataset, in which cell resolution is 100 by 100 metres.

ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and wellbeing by ensuring that Australia's financial markets are fair and transparent, and supported by confident and informed investors and consumers.

Credit representatives are required to maintain their details on ASIC's registers. Information contained on the Credit Representative Register is made available to the public to search via the ASIC Connect website.

Selected data from the register will be uploaded each month to www.data.gov.au. The data made available will be a snapshot of the register at a point in time. Legislation prescribes the type of information ASIC is allowed to disclose to the public.

The information in the downloadable dataset includes:

• Register name
• Credit representative number
• Credit licensee number
• Credit representative name
• Credit representative ABN or ACN(if applicable)
• Date commenced
• Date ceased (if applicable)
• Principal business locality (representative)
• Principal business state/territory (representative)
• Principal business postcode (representative)
• EDRS code
• Credit representative authorisations
• Cross endorsements

Additional information about Credit representatives can be found via ASIC's website. To view some information you may be charged a fee.

More information about searching ASIC's registers.

Australia recorded a trade surplus of 5085 AUD Million in December of 2024. This dataset provides the latest reported value for - Australia Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Update April 2022### We have replaced the .xlsx file resources for all our datasets. This was required due to the API and web page search functionality no longer being supported for .xlsx files …Show full description###Update April 2022### We have replaced the .xlsx file resources for all our datasets. This was required due to the API and web page search functionality no longer being supported for .xlsx files on the Data.Gov platform. ###Update November 2018 - frequency change to Australian Financial Services Authorised Representatives dataset ### From 15 November 2018, the Australian Financial Services Authorised Representative dataset will be updated weekly every Thursday. ###Dataset summary### ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and wellbeing by ensuring that Australia's financial markets are fair and transparent, and supported by confident and informed investors and consumers. Australian Financial Services Representatives are required to maintain their details on ASIC's registers. Information contained on the Australian Financial Services Representatives Register is made available to the public to search via the ASIC Connect website. Selected data from the register will be uploaded each month to www.data.gov.au. The data made available will be a snapshot of the register at a point in time. Legislation prescribes the type of information ASIC is allowed to disclose to the public. The information in the downloadable dataset includes: Register Name Australian Financial Services Representative number Australian Financial Services Licence number Representative name ABN Organisation Number (eg ACN) Other role Date representative was appointed Appointment status Date representative appointment ceased (if applicable) Australian Financial Services Representative's principal business Address suburb Australian Financial Services Representative's principal business Address State/Territory Australian Financial Services Representative's principal business Address postcode Australian Financial Services Representative's principal business Address Country Cross endorsements Authority to appoint other representatives (Yes/No) Appointing authorised representative number (if applicable) Australian Financial Services authorisations Same authorisations as licensee (Yes/No) Australian Financial Services Representative related business names Additional information about financial advisers can be found via ASIC's website. Accessing some information may attract a fee. More information about searching ASIC's registers.

Government Debt in Australia increased to 541402 AUD Million in January from 522524 AUD Million in December of 2024. This dataset provides - Australia Government Net Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset presents information about total income. The data covers the financial years 2011-12 to 2017-18, and is based on Statistical Area Level 2 (SA2) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS). Total Income is the sum of all reported income derived from Employee income, Own unincorporated business, Superannuation, Investments and Other income. Total income does not include the non-lodger population. Government pensions, benefits or allowances are excluded from the Australian Bureau of Statistics (ABS) income data and do not appear in Other income or Total income. Pension recipients can fall below the income threshold that necessitates them lodging a tax return, or they may only receive tax free pensions or allowances. Hence they will be missing from the personal income tax data set. Recent estimates from the ABS Survey of Income and Housing (which records Government pensions and allowances) suggest that this component can account for between 9% to 11% of Total income. All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the ABS to closely align to ABS definitions of income. The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18. Please note: All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.

Update January 2022 - renamed field

From 10 January 2022, the field ADV_FASEA _APPROVED_QUAL will be renamed to ADV_APPROVED_QUAL.

Update November 2019 - additional fields

From 21 November 2019, the dataset will be updated to include 7 new fields (see help file for details)

These fields are included in conjunction with the professional standards reforms for financial advisers. More information can be found on the ASIC website https://asic.gov.au/regulatory-resources/financial-services/professional-standards-for-financial-advisers-reforms/.

Note: For most advisers the new fields will be unpopulated on 21 November 2019. As advisers provide this data to ASIC it will appear in the dataset.

Dataset summary

ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and wellbeing by ensuring that Australia’s financial markets are fair and transparent, supported by confident and informed investors and consumers.

Australian Financial Services Licensees are required to keep the details of their financial advisers up to date on ASIC's Financial Advisers Register. Information contained in the register is made available to the public to search via ASIC's Moneysmart website.

Select data from the Financial Advisers Register will be uploaded each week to www.data.gov.au. The data made available will be a snapshot of the register at a point in time. Legislation prescribes the type of information ASIC is allowed to disclose to the public.

The information included in the downloadable dataset is:

• Adviser name
• Adviser number
• Adviser role
• Adviser sub type
• Adviser role status
• Adviser ABN
• Year first provided advice
• Licence name
• Licence number
• Licence ABN
• Licence controlled by
• Adviser start date
• Adviser end date
• Adviser CPD failure year
• Adviser principal business address suburb
• Adviser principal business address State/Territory
• Adviser principal business address postcode
• Adviser principal business address Country
• Appointing representative name
• Appointing representative number
• Appointing representative ABN
• Disciplinary action start date
• Disciplinary action end date
• Disciplinary action type
• Product authorisations (for a full list see the Financial Adviser Register – Help File)
• Qualifications and Training
• Approved qualifications
• Memberships
• Further restrictions

Additional information about financial advisers can be found via ASIC's website. Accessing some information may attract a fee.

More information about searching ASIC's registers.

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).