Techsalerator's Corporate Actions Dataset in Australia offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 2200 companies traded on the Australian Securities Exchange* (XASX).

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Top 5 used data fields in the Corporate Actions Dataset for Australia:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Australia:

Resource Sector Developments: Corporate actions in the mining and resource sectors, including new mineral discoveries, expansion of mining operations, and commodity price fluctuations, have a significant impact on Australia's economy.

Financial Services and Fintech: Corporate actions related to financial services, including the growth of fintech companies, digital banking solutions, and changes in financial regulations, play a crucial role in Australia's financial landscape.

Real Estate Investments: Corporate actions in the real estate sector, such as property development projects, commercial real estate investments, and urbanization efforts, are notable contributors to Australia's economy.

Renewable Energy Initiatives: Corporate actions involving investments in renewable energy projects, such as solar and wind farms, reflect Australia's commitment to transitioning to sustainable energy sources.

Healthcare and Biotechnology: Corporate actions in the healthcare and biotechnology sectors, including drug development, medical research, and healthcare technology advancements, are important contributors to Australia's innovation-driven economy.

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Top 5 financial instruments with corporate action Data in Australia

Australian Stock Exchange (ASX) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Australian Stock Exchange. This index provides insights into the performance of the Australian stock market.

ASX Foreign Company Index: The index that tracks the performance of foreign companies listed on the Australian Stock Exchange, if foreign listings are present. This index gives an overview of foreign business involvement in Australia.

GroceryLand Australia: An Australia-based supermarket chain with operations in multiple regions. GroceryLand Australia focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinanceDown Under: A financial services provider in Australia with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

AgriTech Australia: A company dedicated to advancing agricultural technology in Australia, focusing on optimizing crop yields, sustainable farming practices, and technological innovation in the agricultural sector.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Australia, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Australia?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset cov...

Money Supply M1 in Australia increased to 1922.88 AUD Billion in October from 1902.07 AUD Billion in September of 2025. This dataset provides - Australia Money Supply M1 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Job Trends in Australia

Insight into Job Types, Salaries and Growth Across States

By [source]

About this dataset

This dataset provides an insightful look into the Australian job market and the country's current trend of economic development. It consists of 30000 unique job postings from SEEK Australia, a renowned job board in Australia, offering valuable insights regarding salaries, job types, and openings across cities and states.

The data allows researchers to compare which type of jobs are offered across different locations, providing critical information on which cities or states offer particular kinds of jobs. Moreover, it offers a framework that can be used to understand how different companies compare when it comes to salaries and hiring practices. In addition, this dataset provides an in-depth view into what type of job openings there are in each city or state and their respective salaries. All this is available through reliable columns such as city, state, company name, salary offered & url enabling effective analysis and providing consumers with much needed knowledge about their potential employment opportunities in the market

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How to use the dataset

This dataset can be used to gain insights into the Australian job market – from job types, salaries, and locations to trends in growth across states. To get started working with this data, you will need to download the dataset from Kaggle.

Once you have the dataset downloaded, it is important to become familiar with the different fields available as these will influence what kind of analysis you can do. The columns include category, city, company name, geo coordinates (for location-based analysis), job board (to determine where these jobs were posted), job description (to find relevant keywords and topics related with each position), job title (to search for specific jobs or trends in titles over time), job type (i.e full time/part-time etc.), posting date and salary offered.

You can further filter your results based on any combination of these different column values to get more targeted information about a certain area or topic that you are researching on. Additionally, visualizing certain elements such as salary ranges by region/job type may be helpful for gaining a wide understanding of Australia’s labor landscape in various sectors and cities.

Finally, it may also be useful to look at how salaries might have changed over time by comparing postings from 2 different years for example which could help identify employment growth areas or opportunities for businesses looking to set up shop in certain regions etc

Research Ideas

• Analyzing Salary Trends: By investigating the salaries of various job postings, researchers can gain insights on wage growth and wage disparities across different cities and states in Australia.
• Comparing Job Types & Salaries: Researchers can observe which cities offer higher salaries for particular job types and also get an understanding of what is expected from potential applicants.
• Tracking Job Market Growth: By using data from the years before, it is possible to identify which areas have seen the most growth in terms of job opportunities and how that compares with other areas in Australia

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: seek_australia.csv | Column name | Description | |:--------------------|:------------------------------------------------------------| | category | The category of the job posting. (String) | | city | The city in which the job is located. (String) | | company_name | The name of the company offering the job. (String) | | geo | The geographic coordinates of the job location. (String) | | job_board | The job board on which the job was posted. (String) | | job_description | The description of the job. (String) | | job_title | The title of the job. (...

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.

Australia Grocery Product Dataset

Products, prices, titles, descriptions, etc..

By Jeff [source]

About this dataset

This dataset provides information on grocery products available in Australia, including pricing information. The data was extracted from the Grocery department of coles.com.au, and includes a selected list of categories. Columns include postal code, category, subcategory, product group, product name, package price, price per unit, package size, estimated status, special status, stock status, retail price, product URL, brand, SKU number, run date, unit price, and unit price unit

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Research Ideas

• Create a retail price comparison tool for grocery items in different Australian states
• Use the data to analyze trends in grocery pricing over time
• Use the data to map out the cheapest and most expensive areas for groceries in Australia

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

See the dataset description for more information.

Columns

File: Australia_Grocery_2022Sep.csv | Column name | Description | |:--------------------|:-------------------------------------------------------------------| | Postal_code | The postal code of the store where the product was found. (String) | | Category | The category of the product. (String) | | Sub_category | The subcategory of the product. (String) | | Product_Group | The product group of the product. (String) | | Product_Name | The name of the product. (String) | | Package_price | The price of the product in Australian dollars. (Float) | | Price_per_unit | The price per unit of the product in Australian dollars. (Float) | | package_size | The size of the product package in grams. (Integer) | | is_estimated | Whether or not the price is an estimate. (Boolean) | | is_special | Whether or not the product is on special. (Boolean) | | in_stock | Whether or not the product is in stock. (Boolean) | | Retail_price | The retail price of the product in Australian dollars. (Float) | | Product_Url | The URL of the product on the Coles website. (String) | | Brand | The brand of the product. (String) | | Sku | The SKU of the product. (String) | | RunDate | The date on which the price was collected. (Date) | | unit_price | The unit price of the product in Australian dollars. (Float) | | unit_price_unit | The unit of measurement for the unit price. (String) | | state | The state in which the product was found. (String) | | city | The city in which the product was found. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jeff.

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.

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.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

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.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

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

Changes in the November 2025 release

• Nationally, the November 2025 update of G-NAF shows an increase of 32,773 addresses overall (0.21%). The total number of addresses in G-NAF now stands at 15,827,416 of which 14,983,358 or 94.67% are principal.

• There is one new locality for the November 2025 Release of G-NAF, the locality of Southwark in South Australia.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

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.

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

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

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.

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.

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

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

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.

What to Expect When You Download G-NAF

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.

The Gross Domestic Product (GDP) in Australia was worth 1752.19 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Australia represents 1.65 percent of the world economy. This dataset provides - Australia GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Data from: Major Roads

Related Articles:

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Data Description:

Abstract Major Roads is a subset of the National Roads dataset, filtered for highways, arterial and sub-arterial roads. National Roads is a digital representation of the road network of Australia. National Roads contains linear features to describe surfaces that have been improved to enable vehicular, pedestrian and bicycle transportation on land and ferry routes that enable vehicles to cross water bodies. National Roads does not include railways, tramways, driveways or passenger ferry routes. This dataset provides an optimised aggregated national view of road geometry and attribution. The dataset is created from multiple sources including jurisdictional data which is revised regularly and supplied in varying formats and at different levels of quality. The purpose of Roads is to provide a single national digital representation of Australian roads with detailed attribution to enable clients to undertake activities including visualisation, analysis and logistics planning at both a national and local scale. The area covers the land mass of Australia, including offshore islands. Norfolk Island is currently not included. Currency Date modified: February 2024 Modification frequency: Monthly Data Extent Spatial Extent West: 96° South: -44° East: 160° North: -9° Source Information The data was obtained from Geoscape Australia. Geoscience Australia is providing this data to the public under a Creative Commons Attribution 4.0 license. Geoscience Australia catalog entry: Major Roads Lineage Statement National Roads provides a single national digital view of road centrelines across the entirety of Australia. Roads is continuously built through sourcing a broad range of datasets from many organisations. This data is quality assured, standardised, integrated and topology-corrected before publication. Road centrelines are primarily sourced from State and Territory governments and form the basis for the Roads network. Roads additional to the State and Territory provisions are digitised or integrated where reliable sources of road centrelines are identified that improves the quality and/or consistency of Roads nationally. For attribution of Roads data sources refer to this webpage: geoscape.com.au/legal/data-copyright-and-disclaimer/. The Digital Atlas of Australia team have published a feature layer for Major roads in GDA2020 format. Major roads is a filtered subset of the National roads dataset that have an operational status, and roads with a hierarchy value of National or State highway, arterial, or sub-arterial.

Attribute Name Description:

road_id Persistent identifier for a roads feature

contributor_id The contributor’s identifier for a Roads segment

jurisdictional_control The Jurisdiction with control of the road as defined by the source State or Territory Jurisdiction (e.g. TRANSPORT FOR NEW SOUTH WALES)

operator The operator of the road

date_created Date this record was created in the data custodian’s system. Where this date is not available, then the first date on which the feature was processed for inclusion within Roads

date_modified Date this record was last updated

national_route A route number to identify a route of National significance (e.g. C30)

state_route A route number to identify a route of State significance (e.g. A20)

full_street_name The full official road name, which is a concatenation of street_name, street_type, and street_suffix attributes (e.g. PARKES PLACE WEST)

street_name Name of the road (e.g. SMITH AND JOHN)

street_name_label Name of the road in Title Case (e.g. Smith and John)

street_type Type of road (e.g. ROAD, STREET, CIRCUIT, LANE)

street_type_label Type of road in Title Case (e.g. Road, Street)

street_suffix Suffix of road (e.g. WEST)

street_suffix_label Suffix of road in Title Case (e.g. West)

street_alias_name A secondary name of the road

street_alias_type A secondary type of the road

street_alias_suffix A secondary suffix of the road

feature_type The classification of a road according to its physical characteristics (e.g. MOTORWAY, SINGLE CARRIAGEWAY)

hierarchy Hierarchy of the road (e.g. NATIONAL OR STATE HIGHWAY)

subtype Physical type of a road (e.g. ROUNDABOUT)

ground_relationship The relationship the road has with the ground (e.g. ABOVE GROUND, ON GROUND, BELOW GROUND)

lane_count Number of physical lanes represented as a total count

lane_description Description of the physical lane count of a road

one_way Indicates if the road supports one-way or two-way traffic direction

status Lifecycle stage of a road (e.g. OPERATIONAL)

surface Surface of the road (e.g. SEALED)

trafficability Indicates the minimum type of vehicle advised to traverse the road (e.g. 2WD)

travel_direction Direction a ve...

This dataset is about countries per year in Australia. It has 64 rows. It features 3 columns: country, and tax revenue.

This dataset is updated weekly. Please ensure that you use the most up-to-date version.### The Australian Charities and Not-for-profits Commission (ACNC) is Australia’s national regulator of charities. Since 3 December 2012, charities wanting to access Commonwealth charity tax concessions (and other benefits), need to register with the ACNC. Although many charities choose to register, registration with the ACNC is voluntary. Each year, registered charities are required to lodge an Annual Information Statement (AIS) with the ACNC. Charities are required to submit their AIS within six months of the end of their reporting period. Registered charities can apply to the ACNC to have some or all of the information they provide withheld from the ACNC Register. However, there are only limited circumstances when the ACNC can agree to withhold information. If a charity has applied to have their data withheld, the AIS data relating to that charity has been excluded from this dataset. This dataset can be used to find the AIS information lodged by multiple charities. It can also be used to filter and sort by different variables across all AIS information. This dataset can be used to find the AIS information lodged by multiple charities. It can also be used to filter and sort by different variables across all AIS information. AIS Information for individual charities can be viewed via the ACNC Charity Register. The AIS collects information about charity finances, and financial information provides a basis for understanding the charity and its activities in greater detail. We have published explanatory notes to help you understand this dataset. When comparing charities’ financial information it is important to consider each charity's unique situation. This is particularly true for small charities, which are not compelled to provide financial reports – reports that often contain more details about their financial position and activities – as part of their AIS. For more information on interpreting financial information, please refer to the ACNC website. The ACNC also publishes other datasets on data.gov.au as part of our commitment to open data and transparent regulation. Please click here to view them. NOTE: It is possible that some information in this dataset might be subject to a future request from a charity to have their information withheld. If this occurs, this information will still appear in the dataset until the next update. Please consider this risk when using this dataset.

Money Supply M3 in Australia increased to 3332.38 AUD Billion in October from 3300.84 AUD Billion in September of 2025. This dataset provides - Australia Money Supply M3 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Update March 2025###\r

\r From 21 March 2025, the dataset update frequency has change from monthly to weekly every Friday.\r \r

Update April 2022###\r

\r 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.\r \r ***\r

Dataset summary###\r

ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and well being by ensuring that Australia's financial markets are fair and transparent, and supported by confident and informed investors and consumers. \r \r Liquidators are required to maintain their details on ASIC's registers. Information contained on the Registered Liquidator and Official Liquidator Registers is made available to the public to search via the ASIC Connect website. \r \r Selected data from the registers 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. \r \r The information in the downloadable dataset includes: \r \r * Register name\r * Registered Liquidator number\r * Official Liquidator number\r * Liquidator name\r * Registered Liquidator start date\r * Official Liquidator start date\r * Liquidator status\r * Liquidator suspension date\r * Principal business address town\r * Principal business address state/territory\r * Principal business address postcode\r * Principal business address country\r * Liquidator firm membership\r \r Additional information about Registered Auditors can be found via ASIC's website. To view some information you may be charged a fee. \r \r More information about searching ASIC's registers.

Update April 2025 ###\r

\r The BN_RENEW_DT field has been removed and is no longer available in this dataset. \r \r

Update October 2018 - frequency change to Business Names dataset ###\r

\r From 31 October 2018, the Business Names dataset will be updated weekly every Wednesday. \r \r ***\r \r

Dataset summary###\r

\r 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.\r \r Australian business names are required to keep their details up to date on ASIC's Business Name Register. Information contained in the register is made available to the public to search via ASIC's website.\r \r Select data from the ASIC's Business Name 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.\r \r The information included in the downloadable dataset is:\r \r * Business Name \r * Status \r * Date of Registration\r * Date of Cancellation\r * Renewal Date \r * Former State Number (where applicable) \r * Previous State of Registration \r * Australian Business Number (ABN) \r \r Additional information about companies can be found via ASIC's website. Accessing some information may attract a fee.\r \r More information about searching ASIC's registers.

Australian Housing Prices

Location, Size, Price, Etc

By Jeff [source]

About this dataset

This dataset contains information on 1000 properties in Australia, including location, size, price, and other details

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

If you're looking for a dataset on Australian housing data, this is a great option. This dataset contains information on over 1000 properties in Australia, including location, size, price, and other details. With this data, you can answer questions like What is the average price of a home in Australia?, What are the most popular type of homes in Australia?, and more

Research Ideas

• This dataset can be used to predict hosing prices in Australia.
• This dataset can be used to find relationships between housing prices and location.
• This dataset can be used to find relationships between housing prices and features such as size, number of bedrooms, and number of bathrooms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: RealEstateAU_1000_Samples.csv | Column name | Description | |:--------------------|:---------------------------------------------------------------------------------------| | breadcrumb | A breadcrumb is a text trail that shows the user's location within a website. (String) | | category_name | The name of the category that the listing belongs to. (String) | | property_type | The type of property being listed. (String) | | building_size | The size of the property's building, in square meters. (Numeric) | | land_size | The size of the property's land, in square meters. (Numeric) | | preferred_size | The preferred size of the property, in square meters. (Numeric) | | open_date | The date that the property was first listed for sale. (Date) | | listing_agency | The agency that is listing the property. (String) | | price | The listing price of the property. (Numeric) | | location_number | The number that corresponds to the property's location. (Numeric) | | location_type | The type of location that the property is in. (String) | | location_name | The name of the location that the property is in. (String) | | address | The property's address. (String) | | address_1 | The first line of the property's address. (String) | | city | The city that the property is located in. (String) | | state | The state that the property is located in. (String) | | zip_code | The zip code that the property is located in. (String) | | phone | The listing agent's phone number. (String) | | latitude | The property's latitude. (Numeric) | | longitude | The property's longitude. (Numeric) | | product_depth | The depth of the product. (Numeric) | | bedroom_count | The number of bedrooms in the property. (Numeric) | | bathroom_count | The number of bathrooms in the property. (Numeric) | | parking_count | The number of parking spaces in the property. (Numeric) | | RunDate | The date that the listing was last updated. (Date) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jeff.

Time series data for the statistic Foreign_Direct_Investment_Net_Inflows_$ and country Australia. Indicator Definition:Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars.The statistic "Foreign Direct Investment Net Inflows $" stands at 54,187,965,386.71 Australian Dollars as of 12/31/2024. Regarding the One-Year-Change of the series, the current value constitutes an increase of 21,055,797,840.82 Australian Dollars compared to the value the year prior.The 1 year change is 21,055,797,840.82 Australian Dollars.The 3 year change is 22,669,017,293.99 Australian Dollars.The 5 year change is 15,442,835,725.59 Australian Dollars.The 10 year change is -9,016,550,961.16 Australian Dollars.The Serie's long term average value is 19,057,744,452.65 Australian Dollars. It's latest available value, on 12/31/2024, is 35,130,220,934.06 Australian Dollars higher, compared to it's long term average value.The Serie's change in Australian Dollars from it's minimum value, on 12/31/2005, to it's latest available value, on 12/31/2024, is +79,281,106,821.90 .The Serie's change in Australian Dollars from it's maximum value, on 12/31/2022, to it's latest available value, on 12/31/2024, is -16,177,016,730.31 .

Government spending in Australia was last recorded at 26.5 percent of GDP in 2024 . This dataset provides - Australia Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Update August 2018 - frequency change to Company dataset ### From 7 August 2018, the Company dataset will be updated weekly every Tuesday. If you require information about Reserved Names the register can be searched via ASIC-Connect. *** ###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 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 included in the downloadable dataset is: * Company Name * Australian Company Number (ACN) * Type * Class * Sub Class * Status * Date of Registration * 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. ***

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.

The Gross Domestic Product per capita in Australia was last recorded at 61211.90 US dollars in 2024. The GDP per Capita in Australia is equivalent to 485 percent of the world's average. This dataset provides - Australia GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

A continuation of the "Phytochemistry of Australian Plants" database compiled by David Collins and Don McGilvery. Contains chemical structures, references, species names, with persistent identifiers to the literature and Atlas of Living Australia (ALA) for geographical distributions. The current curation effort here adds DOIs/ISBNs/ISSNs for ~80% of references, persistent IDs for all species or genus to the ALA or other datasets, and validated structures (smiles) for ~70% of structures. No new entries have been added since the last update to the original database in 2022. Change log is in the README file.

Data provided here was obtained by the listed authors on linked publications, and these authors may have no association with CSIRO. CSIRO acknowledges that the publications linked here may contain Indigenous Cultural and Intellectual Property (ICIP), including traditional knowledge. CSIRO recognizes that First Nations peoples have the right to control, own and maintain their ICIP in accordance with Article 31 of the United Nations Declaration on the Rights of Indigenous Peoples. Users of this dataset may need to obtain permission from First Nations peoples for use of the information in linked publications. Users intending to collect and use biological specimens containing the compounds described in the dataset may also require permission of First Nations peoples, and may require permits and access permission from landholders. Recognizing that any ICIP in the linked publications is already publicly available but that the publications are not readily accessible by First Nations peoples, CSIRO is committed to finding ways to make the ICIP in these publications more findable and accessible to the First Nations communities from which the knowledge was originally obtained. Users should be aware that because of the historical context of some of the linked publications, they may contain words, descriptions, images or terms which may be culturally sensitive and/or offensive and that reflect authors’ views, or those of the period in which the content was created but may not be considered appropriate today. If First Nations people identify content within this dataset that they consider breaches cultural protocols they are encouraged to contact CSIRO on csiroenquiries@csiro.au or +61 3 9545 2176 to request its removal from the dataset. Please note that while CSIRO is able to administer the data housed within this dataset, this control does not extend to the associated publications. Requests to remove publications should be directed to the associated publishing company. Lineage: Original data extracted in 2022 from https://fms05.filemakerstudio.com.au/fmi/webd?homeurl=http://www.monash.edu/#PhytoChem by kind permission of David Collins and Don McGilvery.