From 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx.
Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und

COVID-19 cases by notification date and postcode, local health district, and local government area. The dataset is updated weekly on Fridays.

The data is for confirmed COVID-19 cases only based on location of usual residence, not necessarily where the virus was contracted.

Case counts reported by NSW Health for a particular notification date may vary over time due to ongoing investigations and the outcome of cases under review thus this dataset and any historical data contained within is subject to change on a daily basis.

The underlying dataset was assessed to measure the risk of identifying an individual and the level of sensitivity of the information gained if it was known that an individual was in the dataset. The dataset was then treated to mitigate these risks, including suppressing and aggregating data.

This dataset does not include cases with missing location information.

As of September 5, 2022, the number of male 20 to 29 year olds diagnosed with COVID-19 in Australia had reached around 23,164 cases per 100,000 people. At the time, people 70-79 years of age had the lowest share of confirmed cases across males and females.

JHU Has Stopped Collecting Data As Of 03/10/2023

After three years of around-the-clock tracking of COVID-19 data from around the world, Johns Hopkins has discontinued the Coronavirus Resource Center’s operations.

The site’s two raw data repositories will remain accessible for information collected from 1/22/20 to 3/10/23 on cases, deaths, vaccines, testing and demographics.

Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github.

Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths.

On 23/03/2020, a new data structure was released. The current resources for the latest time series data are:

• time_series_covid19_confirmed_global.csv
• time_series_covid19_deaths_global.csv
• time_series_covid19_recovered_global.csv

---DEPRECATION WARNING---
The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020:

• time_series_19-covid-Confirmed.csv
• time_series_19-covid-Deaths.csv
• time_series_19-covid-Recovered.csv

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

Context

NSW has been hit by the Omicron variant, with skyrocketing cases. This dataset, updated regularly, details the location of positive cases. A prediction of where the most cases could occur can be derived from this dataset and a potential prediction of how many cases there is likely to be.

Content

notification_date: Text, dates to when the positive case was notified of a positive test result. postcode: Text, lists the postcode of the positive case. lhd_2010_code: Text, the code of the local health district of the positive case. lhd_2010_name: Text, the name of the local health district of the positive case. lga_code19: Text, the code of the local government area of the positive case. lga_name19: Text, the name of the local government area of the positive case.

Acknowledgements

Thanks to NSW Health for providing and updating the dataset.

Inspiration

The location of cases is highly important in NSW. In mid-2021, Western Sydney had the highest proportion of COVID-19 cases with many deaths ensuing. Western Sydney is one of Sydney's most diverse areas, with many vulnerable peoples. The virus spread to western NSW, imposing a risk to the Indigenous communities. With location data, a prediction service can be made to forecast the areas at risk of transmission.

Health expenditure occurs where money is spent on health goods and services. It occurs at different levels of government, as well as by non-government entities such as private health insurers and individuals.

In many cases, funds pass through a number of different entities before they are ultimately spent by providers (such as hospitals, general practices and pharmacies) on health goods and services.

The term ‘health expenditure’ in this context relates to all funds given to, or for, providers of health goods and services. It includes the funds provided by the Australian Government to the state and territory governments, as well as the funds provided by the state and territory governments to providers.

This data has been superseded, for more recent data on health expenditure, please the AIHW page on health expenditure.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Introduction

Welcome to the Australian English Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 40 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Australian English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Australian English call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.

This dataset presents the footprint of cancer incidence statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Cancer Database 2012 Data Quality Statement.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a PHN. The number of people who could not be assigned a PHN is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• The ACD records all primary cancers except for basal and squamous cell carcinomas of the skin (BCCs and SCCs). These cancers are not notifiable diseases and are not collected by the state and territory cancer registries.

• The diseases coded to ICD-10 codes D45-D46, D47.1 and D47.3-D47.5, which cover most of the myelodysplastic and myeloproliferative cancers, were not considered cancer at the time the ICD-10 was first published and were not routinely registered by all Australian cancer registries. The ACD contains all cases of these cancers which were diagnosed from 1982 onwards and which have been registered but the collection is not considered complete until 2003 onwards.

• Note that the incidence data presented are for 2006-2010 because 2011 and 2012 data for NSW and ACT were not able to be provided for the 2012 ACD.

Abstract 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 the nationally consistent representation of the land borders as published by the Australian states and territories. It is topologically correct in relation to published jurisdictional land borders and the Geocoded National Address File (G-NAF). The purpose of this product is to provide:

a building block which enables development of other national datasets; integration with other geospatial frameworks in support of data analysis; and visualisation of these borders as cartographic depiction on a map.

Although this service depicts land borders, it is not nor does it purport to be a legal definition of these borders. Therefore it cannot and must not be used for those use-cases pertaining to legal context. Termination Points are the point at which the state border polylines meet the coastline. For the purpose of this product, the coastline is defined as the Mean High Water Mark (MHWM). In the absence of a new MHWM for NSW, the Jervis Bay termination points are defined by the NSW cadastre. This feature layer is a sub-layer of the Land Borders service. Currency Date modified: 10 November 2021 Modification frequency: None Data extent Spatial extent North: -14.88° South: -38.06° East: 153.55° West: 129.00° Source information Catalog entry: Australia's Land Borders The Land Borders dataset is created using a range of source data including:

Australian Capital Territory data was sourced from the ACT Government GeoHub – ‘ACT Boundary’. No changes have been made to the polylines or vertices of the source data. In the absence of any custodian published border for Jervis Bay – New South Wales, a border has been constructed from the boundary of the NSW cadastre supplied by NSW Spatial Services. Geoscience Australia’s GEODATA TOPO 250K data was considered as an alternative, however, that border terminated short of the coastline as it stops at the shoreline of the major water bodies. Therefore, a decision was made to use the NSW and OT supplied cadastre to create a new representation of the Jervis Bay border that continued to the coastline (MHWM), in place of the TOPO 250K data. In the absence of publicly available data from New South Wales, the land borders for New South Wales have been constructed using the data of adjoining states Queensland, South Australia, Victoria and the Australian Capital Territory. This approach is agreeable to New South Wales Government for this interim product. In the absence of publicly available data from the Northern Territory the land borders for the Northern Territory have been constructed using the data of adjoining states Western Australia, Queensland and South Australia. This approach is agreeable to Northern Territory Government for this interim product. Queensland state border and coastline data have been download from the Queensland Spatial, Catalogue – QSpatial. Publicly available data for the state borders of South Australia was downloaded from data.gov.au and is ‘SA State Boundary - PSMA Administrative Boundaries’. Downloaded as a file geodatabase in GDA2020. Victorian state border data has been downloaded from the Victorian state Government Spatial Datamart, it is titled ‘FR_FRAMEWORK_AREA_LINE’. The Victorian state border data was used for the NSW/VIC section of border due to the absence of any publicly available data from New South Wales for this section of the border. Western Australian state border data was downloaded from the WA Government as publicly available. The Western Australia state border data has been used for the WA/NT section of the border due to the absence of publicly available data from Northern Territory for this section of the border. Selecting the SA data for the WA/SA border would introduce mismatches with the WA cadastre. It would also not improve the SA relationship with the SA cadastre. Using the WA data for the WA/SA section of the border aligns each state with its own cadastre without causing overlaps.

Sources specific to the Termination Points are as follows:

Jurisdictions Coastline data source

NT/QLD Publicly available Queensland Coastline and State Border data

QLD/NSW Publicly available Queensland Coastline and State Border data

NSW/VIC VIC Framework (1:25K) line

VIC/SA Coastline Capture Program (of SA by Tasmania)

SA/WA Coastline Capture Program (of SA by Tasmania)

WA/NT Coastline Capture Program (of NT by Tasmania)

JBT (OT) NSW Cadastre

Lineage statement At the southwest end of the NT/SA/WA border the South Australian data for the border was edited by moving the end vertex ~1.7m to correctly create the intersection of the 3 states (SA/WA/NT). At the southeast end of the NT/QLD/SA border the South Australian data for the border was edited by moving the end vertex ~0.4m to correctly create the intersection of the 3 states (NT/SA/QLD). Queensland data was used for the NT/QLD border and the QLD/NSW border due to the absence of publicly available data from the Northern Territory for these section of the border. Data published by Queensland also included a border sections running westwards along the southern Northern Territory border and southwards along the western New South Wales border. These two sections were excluded from the product as they are not within the state of Queensland. Queensland data was also used in the entirety for the SA/QLD segment of the land borders. Although the maximum overlap between SA and QLD state border data was less than ~5m (and varied along the border), the Queensland data closely matched its own cadastre and that of South Australia. The South Australian data overlapped the Queensland data, it also did not match the South Australian cadastre. Therefore, a decision to use the Queensland data for the QLD/SA section of the border ensured the best possible topological consistency with the published cadastre of each state. The South Australian/Victorian state border, north-south, were generally very similar with some minor deviations from each other from less than 1m to ~60m (there is one instance of deviation of 170m). The section of border that follows the Murray River is matched, for the most part by both states. Over three quarters of the border running along the river is matched with both states. There is a mismatch between the states in the last quarter of the border along the river, the northern section, however, both states still have the border running inside, or along, the river polygon (Surface hydrology), the Victorian data was chosen for this section purely for consistency as the Victorian data was used for the preceding arcs. Overall, the Victorian data was selected for use as the South Australia/Victoria land border. After taking the existing cadastre and GNAF points into account and it did not introduce extra errors into the relationship between the land borders and the cadastre of either state. In parts, it improved the relationship between the South Australian cadastre and the SA/VIC state border. This interim product will be updated when all states and territories have published agreed, authoritative representations of their land borders. This product will also be updated to include land mass polygons at time when the Coastline Capture Program is complete. This dataset is GDA 2020 compliant - transformed into GDA2020 from it's original source datum. Reference System Code 2020.00. Data dictionary All Layers

Attribute name Description

CREATE_DATE Date on which the positional data point was created in the data set

Field All features in this data set are labelled "TERMINATION_POINT"

SOURCE Project from which the data point information is derived

STATEMENT Legal disclaimer for the positional data

STATES Termination points divide at least two states and/or territories

Contact Geoscience Australia, clientservices@ga.gov.au

Data are collected under the Health Care Act 2008. The Cancer Registry collects information on all invasive (malignant) cancer diagnoses for all South Australian residents and on deaths for these cancer cases. Data include demographic information, details of the cancer including the date of diagnosis, site and histology, and the tests used to diagnose the cancer. For those cancer cases who have died, details of the cause and date of death are collected. Further information can be found on the SA Health website.

This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.

This dataset presents the footprint of cancer incidence statistics in Australia for all cancers combined. The data spans the years 2006-2010 and is aggregated to the 2011 Public Health Information Development Unit (PHIDU) Population Health Areas (PHA), based on the 2011 Australian Statistical Geography Standard (ASGS).

Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Cancer Database 2012 Data Quality Statement.

Please note:

• AURIN has spatially enabled the original data using the PHIDU - PHAs.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned to a PHA. The number of people who could not be assigned a PHA is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• The ACD records all primary cancers except for basal and squamous cell carcinomas of the skin (BCCs and SCCs). These cancers are not notifiable diseases and are not collected by the state and territory cancer registries.

• The diseases coded to ICD-10 codes D45-D46, D47.1 and D47.3-D47.5, which cover most of the myelodysplastic and myeloproliferative cancers, were not considered cancer at the time the ICD-10 was first published and were not routinely registered by all Australian cancer registries. The ACD contains all cases of these cancers which were diagnosed from 1982 onwards and which have been registered but the collection is not considered complete until 2003 onwards.

• Note that the incidence data presented are for 2006-2010 because 2011 and 2012 data for NSW and ACT were not able to be provided for the 2012 ACD.

This dataset presents the footprint of female cancer incidence statistics in Australia for all cancers combined and the 11 top cancer groupings (breast, cervical, colorectal, leukaemia, lung, lymphoma, melanoma of the skin, ovary, pancreas, thyroid and uterus) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS). Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).

This collection contains aggregated Australian species occurrence data from 1900 to April 2022 using a suite of facets of most importance for environmental assessments. Occurrence records were aggregated and organised by the Atlas of Living Australia (ALA) and include survey and monitoring data collected and managed by the Integrated Marine Observing System (IMOS) and the Terrestrial Ecosystem Research Network (TERN). Data from these infrastructures and other sources have been organised here as a national public-access dataset. This collection serves as a standardised snapshot of Australian biodiversity occurrence data from which many indicator datasets can more readily be derived (see Has Derivation entries below).

The primary asset is aggregated_spp_occ.csv. This contains all faceted data records for the period and supported facets related to time, space, taxonomy and conservation significance. Six derived assets demonstrate uses supported by the faceted data. Each is a pivot of the aggregated dataset. The data_sources.csv file includes information on the source datasets within the Atlas of Living Australia that contributed to this asset.

Grouping records from this dataset supports comparisons between the number of occurrence records for different regions and/or time periods and/or categories of species and occurrence data. Grouped counts of this kind may serve as useful indications of variation and change across the dimensions compared. Note however that such counts may not accurately reflect real differences in biodiversity. It is important to consider confounding factors (particularly variations in recording effort over time). Grouping all records by a single facet (e.g. IBRA region) may help to expose such factors.

Notes

GRIIS 1.6 includes a number of vertebrate species listed because some individuals have been translocated or (re-)introduced beyond their remaining ranges for conservation purposes. It is unhelpful for the current analysis to treat these as introduced species. These species were removed from the version of the GRIIS list used in this analysis. In future versions of GRIIS, these species will be documented as native species that have been translocated/reintroduced. Lineage: All species occurrence data aggregated by the ALA as of 2022-04-14 were filtered to include only:

• Records from 1900 onwards • Presence records only • Spatial coordinates present • Taxon identified to at least species level • Location falls within an IBRA or IMCRA region

Filtered data were processed to include the following elements:

1. Accepted taxon ID
2. Accepted species name
3. Classification (higher ranks)
4. Year of occurrence
5. Coordinates of occurrence
6. Basis of record (specimen, human observation, etc.)
7. State or Territory
8. IBRA7 terrestrial region
9. IMCRA 4.0 mesoscale marine bioregion
10. Status of location in CAPAD 2020 (not protected area, protected area, Indigenous protected area)
11. Status of location in Forests of Australia (2013)
12. Status of location in Forests of Australia (2018)
13. Status of species on EPBC Act List of Threatened Species (mapped to accepted ALA species using galah R package)
14. Status of species on Global Register of Introduced and Invasive Species – Australia (GRIIS) version 1.6 (mapped to accepted ALA species using galah R package)

Processed occurrence data were grouped to count records detected for each distinct combination of eleven primary facets. The resulting dataset is published as aggregated_spp_occ.csv and includes the following elements:

1. Year of occurrence
2. Basis of record (specimen, human observation, etc.)
3. State/Territory
4. IBRA7 terrestrial region
5. IMCRA 4.0 mesoscale marine bioregion
6. Status of location in Forests of Australia (2018)
7. Status of location in Forests of Australia (2013)
8. Status of location in CAPAD 2020 (not protected, PA – protected area, IPA – Indigenous protected area)
9. Status of species on EPBC Act List of Threatened Species
10. Status of species on Global Register of Introduced and Invasive Species – Australia (GRIIS) version 1.6
11. ALA species identifier
12. Scientific name for species
13. Count of occurrence records matching the values for elements 1 to 11

Six derived summary datasets are also included. Each of these is a pivot of data in the main dataset and demonstrates a use case for the information.

• summary_protection_status_marine.csv • summary_protection_status_terrestrial.csv

These two datasets include the following columns:
1. IMCRA 4.0 / IBRA 7 bioregion
2. ALA Species ID
3. Species scientific name 4. EPBC status for species
5. Count of all records for species from region
6. Count of all records for species from protected areas inside region
7. Count of all records for species from protected areas under Indigenous management inside region

• summary_threatened_spp_occ_marine.csv • summary_threatened_spp_occ_terrestrial.csv

1. IMCRA 4.0 /IBRA 7 bioregion
2. Starting year of the time period
3. Ending year of the time period
4. EPBC status for species
5. Count of all occurrence records in the region and status for the given period
6. Count of all distinct species in the region and status for the given period

• summary_introduced_spp_occ_marine.csv • summary_introduced_spp_occ_terrestrial.csv

These two datasets include the following columns:
1. IMCRA 4.0 /IBRA 7 bioregion 2. Starting year of the time period
3. Ending year of the time period
4. GRIIS status for species (Native, Introduced, Invasive)
5. Count of all occurrence records in the region and status for the given period
6. Count of all distinct species in the region and status for the given period

This dataset presents the footprint of male cancer incidence statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, lung, lymphoma, melanoma of the skin, pancreas, prostate and stomach) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Cancer Database 2012 Data Quality Statement.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a PHN. The number of people who could not be assigned a PHN is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• The ACD records all primary cancers except for basal and squamous cell carcinomas of the skin (BCCs and SCCs). These cancers are not notifiable diseases and are not collected by the state and territory cancer registries.

• The diseases coded to ICD-10 codes D45-D46, D47.1 and D47.3-D47.5, which cover most of the myelodysplastic and myeloproliferative cancers, were not considered cancer at the time the ICD-10 was first published and were not routinely registered by all Australian cancer registries. The ACD contains all cases of these cancers which were diagnosed from 1982 onwards and which have been registered but the collection is not considered complete until 2003 onwards.

• Note that the incidence data presented are for 2006-2010 because 2011 and 2012 data for NSW and ACT were not able to be provided for the 2012 ACD.

This dataset has been superseded. An updated version of this dataset is available from the ABARES website.\r

\r This dataset is a national compilation of catchment scale land use data for Australia (CLUM), as at September 2017. It replaces the Catchment Scale Land Use of Australia - Update May 2016 released in June 2016. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. It has been compiled from vector land use datasets collected as part of state and territory mapping programs through the Australian Collaborative Land Use and Management Program (ACLUMP). Catchment scale land use data was produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field. The date of mapping (2003 to 2017) and scale of mapping (1:5 000 to 1:250 000) vary, reflecting the source data, capture date and scale. This information is provided in a supporting polygon dataset. \r \r The following areas have been updated since the May 2016 version: Desert Channels and Mackay-Whitsundays natural resource management (NRM) regions in Queensland; the Adelaide and Mount Lofty Ranges NRM region in South Australia (extending into part of the Murray-Darling Basin NRM region); the state of New South Wales; the state of Victoria; the state of Tasmania; the state of Western Australia; and the Northern Territory. The capital city of Adelaide was also updated using 2016 mesh block information from the Australian Bureau of Statistics. This equates to over 585 million hectares or 76% of Australia, the largest update of catchment scale land use mapping to date. \r \r Users should update any references or links to previous CLUM datasets in their databases. Users should also note that it is not possible to calculate land use change statistics between annual CLUM national compilations as not all regions are updated each year; land use mapping methodologies, precision, accuracy and source data (in particular satellite imagery) have improved over the years; and the land use classification has changed over time. In particular, the major differences between this September 2017 version and the May 2016 version are:\r \r * The September 2017 version has been mapped to version 8 of the Australian Land Use and Management (ALUM) Classification (released in October 2016)\r * Improvements to the Western Australia mapping including improved mapping of native forests, conservation areas, topographic features, horticulture and intensive animal production\r * Changes to the mapping of native and modified pastures in New South Wales\r * Changes to the mapping of dairies in Tasmania so that only the dairy infrastructure is mapped as dairies and the dairy pastures are mapped as modified pastures.\r \r It is only possible to calculate change when earlier land use datasets have been revised and corrected to ensure that changes detected are real change and not an artefact of the mapping process. The Queensland Land Use Mapping Program (QLUMP) have done this on an NRM regions basis for Queensland and can be accessed at:\r \r * Data: Search the Queensland Spatial Catalogue (http://dds.information.qld.gov.au/dds/) for "Land Use Mapping"\r * Reports: https://www.qld.gov.au/environment/land/vegetation/mapping/qlump-reports.\r \r The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). As a seamless spatial dataset for Australia, it can be used to identify, map and analyse high level land use categories (such as irrigated horticulture and dryland cropping) and more specific land use categories such as grapes, cotton, cereals, sugar and tree fruits. These categories can be extracted or combined with other spatial datasets to provide new insights and analysis concerning land use in Australia. A complementary dataset Catchment Scale Land Use of Australia - Commodities - September 2017 provides commodity level mapping as a vector dataset. \r \r Land use is classified according to the Australian Land Use and Management (ALUM) Classification version 8, a three-tiered hierarchical structure. There are five primary classes, identified in order of increasing levels of intervention or potential impact on the natural landscape. Water is included separately as a sixth primary class. Primary and secondary levels relate to the principal land use. Tertiary classes may include additional information on commodity groups, specific commodities, land management practices or vegetation information. The primary, secondary and tertiary codes work together to provide increasing levels of detail about the land use. Land may be subject to a number of concurrent land uses. For example, while the main management objective of a multiple-use production forest may be timber production, it may also provide conservation, recreation, grazing and water catchment land uses. In these cases, production forestry is commonly identified in the ALUM code as the prime land use. \r \r The operational scales of catchment scale mapping vary according to the intensity of land use activities and landscape context. Scales range from 1:5 000 and 1:25 000 for irrigated and peri-urban areas, to 1:100 000 for broadacre cropping regions and 1:250 000 for the semi-arid and arid pastoral zone. The date of mapping generally reflects the intensity of land use. The most current mapping occurs in intensive agricultural areas; older mapping generally occurs in the semi-arid and pastoral zones. \r The primary classes of land use in the ALUM Classification are:\r \r 1. Conservation and natural environments - land used primarily for conservation purposes, based on maintaining the essentially natural ecosystems present\r 2. Production from relatively natural environments - land used mainly for primary production with limited change to the native vegetation\r 3. Production from dryland agriculture and plantations - land used mainly for primary production based on dryland farming systems\r 4. Production from irrigated agriculture and plantations - land used mostly for primary production based on irrigated farming\r 5. Intensive uses - land subject to extensive modification, generally in association with closer residential settlement, commercial or industrial uses\r 6. Water - water features (water is regarded as an essential aspect of the classification, even though it is primarily a land cover type, not a land use)\r \r The Catchment Scale Land Use of Australia - Update September 2017 is a product of the Australian Collaborative Land Use and Management Program (ACLUMP). ACLUMP, of which ABARES is a partner, promotes the development of consistent information on land use and land management practices. This consortium of Australian, state and territory government partners is critical to providing nationally consistent land use mapping at both catchment and national scale, underpinned by common technical standards including an agreed national land use classification. ACLUMP provides a national land use data directory and the maintenance of land use datasets on Australian and state government data repositories. More information on ACLUMP available at www.abares.gov.au/landuse \r

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Abstract

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

The 3 second (~90m) Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) version 1.0 was derived from resampling the 1 arc second (~30m) gridded DEM (ANZCW0703013355). The DEM represents ground surface topography, and excludes vegetation features. The dataset was derived from the 1 second Digital Surface Model (DSM; ANZCW0703013336) by automatically removing vegetation offsets identified using several vegetation maps and directly from the DSM. The 1 second product provides substantial improvements in the quality and consistency of the data relative to the original SRTM data, but is not free from artefacts. Man-made structures such as urban areas and power line towers have not been treated. The removal of vegetation effects has produced satisfactory results over most of the continent and areas with defects are identified in the quality assessment layers distributed with the data and described in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010). A full description of the methods is in progress (Read et al., in prep; Gallant et al., in prep). The 3 second DEM was produced for use by government and the public under Creative Commons attribution.

The 3 second DSM and smoothed DEM are also available (DSM; ANZCW0703014216,

DEM-S; ANZCW0703014217).

Dataset History

Source data

1. SRTM 1 second Version 2 data (Slater et al., 2006), supplied by Defence Imagery and Geospatial Organisation (DIGO) as 813 1 x 1 degree tiles. Data was produced by NASA from radar data collected by the Shuttle Radar Topographic Mission in February 2000.

2. GEODATA 9 second DEM Version 3 (Geoscience Australia, 2008) used to fill voids.

3. SRTM Water Body Data (SWBD) shapefile accompanying the SRTM data (Slater et al., 2006). This defines the coastline and larger inland waterbodies for the DEM and DSM.

4. Vegetation masks and water masks applied to the DEM to remove vegetation.

5. 1 second DEM resampled to 3 second DEM.

1 second DSM processing

The 1 second SRTM-derived Digital Surface Model (DSM) was derived from the 1 second Shuttle Radar Topographic Mission data by removing stripes, filling voids and reflattening water bodies. Further details are provided in the DSM metadata (ANZCW0703013336).

1 second DEM processing (vegetation offset removal)

Vegetation offsets were identified using Landsat-based mapping of woody vegetation. The height offsets were estimated around the edges of vegetation patches then interpolated to a continuous surface of vegetation height offset that was subtracted from the DSM to produce a bare-earth DEM. Further details are provided in the 1 second DSM metadata (ANZCW0703013355).

Void filling

Voids (areas without data) occur in the data due to low radar reflectance (typically open water or dry sandy soils) or topographic shadowing in high relief areas. Delta Surface Fill Method (Grohman et al., 2006) was adapted for this task, using GEODATA 9 second DEM as infill data source. The 9 second data was refined to 1 second resolution using ANUDEM 5.2 without drainage enforcement. Delta Surface Fill Method calculates height differences between SRTM and infill data to create a "delta" surface with voids where the SRTM has no values, then interpolates across voids. The void is then replaced by infill DEM adjusted by the interpolated delta surface, resulting in an exact match of heights at the edges of each void. Two changes to the Delta Surface Fill Method were made: interpolation of the delta surface was achieved with natural neighbour interpolation (Sibson, 1981; implemented in ArcGIS 9.3) rather than inverse distance weighted interpolation; and a mean plane inside larger voids was not used.

Water bodies

Water bodies defined from the SRTM Water Body Data as part of the DSM processing were set to the same elevations as in the DSM.

Edit rules for land surrounding water bodies

SRTM edit rules set all land adjacent to water at least 1m above water level to ensure containment of water (Slater et al., 2006). Following vegetation removal, void filling and water flattening, the heights of all grid cells adjacent to water was set to at least 1 cm above the water surface. The smaller offset (1cm rather than 1m) could be used because the cleaned digital surface model is in floating point format rather than integer format of the original SRTM.

Some small islands within water bodies are represented as voids within the SRTM due to edit rules. These voids are filled as part of void filling process, and their elevations set to a minimum of 1 cm above surrounding water surface across the entire void fill.

Overview of quality assessment

The quality of vegetation offset removal was manually assessed on a 1/8 ×1/8 degree grid. Issues with the vegetation removal were identified and recorded in ancillary data layers. The assessment was based on visible artefacts rather than comparison with reference data so relies on the detection of artefacts by edges.

The issues identified were:

* vegetation offsets are still visible (not fully removed)

* vegetation offset overestimated

* linear vegetation offset not fully removed

* incomplete removal of built infrastructure and other minor issues

DEM Ancillary data layers

The vegetation removal and assessment process produced two ancillary data layers:

* A shapefile of 1/8 × 1/8 degree tiles indicating which tiles have been affected by vegetation removal and any issue noted with the vegetation offset removal

* A difference surface showing the vegetation offset that has been removed; this shows the effect of vegetation on heights as observed by the SRTM radar

instrument and is related to vegetation height, density and structure.

The water and void fill masks for the 1 second DSM were also applied to the DEM. Further information is provided in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010).

Resampling to 3 seconds

The 1 second SRTM derived Digital Elevation Model (DEM) was resampled to 3 seconds of arc (90m) in ArcGIS software using aggregation tool. This tool determines a new cell value based on multiplying the cell resolution by a factor of the input (in this case three) and determines the mean value of input cells with the new extent of the cell (i.e. Mean value of the 3x3 input cells). The 3 second SRTM was converted to integer format for the national mosaic to make the file size more manageable. It does not affect the accuracy of the data at this resolution. Further information on the processing is provided in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010).

Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_aac46307-fce9-449d-e044-00144fdd4fa6/SRTM-derived+3+Second+Digital+Elevation+Models+Version+1.0

Dataset Citation

Geoscience Australia (2010) Geoscience Australia, 3 second SRTM Digital Elevation Model (DEM) v01. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/12e0731d-96dd-49cc-aa21-ebfd65a3f67a.