AndreaBozzo/ceres-open-data-index huggingface dataset:
Indicators of Catchment Condition in the Intensive Land Use Zone of Australia – Feral animal density

    

It should be noted that this data is now somwhat dated!

Feral animals impact on catchment condition by added grazing pressure,
competition with native animals and by increased ground and water disturbance.
Feral animals can also host and distribute diseases such as Brucellosis and
mange. Rabbits and goats are renowned for their grazing impact, and including
pigs, can cause severe ground disturbance leading to erosion. Foxes and cats
are predatory on many native species and also transmit diseases. Buffalo cause
muddying of waterways and can harbour Brucellosis. Buffalo, horses and goats
can also assist the spread of weeds.

Feral animal density is an indicator of the extent to which Australia has been
colonised by a range of exotic fauna species. Native species numbers and
distributions have declined through direct predation, such as by foxes and
cats, overgrazing by rabbits and goats to changing habitat conditions through
competition for available resources. This issue is difficult to address at a
catchment scale due to animal mobility.

Regional/national strategies are required (eg. use of calicivirus to wipe out
rabbits). The main areas of Australia with relatively high feral animal counts
are Gippsland and the mountains of southern NSW. The Victoria River Downs (NT)
also shows with high feral animal density. Moderate to poor condition is
otherwise indicated throughout most of eastern Australia and in the north of
W and in the Northern Territory. Feral animal density is generally low in
most parts of Tasmania, South Australia, and Western Australia and in the
Riverina District of New South Wales. The ABARES/CSIRO feral animal coverages
were used, which date from the late 1980s.

The scale of mapping is 1:25M. These maps give qualitative spatial
distributions for 22 feral species. Four classes of feral animal density were
defined (high, medium, low and none) and assigned a numerical value (3, 2, 1
and 0). The density values for each species were added for all species, giving
a relative feral animal rating. Given the low resolution of the feral animal
coverages, only a broad regional picture is possible. Data reliability is
poor.

Data are available as:

  * continental maps at 5km (0.05 deg) cell resolution for the ILZ;
  * spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
  * spatial averages over the AWRC river basins in the ILZ.

See [further metadata](http://data.daff.gov.au/anrdl/metadata_files/pa_iccilr9ab
__07121axx.xml) for more detail.



Tags: environment, indicator, land, landcover, landuse, water
Organization: Australian Bureau of Agricultural and Resource Economics and Sciences
Indicators of Catchment Condition in the Intensive Land Use Zone of Australia – Catchment Condition Index

    

It should be noted that this data is now somwhat dated!

The overall Index of Catchment Condition was based on a combination of the 14
indicators used for the Sub-index assessments.

Method: Indicators equal area stretched into 5 classes are added and the
resultant grid resliced into 5 equal interval classes.

Datasets included in this assessment were: sedl, pest, indu, nutr, impn, st50,
sdeg, eros, frag, nati, prot, rden, fera, weed.

There are large, coherent areas with relatively poor condition in the wheat
and sheep zone of Western Australia, through central and western Victoria and
west Gippsland, and onto the western slopes and plains of New South Wales.

In Western Australia the poorest catchments are mainly in the mid to upper
slopes of the major catchments. This heterogeneity is masked at the AWRC basin
scale.

Most of sub-catchments in the intensive landuse zone in West Australia fall
into the category next to the poorest category.

In South Australia none of the sub-catchments fall into the poorest category.

The poorest areas are located in the Yorke Peninsula and in the catchments
around Adelaide and in the south-east corner of South Australia.

In the eastern states the catchments in poorer condition are now more confined
and disjunct, they tend to be located in the mid-slopes of the major east-west
trending catchments in New South Wales and south-central Victoria.

In the coastal catchments in New South Wales, the catchments in the corridor
between Sydney and Goulburn and the Hunter River catchment are in the poorest
category.

In Queensland, the catchments in poorer condition include the upper reaches of
the Condamine and Mackenzie River systems, and the more intensively developed
large river basins in central Queensland to south-east Queensland (Fitzroy
coastal, Burnett, Mary, Maroochy, Pine and Brisbane) and smaller coastal
basins with developed coastal plains.

Similar to the 5 km resolution, catchments in relatively good condition again
lie within the less intensively used parts of the assessment area (Northern
Territory, Cape York and inland New South Wales and South Australia).

However, in the higher rainfall zone the influence of conserved lands is less
and as a consequence good catchment condition areas are much more restricted.

Tasmania generally and in particular Southwest Tasmania, north-eastern
Victoria, the Blue Mountains National Park, parts of the Upper Clarence Basin
in New South Wales and the south-west of Western Australia are of relatively
better condition.

Data are available as:

  * continental maps at 5km (0.05 deg) cell resolution for the ILZ;
  * spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
  * spatial averages over the AWRC river basins in the ILZ.

See [further metadata](http://data.daff.gov.au/anrdl/metadata_files/pa_iccilr9ab
__07321axx.xml) for more detail.



Tags: environment, indicator, land, landcover, landuse, water
Organization: Australian Bureau of Agricultural and Resource Economics and Sciences
Indicators of Catchment Condition in the Intensive Land Use Zone of Australia – biota Condition Sub-index

    

It should be noted that this data is now somwhat dated!

The biota Condition Sub-index was based on six indicators:

  * Native vegetation fragmentation;
  * Native vegetation extent;
  * Protected areas;
  * Road density;
  * Feral animal density; and
  * Weed density.

Areas with relatively good condition are confined to a narrow strip of the
eastern ranges, south-west Tasmania, and the less intensively used Northern
Territory, Cape York and inland areas.

Data are available as:

  * continental maps at 5km (0.05 deg) cell resolution for the ILZ;
  * spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
  * spatial averages over the AWRC river basins in the ILZ.

See [further metadata](http://data.daff.gov.au/anrdl/metadata_files/pa_iccilr9ab
__07421axx.xml) for more detail.



Tags: environment, indicator, land, landcover, landuse, water
Organization: Australian Bureau of Agricultural and Resource Economics and Sciences
Independent Hospital Pricing Authority FOI Disclosure Log

This dataset does not contain any resources hosted on data.gov.au. It provides a link to the location of the Independent Hospital Pricing Authority Freedom of Information (FOI) disclosure log to aide in information and data discovery. You can find the [FOI Disclosure log here](http://www.ihpa.gov.au/internet/ihpa/publishing.nsf/Content/foi) and the Agency's [Information Publication Scheme here](http://www.ihpa.gov.au/internet/ihpa/publishing.nsf/Content/info-pub-sch-pl).

The data.gov.au team is not responsible for the contents of the above linked pages.

Tags: foi log
Organization: Department of Finance
Economic impacts of Xylella fastidiosa on the Australian wine grape and wine-making industries

Overview  
Xylella fastidiosa is a bacterium that affects many plant species. In Brazil X. fastidiosa infects an estimated 200 million citrus trees. In California, it causes over $100 million in yearly losses to the grape industry. And on the Italian peninsula of Salento, around one million olive trees are estimated to be infected. There is no cure for the disease. 

 Recent outbreaks in Europe prompted Australia's biosecurity authorities to introduce emergency measures in late 2015 to reduce the risk of an incursion. As part of the heightened preparedness, the Department's Plant Biosecurity Division requested ABARES to assess the economic impacts of a potential X. fastidiosa outbreak on the wine grape and wine-making industries in Australia. 

 ABARES assessed a range of scenarios and the expected benefits to the wine industry of keeping Australia free of X. fastidiosa and its vectors. Three scenarios of progressively smaller habitat suitability were assessed: 
• Scenario 1 - all existing wine grape growing areas 
• Scenario 2 - Scenario 1 areas with an average minimum winter temperature above 1.7 degrees C, and 
• Scenario 3 - Scenario 2 areas that were in close proximity to riparian vegetation. 

 ABARES estimated that if it enters and establishes in Australia, X. fastidiosa could cost the Australian grape vine industries between $2.8 billion to $7.9 billion, over 50 years, on a net present value (NPV) basis. ABARES also found that if X. fastidiosa appears in a region but was contained within that region, the aggregate impact on the wine industry would be a fraction of the impact of an uncontrolled spread. Containing the outbreak to either the Murray Darling - Swan Hill or Lower Murray regions, for example, could avoid losses estimated between $2.6 billion and $2.0 billion, on a NPV basis. 

Key Issues 
• X. fastidiosa is not present in Australia, but is a major concern. 
• If it established in Australia, X. fastidiosa would pose a significant threat to the productivity, sustainability and competitiveness of Australia's wine industry. 
• Habitat suitability in Australia is likely to be high. 
• The current level of investment in prevention measures to reduce the risk of X. fastidiosa entering Australia appear to be highly cost-effective. 
• International experience suggests that successful eradication is unlikely. Nonetheless, were it to enter, enhancing early detection capacity would be prudent. 



Tags: AGRICULTURE, Australia, Biota, DISEASE, Economy, Farming, INDUSTRY, INDUSTRY Primary, Xylella fastidiosa, biosecurity, disease spread, economic impact, economic modelling, outbreak, plant disease, spatial modelling, wine grape industry
Organization: Australian Bureau of Agricultural and Resource Economics and Sciences
Australia's merchandise trade by country (TRIEC) to FY2017

Australia's composition of merchandise trade, covering all countries, selected country groups and commodities. The data are consistent with the July 2017 release of catalogue 5368.0 although some totals may differ because of rounding and DFAT adjustments.
The data should be used in conjunction with the explanatory notes.

Tags: 2016-17, APEC, ASEAN, Australian economy, Australian trade, Commodity, Economy, European Union, Exports, Imports, OECD, TRIEC, composition of trade, country, economy, merchandise trade, trade
Organization: Department of Foreign Affairs and Trade
Australia's merchandise trade by country (TRIEC) to CY2016

Australia's composition of merchandise trade, covering all countries, selected country groups and commodity groups. The data are consistent with the June 2017 release of ABS catalogue 5368.0 although some totals may differ because of rounding and DFAT adjustments. The data should be used in conjunction with the explanatory notes.

Tags: 2016, APEC, ASEAN, Australian economy, Australian trade, Commodity, Economy, European Union, Exports, Imports, Level of processing, OECD, TRIEC, composition of trade, country, economy, merchandise trade, trade
Organization: Department of Foreign Affairs and Trade
2019 APS employee census

The 2019 APS employee census was administered to all available Australian Public Service (APS) employees. This census approach provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The census' content is designed to establish the views of APS employees on workplace issues such as leadership, learning and development, and job satisfaction. The census ran from 6 May to 7 June 2019. Overall, 104,471 APS employees responded to the employee census, a response rate of 77%.

Please be aware that the very large number of respondents to the employee census means these files are over 200 mb in size. Downloading and opening these files may take some time.

_TECHNICAL NOTES_

Three files are available for download.

* 2019 APS employee census - Questionnaire: This contains the 2019 APS employee census questionnaire.

* 2019 APS employee census - 5 point dataset.csv: This file contains individual responses to the 2019 APS employee census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document.

* 2019 APS employee census - 5 point dataset.sav: This file contains individual responses to the 2019 APS employee census for use with the SPSS software package. 

To protect the privacy and confidentiality of respondents to the 2019 APS employee census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.

Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author.  

A recommended short citation is: 2019 APS employee census data, Australian Public Service Commission. 

Any queries can be directed to research@apsc.gov.au.

Tags: APS, State of the Service, employee census
Organization: Australian Public Service Commission
Australia's merchandise trade by country (SITC) to CY2016

Australia's composition of merchandise trade, covering all countries, selected country groups and commodities. The data are consistent with the June 2017 release of ABS catalogue 5368.0 although some totals may differ due to rounding and DFAT adjustments. The data should be used in conjunction with the explanatory notes. 

Tags: 2016, APEC, ASEAN, Australian economy, Australian trade, Economy, European Union, OCED, SITC, commodity, composition of trade, country, economy, exports, imports, merchandise trade, trade
Organization: Department of Foreign Affairs and Trade
IMOS SOOP Sea Surface Temperature Data collected on the Merchant Vessel Sea Flyte (Rottnest Island Ferry)

This dataset contains Sea Surface Temperature (SST) underway observations collected on trips of a ferry (MV Sea Flyte - VHW5167) between Hillarys Boat Harbour and Rottnest Island (Western Australia).

The trips take place several times a day. The data are averaged to 1-minute from the original 7 to 15 seconds sampling interval. The data have been quality controlled by the Bureau of Meteorology.

Data are made available through IMOS approx 24 hours after collection.

Enhancement of Measurements on Ships of Opportunity (SOOP)-Sea Surface Temperature (SST) aims to supply near real-time  SST data (within 24 hours) from SOOPs and research vessels in the Australian region.

Tags: Indian Ocean, Ocean Temperature, Oceans, Sea Surface Temperature
Organization: CSIRO Oceans & Atmosphere - Hobart
IMOS SOOP Sea Surface Temperature Data collected on the Merchant Vessel Portland

This dataset contains Sea Surface Temperature (SST) underway observations collected on trips of a merchant vessel (MV Portland - VNAH ) between Fremantle (Western Australia) and Melbourne (Victoria).

The data have been quality controlled by the Bureau of Meteorology.

Enhancement of Measurements on Ships of Opportunity (SOOP)-Sea Surface Temperature (SST) aims to supply near real-time  SST data (within 24 hours) from SOOPs and research vessels in the Australian region.

Tags: Air Temperature, Atmosphere, Atmospheric Pressure, Atmospheric Temperature, Atmospheric Water Vapor, Dew Point, Indian Ocean, Ocean Temperature, Ocean Winds, Oceans, Sea Surface Temperature, South Australia, Southern Ocean, Surface Winds, Victoria, Western Australia
Organization: Bureau of Meteorology (BOM)
IMOS SOOP Sea Surface Temperature Data collected on the Merchant Vessel Highland Chief

This dataset contains Sea Surface Temperature (SST) underway observations collected on trips of a merchant vessel (MV Highland Chief - VROB) between south-east of Australia (Melbourne) and Papua New Guinea, including New Zealand, New Caledonia and Solomon Islands.

The data have been quality controlled by the Bureau of Meteorology.

Enhancement of Measurements on Ships of Opportunity (SOOP)-Sea Surface Temperature (SST) aims to supply near real-time  SST data (within 24 hours) from SOOPs and research vessels in the Australian region.

Tags: Air Temperature, Atmosphere, Atmospheric Pressure, Atmospheric Temperature, Atmospheric Water Vapor, Countries, Dew Point, New Caledonia, New Zealand, Ocean Temperature, Ocean Winds, Oceans, Pacific Ocean, Papua New Guinea, Regional Seas, Sea Surface Temperature, Solomon Islands, Solomon Sea, Surface Winds, Tasman Sea, Victoria
Organization: Bureau of Meteorology (BOM)
Australia's merchandise trade by country (SITC) to FY2017

Australia's composition of merchandise trade, covering all countries, selected country groups and commodities. The data are consistent with the July 2017 release of catalogue 5368.0 although some totals may differ because of rounding and DFAT adjustments.
The data should be used in conjunction with the explanatory notes.

Tags: 2016-17, APEC, ASEAN, Australian economy, Australian trade, Economy, European Union, Exports, Imports, OECD, SITC, commodity, composition of trade, country, economy, merchandise trade, trade
Organization: Department of Foreign Affairs and Trade
IMOS-SOOP-Air Sea Flux: Flux product from the Research Vessel Tangaroa

This dataset contains quality controlled meteorological observations and bulk air-sea fluxes. 

Enhancement of Measurements on Ships of Opportunity (SOOP) - Air Sea Flux (ASF) R.V. Tangaroa collects underway meteorological and oceanographic observations during its voyages in Sub-Antarctic. 

Data streams are fed into the existing ship data management system, and broadcast via satellite back to New Zealand daily. The observations are quality controlled at the Bureau of Meteorology and air-sea fluxes calculated using the COARE Bulk Flux algorithm. 

A daily file of 5-minute average calculated bulk fluxes are generated shortly after 0000UTC and provided to eMII.

The data is contained in NetCDF format files and consists of input QC'd meteorological observations as well as calculated air-sea fluxes. The fluxes are calculated using the COARE Bulk Flux algorithm version 3.0. Input observations are selected from multiple data-streams in the cases of; wind (upwind instrument selected); air temperature and humidity (upwind instrument selected), and short (maximum value used) and long-wave (minimum value used). Other input observations included are sea surface temperatures (SST) and air pressure. Calculated fluxes include, sensible heat, latent heat, outgoing long-wave, outgoing short-wave and heat due to precipitation. The calculated skin SST value is included. Flags are provided to indicate which sensor (when multiple are available) has been used as input to compute the flux. QC flags are not provided as fluxes are only computed when good data is available.

Tags: Air Temperature, Atmosphere, Atmospheric Pressure, Atmospheric Temperature, Atmospheric Water Vapor, Atmospheric Winds, Coral Sea, Countries, Heat Flux, Humidity, Longwave Radiation, Net Radiation, New Zealand, Ocean Heat Budget, Ocean Optics, Ocean Temperature, Ocean Winds, Oceans, Pacific Ocean, Photosynthetically Active Radiation, Precipitation, Precipitation Rate, Radiation Budget, Radiative Flux, Regional Seas, Sea Surface Temperature, Shortwave Radiation, Skin Temperature, Southern Ocean, Surface Air Temperature, Surface Winds, Tasman Sea
Organization: Bureau of Meteorology (BOM)
IMOS-SOOP-Air Sea Flux: Flux product from the Research Vessel Southern Surveyor

This dataset contains quality controlled meteorological observations and bulk air-sea fluxes. 

Enhancement of Measurements on Ships of Opportunity (SOOP) - Air Sea Flux (ASF) Southern Surveyor collects underway meteorological and oceanographic observations during scientific voyages in the oceans adjacent to Australia. 

Data streams are fed into the existing ship data management system, and broadcast via satellite back to Australia 3-hourly. The observations are quality controlled at the Bureau of Meteorology and air-sea fluxes calculated using the COARE Bulk Flux algorithm. 

A daily file of 5-minute average calculated bulk fluxes are generated shortly after 0000UTC and provided to eMII.

The data is contained in NetCDF format files and consists of input QC'd meteorological observations as well as calculated air-sea fluxes. The fluxes are calculated using the COARE Bulk Flux algorithm version 3.0. Input observations are selected from multiple data-streams in the cases of; wind (upwind instrument selected); air temperature and humidity (upwind instrument selected), and short (maximum value used) and long-wave (minimum value used). Other input observations included are sea surface temperatures (SST) and air pressure. Calculated fluxes include, sensible heat, latent heat, outgoing long-wave, outgoing short-wave and heat due to precipitation. The calculated skin SST value is included. Flags are provided to indicate which sensor (when multiple are available) has been used as input to compute the flux. QC flags are not provided as fluxes are only computed when good data is available.

Tags: Air Temperature, Atmosphere, Atmospheric Pressure, Atmospheric Temperature, Atmospheric Water Vapor, Atmospheric Winds, Australia, Coral Sea, Countries, Fiji, Heat Flux, Humidity, Indian Ocean, Longwave Radiation, Net Radiation, New Caledonia, New Zealand, Ocean Heat Budget, Ocean Optics, Ocean Temperature, Ocean Winds, Oceans, Pacific Ocean, Photosynthetically Active Radiation, Precipitation, Precipitation Rate, Radiation Budget, Radiative Flux, Regional Seas, Sea Surface Temperature, Shortwave Radiation, Skin Temperature, Southern Ocean, Surface Air Temperature, Surface Winds, Tasman Sea
Organization: Bureau of Meteorology (BOM)