Introduction

Welcome to the Australian English General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of English speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Australian English communication.

Curated by FutureBeeAI, this 40 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade English speech models that understand and respond to authentic Australian accents and dialects.

Speech Data

The dataset comprises 40 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Australian English. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple English speech and language AI applications:

Intended destination (eg employment, further education) of students who have left the SA government school system or moved schools within the SA government school system. Data is for the previous 12 months (February to February), each year from 2014. Important notes: • Includes students who have left the SA government school system or moved schools within the SA government school system. • Destination data is entered by school at the time of departure and may not reflect the final destination. • Students may be counted more than once if they left the SA government school system and re-entered within the same year. • Ages 4 years and under are excluded. • Students with a Full-time Equivalent (FTE) of less than 0.4 are excluded from the person count.

This dataset presents counts of payment recipients by payment type for the quarter ending September 2016. The data is based on the recipient's geocoded address aggregated by Local Government Area (LGA) from the Australian Statistical Geography Standard (ASGS) 2014.

The Department of Social Services (DSS) is the Australian Government's lead agency in the development and delivery of social policy, and is working to improve the lifetime wellbeing of people and families in Australia. DSS' policies and services respond to need across people's lives - looking after families, children and older people; providing a safety net for people who cannot fully support themselves; enhancing the wellbeing of people with high needs; assisting people who need help with care; and supporting a diverse and harmonious society. DSS supports people and families in Australia by encouraging independence and participation, and supporting a cohesive society.

For more information on this dataset please visit the Australian Government Open Data.

For further details on payments, see 'A guide to Australian Government payments'.

Please note:

• In order to protect individuals' privacy, identified populations between 1 and 4 have been suppressed and replaced with '<5' for confidentiality purposes. Additional data may be suppressed and replaced with 'n.p.' (not provided) to prevent the derivation of identified populations that have values of less than 5.

• Individuals who live overseas, individuals who are without a valid home address and individuals who only have a postal address cannot be assigned to a locational boundary. State/territory totals derived from Local Government Area data will not match actual state and territory totals, due to different statistical geography methodologies.

• Caveats specific to payments type can be found in the relevant payment description of the original data.

  AURIN has spatialised this dataset and have made the following replacements to enforce data type consistency:

• '<5' has been assigned a value of '-1' within the data

• A boolean field has been created for each data field to flag left-censored data. 'True' is assigned to fields that have been censored.

• 'n.p.' (not provided) has been set to Null within the data.

River foreshore condition of the left bank, assessed and classed using the Pen-Scott classification method. The Pen-Scott method is a standardised rating technique that classifies foreshore areas along a gradient from pristine (A grade) through to D grade, connoting a ditch. The four grades are further divided into three sub-categories per grade, i.e. A1, A2 and A3. A1: Pristine A2: Near pristine A3: Slightly disturbed B1: Degraded – weed infested B2: Degraded – heavily weed infested B3: Degraded – weed dominated C1: Erosion prone C2: Soil exposed C3: Eroded D1: Ditch - eroding D2: Ditch – feely eroding D3: Drain – weed dominated See specific reports for more information and pictorial examples. See Pen, L.J. and Scott, M. (1995) Stream foreshore assessment in farming areas. Blackwood Catchment Coordinating Group and Water and Rivers Commission (1999) River Restoration Reports No. RR2 and RR3 for foreshore condition assessment methods. Left and right bank are relative to an observer facing downstream. DISCLAIMER: While the dataset has been prepared by the Department of Water and Environmental Regulation, it contains information from State and federally funded foreshore assessment projects conducted at different times by Natural Resource Management groups with support from DWER regional offices. It should be noted that for any given location, the data provides a ‘snapshot’ of the attributes recorded at one specific time. Any information or representation expressed or implied in this database is made in good faith and on the basis that the Department of Water and Environmental Regulation and its employees are not liable for any damage or loss whatsoever which may occur as a result of action taken or not taken, as the case may be in respect of any information or representation referred to herein. Professional advice should be obtained to verify the information contained in this database before applying to particular circumstances. The Department of Water and Environmental Regulation accepts no responsibility for collecting or updating this data but some known errors are being addressed. This dataset was formerly known as Foreshore Conditions - Left Bank Condition (DOW-048)

Indicators describing the levels of different aspects of wellbeing within the South Australian population: Children -Children aged less than 15 years living in jobless families, 2011 -Children …Show full descriptionIndicators describing the levels of different aspects of wellbeing within the South Australian population: Children -Children aged less than 15 years living in jobless families, 2011 -Children developmentally vulnerable in one or more domains, 2012 -Early school leavers who left school at Year 10 or below, or did not go to school, 2011 Unemployment -Unemployment benefits recipients, June 2014 -Young people aged 16 to 24 years receiving an unemployment benefit, June 2014 -Young people aged 15 to 24 years engaged in learning or earning, 2011 Health -Prevalence of high or very high psychological distress, 2011-13 -Smoking: persons, 2011-13 -Obesity: adults, 2011-13 Overview here: http://dcsi.maps.arcgis.com/apps/MapJournal/index.html?appid=1b84d1ccea924d58ad8247d9f7e8395a

This dataset contains maps showing the principal attributes of tides around the Australian coast. It has been derived from data published in the Australian National Tide Tables.

Format: shapefile.

Quality - Scope: Dataset. External accuracy: +/- one degree. Non Quantitative accuracy: Data are assumed to be correct. Three datasets describe tidal information around Australia:

Cover_Name, Item_Name, Item_Description:

TIDEMAX, MAX_TIDE_(M), Maximum tidal range in metres.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Data was projected to geographics using the WGS84 datum and spheroid, to be compatible for the Australian Coastal Atlas. The digital datsets were attributed using the information held in the legend (.key) files.

CSIRO: All CAMRIS data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary follows of processing completed by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and The Complete Book of Australian Weather). 2. From the information held in r-BASE a BASE Table was generated incorporating specific fields. 3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point. 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - created from the BASE map and table by selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level). 6. Rasterise maps specifying key parameters such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn). 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid. 8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

The Australian Longitudinal Study of Ageing, which ran from 1992 to 2014, was devised to generate longitudinal data over multiple time points. Thirteen waves were carried out. Waves 1, 3, 6, 7, 9, 11 and 12 comprised of a full face-to-face ‘household’ interview and a clinical assessment. Waves 2, 4, 5, 8, 10, 13 consisted of shorter telephone household interviews.The initial sample of the older old (70 and older) was randomly drawn from the database of the South Australian Electoral Roll. Persons in the older age groups as well as males were deliberately oversampled to compensate for the higher mortality that could be expected over the study period. In addition, spouses of primary respondents (aged 65 and over) and other household members aged 70 and over were asked to participate. 2087 participants were initially interviewed at Wave 1 in 1992. Over the years, attrition due to either death, ill health, moving out of scope, being uncontactable, or refusal has reduced the number of participants to 94 in 2014. Information covering the data, questionnaires and relevant details are openly available.Items in the household interview schedule represent a comprehensive set of measures chosen for their reliability and validity in previous studies, sensitivity to change over time, and suitability for use in a study of elderly persons. The domains assessed included demography, health, depression, morbid conditions, hospitalisation, hearing and vision difficulties, cognition, gross mobility and physical performance, activities of daily living and instrumental activities of daily living, lifestyle activities, exercise education and income.At the completion of the household interview, participants were left with self-administered questionnaires, which were mailed back in pre- paid envelopes or collected at the time of the clinical assessment. The domains covered by the questionnaires were dental health, sexual activity and psychological measures of self-esteem, morale and perceived control.The individual clinical assessment objectively measured both physical and cognitive functioning. The physical examination included measures of blood pressure, anthropometry, visual acuity, audiometry and physical performance. The cognitive assessment included measures of memory, information processing efficiency, verbal ability and executive function. The clinical assessments were conducted by nurses who received special training in the standard administration of all psychological instruments and the anthropometric measures. In addition, fasting blood samples and urine specimens were collected on the morning following the clinical assessment at Wave 1, and blood samples were again taken at Wave 3.Some data have been provided by secondary sources. Participant deaths have been systematically monitored through the government Registry of Births, Deaths and Marriages.From Wave 7 onward, collateral data were gathered from the files of the Health Insurance Commission (HIC). Permission was sought for access to the Health Insurance Commission HIC for purposes of establishing use of medical care and services and expenditure. The information sought from the HIC database included: the number of medical care services, and for each service, the nature of the service, date, charge, and benefit; the number of PBS prescriptions, and for each prescription, the drug prescribed, number of repeats, date, charge, and benefit.

The total number of registered motor vehicles shows continued and sustained growth over time. While the number of electric and hybrid vehicles registered in Queensland has risen significantly in the last 5 years, particularly passenger vehicles, they represent only 1% of all cars and light commercial vehicles.

Released under formal Government Information Public Access (GIPA) Application to Department of Finance, Services and Innovation (DFSI) - FA#52 15-16

"1. Keep to the left unless overtaking in a multi-laned road where signposted or the speed limit exceeds 80km/h. (Australian Road Rules 180).

1. When a driver is travelling on a road without lane markings and the number of lanes is reduced, they must merge by giving way to any vehicle that is ahead of them (ARR147)

2. Drivers approaching a roundabout must use their indicators when turning or making a U-turn. (ARR 112, 113)

3. High beam not permitted if travelling less than 200 meters behind a car going into the same direction or less than 200 metres from an oncoming vehicle. (ARR 218)

4. When making a U-tum a driver must have a clear view of any approaching traffic and give way to all vehicles and pedestrians. (ARR 38)

5. Drivers must stay three seconds behind the vehicle in front of them (ARR 126)

6. Not staying in lane on a multi-laned road while making right hand tum.

7. Driving too slowly under the speed limit. (ARR 125)"

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.

WOfS is a gridded dataset indicating areas where surface water has been observed using the Geoscience Australia (GA) Earth observation satellite data holdings. The WOfS product Version 1.0 includes observations taken between 1998 and 2012 (inclusive) from the Landsat 5 and 7 satellites. WOfS version 1.5 includes observations from 1987 to March 2014. Future versions of the product will extend the temporal range and diversify the data sources. WOfS covers all of mainland Australia and Tasmania but excludes off-shore Territories.

Dataset History

16 2-degree tiles were downloaded from the wofs website (http://intranet.ga.gov.au/confluence/display/GEMDNEO/Water+Observations+from+Space+%28WOfS%29+Access+Details) using the following parameters:

Format - GeoTiff

Height - 8000

Width - 8000

Data Type - Water Summary v1.0

The name and URL used to download each tile is as follows, names are differentiated by the top left corner latitude and longitude:

1. NFRIP-WOfS-WaterSummary_v1.0_E140_S22.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=140,-22,142,-20&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

2. NFRIP-WOfS-WaterSummary_v1.0_E142_S22.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=142,-22,144,-20&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

3. NFRIP-WOfS-WaterSummary_v1.0_E144_S22.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=144,-22,146,-20&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

4. NFRIP-WOfS-WaterSummary_v1.0_E146_S22.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=146,-22,148,-20&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

5. NFRIP-WOfS-WaterSummary_v1.0_E140_S24.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=140,-24,142,-22&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

6. NFRIP-WOfS-WaterSummary_v1.0_E142_S24.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=142,-24,144,-22&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

7. NFRIP-WOfS-WaterSummary_v1.0_E144_S24.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=144,-24,146,-22&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

8. NFRIP-WOfS-WaterSummary_v1.0_E146_S24.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=146,-24,148,-22&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

9. NFRIP-WOfS-WaterSummary_v1.0_E140_S26.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=140,-26,142,-24&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

10. NFRIP-WOfS-WaterSummary_v1.0_E142_S26.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=142,-26,144,-24&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

11. NFRIP-WOfS-WaterSummary_v1.0_E144_S26.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=144,-26,146,-24&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

12. NFRIP-WOfS-WaterSummary_v1.0_E146_S26.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=146,-26,148,-24&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

13. NFRIP-WOfS-WaterSummary_v1.0_E140_S28.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=140,-28,142,-26&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

14. NFRIP-WOfS-WaterSummary_v1.0_E142_S28.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=142,-28,144,-26&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

15. NFRIP-WOfS-WaterSummary_v1.0_E144_S28.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=144,-28,146,-26&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

16. NFRIP-WOfS-WaterSummary_v1.0_E146_S28.tif - http://eos.ga.gov.au/geoserver/NFRIP-WOfS/wcs?service=WCS&version=1.0.0&request=GetCoverage&coverage=NFRIP-WOfS:WaterSummary_v1.0&bbox=146,-28,148,-26&crs=epsg:4326&format=image/geotiff&width=8000&height=8000

Water Observations from Space (WOfS) is derived from Landsat-5 and Landsat-7 satellite imagery acquired over Australia between 1987 and 2014. The Landsat data underpinning WOfS is ARG25 standard data located in the Australian Geoscience Data Cube (AGDC) at the National Computational Infrastructure (NCI) in the Australian National University (ANU), Canberra. The WOfS product is calculated from all acceptable Landsat scenes in the Geoscience Australia archive for the time period. The detection process is based on spectral analysis of each pixel in each Landsat scene. The water detecton algorithm used to detect water from each observed pixel is based on a statistical regression tree analysis of a set of normalised difference indices and corrected band values. The regression is based on a set of water and non-water samples created by visual interpretation of 20 Landsat scenes from across Australia. The sample locations, ensure that the logistic regression is based on the full geographic range of conditions experienced in Australia. The regression analysis determined a set of best indices and bands for the analysis and the associated thresholds in each component to derive a final classification tree, producing a water/non-water classification for every pixel in the Data Cube. The final water classification for each pixel is modified by Pixel Quality (see associated RG25 - PQ product information) and terrain. Once the water algorithm has completed its process, the water detection for a pixel through time is combined to produce a total number of water observations for each pixel. This is compared to a total number of clear observations for the same pixel, derived from the PQ analysis. The ratio is expressed as a percentage water recurrence. A separate analysis produces a confidence dataset, providing an assessment on whether a pixel depicted as having had water detected at some time is likely. The layer is computed by combining a set of confidence factors using a weighted sum approach, with the weightings derived by logistic regression.

The confidence factors are:

1. MrVBF, a multi-resolution valley bottom flatness product (Gallant et al., 2012) derived from SRTM as part of the Terrestrial Ecosystems Research Network. Surface water pixels identified in valley bottoms were more likely to be positively detected.

2. Slope derived from SRTM Digital Surface Models. Water pixels on a slope were considered less plausible than those on a flat surface.

3. MODIS Open Water Likelihood (OWL) (Ticehurst et al, 2010) provides a plausibility based an independent water detection algorithm employing the MODIS sensor. If both detection algorithms agree on the presence of a surface water pixel, there is a greater plausibility that the detection is correct.

4. Australian Hydrological Geospatial Fabric (Geofabric) is a GIS of hydrological features derived from manually interpreted topographic map grids. If known hydrologic features (pixels) from GeoFabric coincide with detected water pixels, the plausibility of detection is greater.

5. P, the number of observations of water as a fraction of the number of clear observations of the target pixel. P is high for more permanent water bodies.

6. Built-Up areas indicating areas of dense urban development. In such areas the water detection algorithm struggles to cope with the deep shadows cast by multi-story buildings and the generally noisy spectral response created by structures. The Built-Up layer is derived from the Ausralian Bureau of Statistics ASGS 2011 dataset, for urban centres of populations of 100 000 and over.

The product creation workflow is as follows:

1. Landsat raw data capture and storage

2. Data pre-processing (ARG25 and PQ products)

3. Water detection

4. Pixel Quality filtering

5. Data product storage and delivery

6. Time series data preparation

7. Summary and extent data preparation

8. Application of Confidence information

9. WMS/WCS service delivery

http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_81568

Dataset Citation

Geoscience Australia (2014) Water Observations From Space - Water Summary v1.0: Galilee Extent. Bioregional Assessment Source Dataset. Viewed 06 May 2016, http://data.bioregionalassessments.gov.au/dataset/250e920c-e99f-4390-9455-a5692e0c9c86.

Indicators describing the levels of different aspects of wellbeing within the South Australian population: Children -Children aged less than 15 years living in jobless families, 2011 -Children developmentally vulnerable in one or more domains, 2012 -Early school leavers who left school at Year 10 or below, or did not go to school, 2011 Unemployment -Unemployment benefits recipients, June 2014 -Young people aged 16 to 24 years receiving an unemployment benefit, June 2014 -Young people aged 15 to 24 years engaged in learning or earning, 2011 Health -Prevalence of high or very high psychological distress, 2011-13 -Smoking: persons, 2011-13 -Obesity: adults, 2011-13 Overview here: http://dcsi.maps.arcgis.com/apps/MapJournal/index.html?appid=1b84d1ccea924d58ad8247d9f7e8395a

Transfer parameter data are essential inputs to models for radiological environmental impact assessment and are used to quantify the extent of movement of radionuclides from one environmental compartment to another, relevant for estimating the transfer of radionuclides through food chains to humans. International data compilations (i.e. transfer parameter data for temperate environments from the IAEA Technical Reports Series No. 472) have been frequently used by regulators and professionals in radiological impact assessment for dose estimations when site-specific data are not available.

Description of Dataset Content

This international compilation of radionuclide and stable isotope soil-plant concentration ratio values for tropical environments is an output of IAEA’s Modelling and Data for Radiological Impact Assessments II (MODARIA II) programme (2016–2019) and is based on the Köppen-Geiger climate classification (BECK et al. 2018). The IAEA’s MODARIA II tropical dataset is associated with IAEA’s TECDOC-1979: Soil-Plant Transfer of Radionuclides in Non-Temperate Environments (2021).

The dataset contains over 7000 records. Each record includes a concentration ratio value and/or plant and soil concentrations, provided in a consistent way, from which a concentration ratio value can be calculated. Where available, environmentally relevant information is included with each record to allow categorization of the plant and soil data into more refined subsets.

The dataset includes information for over 100 plant species, including many that are common crops and staple foods in tropical environments. Data are included for all measured plant compartments, including both the edible and inedible parts of the plant.

Information in the dataset is organized into 41 fields, with individual lines in ascending order of their source reference. These headline fields are described in the associated ‘Explanatory Information’ file, while a description of the dataset content can be found in the ‘Dataset content‘ file.

Use of Data

The IAEA’s MODARIA II tropical dataset is freely available for all external users, without prejudice to the applicable IAEA’ Terms and Conditions.

Any use of the tropical dataset shall contain appropriate acknowledgement of the data source(s) and the IAEA’s Data Platform [online].

The preferred form of citation of IAEA’s MODARIA II tropical dataset is:

INTERNATIONAL ATOMIC ENERGY AGENCY, IAEA’s MODARIA II Soil-Plant Transfer Parameter Dataset for Tropical Environments. In: IAEA Data Platform [online], IAEA, Vienna (2021). https://ckan.iaea.production.datopian.com/dataset/modaria

Acknowledgement

The IAEA wishes to express its gratitude to C. Doering (Australia) for compiling this comprehensive dataset as part of the activities of Working Group 4 of the MODARIA II programme, led by B. Howard (UK). The IAEA also gratefully acknowledges the valuable contributions of J. Twining (Australia) and S. Rout (India).

How do I Search for Data?

The ‘Explore’ tab, on the right corner of the first page, allows users to explore the data online (by selecting the ‘Preview’ tab or by accessing the CSV-type file under ‘Data and Resources’) or to retrieve the whole dataset as a CSV-type file by selecting the ‘Download’ tab. To search for data in the online preview mode, use the filter control panel on the left of the ‘Data Explorer’ page. Click ‘Download’ at the top right of the page to download the data as a CSV file.

Get Involved

Would you like to learn more about the IAEA’s MODARIA II tropical dataset, or do you have questions related to data compilation? Get in touch with the IAEA’s team at the Terrestrial Environmental Radiochemistry Laboratory and at the Assessment and Management of Environmental Releases Unit by accessing the ‘Contact dataset maintainer’ tab. We will get back to you soon.

Separations: Yearly headcount of people who have left the Department for Education. The total represents separations over the full twelve month period. Unpaid leave: Total employees who were on unpaid leave from the Department for Education as at the end of the reporting period. The total represents a snapshot of people on unpaid leave. Data is from the last pay period in June each year from 2014.

The NSW Post-School Destinations and Experiences Survey (PSDES) collects information about the main destinations of recent school leavers in the 6 to 12 months after leaving school.

Data Notes

• The survey collected data on school leavers in the 6-12 months after leaving school in 2023, School leavers comprise students who completed Year 12 and students who left school while they were in Year 10, 11 or 12 (early school leavers).

• There are some caveats and limitations in the generalisability of survey findings to the total population of recent school leavers in NSW. For example, students who completed Year 12 via an alternative pathway other than the HSC, such as the International Baccalaureate, are not counted as Year 12 completers and are not covered in the survey.

• Prior to 2021 a stratified sampling approach was used for the mainstream Year 12 completer survey (excluding Aboriginal and/or Torres Strait Islander and non-Connected Community school leavers). The sampling strategy for this group changed to a census for the first time in 2021 and resulted in a marked increase in the overall proportion of responses collected from the target population.

• Time series data of destinations by student type from 2014 to 2018 should be used with caution as some counts of school leavers are estimated from lower cell counts than in later years. Estimates in the data are based on base weights which are adjusted to matched population distributions for school leaver characteristics to minimise non-response bias.

• Each table shows population estimates (as column totals) for each grouping variable and leaver type combination as well as weighted percentages for each of the 10 main destination categories included in the survey. Population estimates and destination percentage breakdowns are also included for all leavers (across leaver type). Findings are reported at a system level (across leavers from government and non-government schools).

• For a full description of notes and caveats, see the 2023 Post-School Destinations and Experiences Survey Technical Report

• See the 2023 Post-School Destinations and Experiences Survey, Annual Report and fact sheets

Data Source

NSW Post-School Destinations and Experiences Survey

Available tables in this dataset:

• Table 1 provides a breakdown of main destination by leaver type and survey year (2014 to 2023).
• Table 2 provides a breakdown of main destination by leaver type and gender (as self-identified) for 2023 only.
• Table 3 provides a breakdown of main destination by leaver type and Aboriginal status (as self-identified) for 2023 only.

Definition For the purposes of this dataset, Rail or Railway is defined as: A transportation system using one or more rails to carry freight or passengers. Abstract The Foundation Rail Infrastructure feature dataset is part of the Foundation Spatial Data Framework theme for Transport. The Foundation Rail Infrastructure feature dataset is specifically made up of Rail line features (Railways, Rail Sidings and Tramline including Light Rail) and Rail points (Stations). This feature class represents a national aggregation of the spatial locations and attributes of line and point features, of publicly available data. Rail Infrastructure information has been derived from various sources provided by data custodians including Spatial Services (NSW), Department of Natural Resources, Mines and Energy (QLD), Department of Environment, Land, Water and Planning (VIC), Land Tasmania (TAS) and Department of Infrastructure and Transport (SA). The coverage is across all states and territories however due to restrictive licensing, Geoscience Australia data was used as the source data for Western Australia (lines and points), the Northern Territory (lines and points) and South Australia (points). Data published by Victoria falling within South Australia has been included (points). Further information on datasets provided by State and Territory custodians can be found under Source Information in this metadata statement. Currency Date modified: 7 June 2021 Modification frequency: As needed Data extent Spatial extent North: -12.424680° South: -43.455040° East: 153.613384° West: 113.616611° Source information eCat published version at https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/145400 Lineage statement Data supplied to Geoscience Australia from the custodians was processed to perform geometry validation (checks to remove corrupt geometries) and transform the attribute values to the national schema. Minor spatial modifications have taken place to update attribute information in the source data for the national specification. During translation, the length of the features in kilometres was also calculated (LENGTH_KM) using a geodesic distance calculation. The Digital Atlas data team have republished and hosted the data as three separate layers:

Railways, Rail Sidings, Tram Lines.

The symbology applied displays the layers as operational or not operational, however queries can be performed using any operational status, refer to the data dictionary for filters and query options. The date of extraction May 2023. Data dictionary Railway Lines

Attribute name Description

OBJECTID Automatically generated system ID

FEATURESUBTYPE An identifier for the type of feature represented: 90015 — Railway90016 — Rail Siding90017 — Tramline

NAME The name of the feature

OPERATIONAL_STATUS The Operational status of the feature: Operational — Fully capable of operation.Abandoned — Not in operation due to it being non-functional and operation is not scheduled to be restored.Disused — Temporarily not in operation due to it being non-functional and operation is scheduled to be restored.Proposed — Proposed infrastructure.Unknown — There is no information specified regarding the attribute value.Other — The attribute value is known, but is not currently a valid member of the attribute range.Under Construction — The feature is currently being constructedClosed — The feature is currently closed for use or accessDismantled — The feature has been physically removed

FEATURE_DATE This is the date of the latest edit of the source data.

FEATURE_SOURCE This is the name of the latest source used to add, update or verify a features existence or position. In most cases, this would be imagery (satellite, orthophotography, World Imagery).

ATTRIBUTE_DATE This is the date of the latest source material used to initially assign, or subsequently change or confirm the value of, one of the attributes of the feature.

ATTRIBUTE_SOURCE This is the name of the latest source material used to populate the attribute field/s of a feature.

PLANIMETRIC_ACCURACY The standard deviation of the horizontal positional accuracy.

SOURCE_UFI The unique identifier of the feature as represented in the source.

SOURCE_JURISDICTION The jurisdiction of the feature source.

CUSTODIAN_AGENCY The agency or organisation for the source of this feature.

CUSTODIAN_LICENCING Specific licensing relating to this feature.

LOADING_DATE Date of data loaded into national model.

SOURCE_SUPPLY_DATE Date of source supply to Geoscience Australia for loading into the national schema, usually the date the data is downloaded from the custodian site

TRACK_GAUGE The gauge is the spacing of the rails on a railway track and is measured between the inner faces of the load-bearing rails. Not Applicable — Gauge is not applicable for this featureStandard: 1435mm — Standard gaugeBroad: 1600mm — Broad gaugeNarrow: 1067mm — Narrow gaugeLight — Light rail gaugeOther — A different type of rail gaugeUnknown — The gauge is not known for this featureStandard-Broad — The feature has a mix of both standard and broad gaugesStandard-Narrow — The feature has a mix of both standard and narrow gauges

GROUND_RELATIONSHIP The relative relationship of the railway to the ground. Unknown — Relationship to the surface is unknownOnBridge — The feature utilises a bridgeInTunnel — The feature is a tunnel through the groundOn Ground — The feature is located on the groundOther — Other relationship exists for the feature

TRACKS The number of railway tracks the feature represents. Unknown — It is not known how many tracks the feature representsSingle — The feature represents a single railway trackMultiple — The feature represents multiple railway tracksNot Applicable — Tracks is not applicable to this feature

LENGTH_KM The length of the feature in kilometres (calculated by projecting SHAPE_LENGTH in the source data).

ALTERNATIVE_NAME An alternative name for the feature or section name.

OWNER The owner of the feature.

SHAPE_Length Automatically generated length in decimal degrees.

Known Limitations of the Data These data have some known issues. The content and quality of the data varies on the state provider. Contact Geoscience Australia, clientservices@ga.gov.au