This Australian and New Zealand food category cost dataset was created to inform diet and economic modelling for low and medium socioeconomic households in Australia and New Zealand. The dataset was created according to the INFORMAS protocol, which details the methods to systematically and consistently collect and analyse information on the price of foods, meals and affordability of diets in different countries globally. Food categories were informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods.

Methods The dataset was created according to the INFORMAS protocol [1], which detailed the methods to collect and analyse information systematically and consistently on the price of foods, meals, and affordability of diets in different countries globally.

Cost data were collected from four supermarkets in each country: Australia and New Zealand. In Australia, two (Coles Merrylands and Woolworths Auburn) were located in a low and two (Coles Zetland and Woolworths Burwood) were located in a medium metropolitan socioeconomic area in New South Wales from 7-11th December 2020. In New Zealand, two (Countdown Hamilton Central and Pak ‘n Save Hamilton Lake) were located in a low and two (Countdown Rototuna North and Pak ‘n Save Rosa Birch Park) in a medium socioeconomic area in the North Island, from 16-18th December 2020.

Locations in Australia were selected based on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) [2]. The index ranks areas from most disadvantaged to most advantaged using a scale of 1 to 10. IRSAD quintile 1 was chosen to represent low socio-economic status and quintile 3 for medium SES socio-economic status. Locations in New Zealand were chosen using the 2018 NZ Index of Deprivation and statistical area 2 boundaries [3]. Low socio-economic areas were defined by deciles 8-10 and medium socio-economic areas by deciles 4-6. The supermarket locations were chosen according to accessibility to researchers. Data were collected by five trained researchers with qualifications in nutrition and dietetics and/or nutrition science.

All foods were aggregated into a reduced number of food categories informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods. Nutrient data for each food category can therefore be linked to the Australian Food and Nutrient (AUSNUT) 2011-13 database [4] and NZ Food Composition Database (NZFCDB) [5] using the 8-digit codes provided for Australia and New Zealand, respectively.

Data were collected for three representative foods within each food category, based on criteria used in the INFORMAS protocol: (i) the lowest non-discounted price was chosen from the most commonly available product size, (ii) the produce was available nationally, (iii) fresh produce of poor quality was omitted. One sample was collected per representative food product per store, leading to a total of 12 food price samples for each food category. The exception was for the ‘breakfast cereal, unfortified, sugars ≤15g/100g’ food category in the NZ dataset, which included only four food price samples because only one representative product per supermarket was identified.

Variables in this dataset include: (i) food category and description, (ii) brand and name of representative food, (iii) product size, (iv) cost per product, and (v) 8-digit code to link product to nutrient composition data (AUSNUT and NZFCDB).

References

Vandevijvere, S.; Mackay, S.; Waterlander, W. INFORMAS Protocol: Food Prices Module [Internet]. Available online: https://auckland.figshare.com/articles/journal_contribution/INFORMAS_Protocol_Food_Prices_Module/5627440/1 (accessed on 25 October).
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016 Available online: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/2071.0~2016~Main Features~Socio-Economic Advantage and Disadvantage~123 (accessed on 10 December).
Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. Available online: https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html#2018 (accessed on 10 December)
AUSNUT 2011-2013 food nutrient database. Available online: https://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 15 November).
NZ Food Composition Data. Available online: https://www.foodcomposition.co.nz/ (accessed on 10 December)

Usage Notes The uploaded data includes an Excel spreadsheet where a separate worksheet is provided for the Australian food price database and New Zealand food price database, respectively. All cost data are presented to two decimal points, and the mean and standard deviation of each food category is presented. For some representative foods in NZ, the only NFCDB food code available was for a cooked product, whereas the product is purchased raw and cooked prior to eating, undergoing a change in weight between the raw and cooked versions. In these cases, a conversion factor was used to account for the weight difference between the raw and cooked versions, to ensure that nutrient information (on accessing from the NZFCDB) was accurate. This conversion factor was developed based on the weight differences between the cooked and raw versions, and checked for accuracy by comparing quantities of key nutrients in the cooked vs raw versions of the product.

The number of COVID-19 vaccination doses administered per 100 people in Australia rose to 243 as of Oct 27 2023. This dataset includes a chart with historical data for Australia Coronavirus Vaccination Rate.

This dataset provides values for PERSONAL COMPUTERS PER 100 PEOPLE WB reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

List of male and female baby names in South Australia from 1944 to 2024. The annual data for baby names is published January/February each year.

Estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Australia, R2025A version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

Please note that these data represent 2025 Alpha release versions, constructed in September 2025

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

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Australia Phone Data

Australia phone data is information about phone numbers in Australia. This data is collected from trusted sources to ensure it's reliable. The sources can include websites, government records, and phone service providers. We verify each source, and you can check the URLs where we got the data. This makes sure the phone data is accurate and dependable. Also, the companies that offer this database provide 24/7 support. This means if you ever need help or have questions, you can contact them anytime and get quick answers.

Additionally, the phone data follows the opt-in rule. This means that people have shared their numbers willingly. It ensures people know their information is being used, so you won’t get into trouble for using phone numbers without permission. Since it’s important to respect people’s privacy, this makes the Australia phone number list safe and compliant with the law. With all these features, you can confidently use this data to reach out to the right people.

Australia Contact Number List

Australia contact number list from B2C Databases is 100% correct and valid. This data is always double-checked to make sure it is accurate. So, when you use this data, you can trust that the phone numbers work. And if you ever get the wrong number, you get a replacement guarantee. This means if a phone number is not working, they will give you a new one without any extra cost. This saves you time and ensures you can still connect with the right people.

The people who are on the list have agreed to share their phone numbers. So, you’re not breaking any rules when you use this data. By having permission from the customers, it makes your contact with them more welcome and effective. This is why this data is very reliable. You can use it with confidence, knowing you are following the correct procedures and keeping your outreach efforts smooth and successful.

"

These datasets represent a Human Industrial Footprint (HIF) index map and an Ecological Intactness Index (EII) map for Australia circa 2020-2024. The datasets are distributed in raster format (.tif) and have a spatial resolution of 100 m, mapped on an Australian Albers Equal Area projection (EPSG:3577).

The HIF was created by incorporating 16 nationally relevant pressure layers, also part of the dataset. The pressures used to compute the HIF were 1) intensive land uses, 2) buildings, 3) mining and quarrying, 4) human population density, 5) croplands, 6) pasturelands, 7) forestry plantations, 8) reservoirs and large dams, 9) farm dams, 10) roads, 11) railways, 12) energy transmission lines, 13) oil pipelines, 14) gas pipelines, 15) hiking trails, and 16) navigable waterways. Each pressure layer was assigned a relative score between 0 and 10 to make them comparable. The scored (scaled) pressure layers were then summed to obtain the final HIF map.

The HIF was used to derive the Ecological Intactness Index (EII). The EII is calculated using the HIF, with the intactness index value for each cell parameterised to: a) be proportional to habitat area when there is no habitat fragmentation; b) decline mono-tonically as fragmentation increases, and be sensitive to both the number of nearby patches and the separation between patches, and (c) to be proportional to habitat quality for a given total area of habitat and degree of fragmentation.

In the pressure layer folder, native and modified pasturelands are merged in the "pastures" pressure layer and paved and unpaved roads are in the "roads" layer.

The code to create these maps is also available through this repository. The code is an end‑to‑end GRASS GIS pipeline to rebuild the Human Industrial Footprint Index for continental Australia on a 100 m grid in Albers Australia Equal Area (EPSG:3577). It generates 16 pressure layers, applies hierarchical priority (Urban > Mining > Crops >Pasture), scales each 0–10, and exports individual layers plus the summed index as Cloud‑Optimised GeoTIFFs (COGs).

Acknowledgements

This research was funded by The Wilderness Society.

Contact

Further queries regarding these datasets can be directed to Ruben Venegas (r.venegas@uq.edu.au) and James Watson (james.watson@uq.edu.au).

This dataset presents the social and economic indicators for the indigenous population of Australia based on the 2016 Census and aggregated following the 2016 edition of the Australian Statistical Geography Standard (ASGS). The data has been provided by The National Centre for Social and Economic Modelling (NATSEM) and includes the following indicators: age, sex, employment, education level, occupation, school attendance, language, household relationships, family types, household tenure type, household income, motor vehicles and household family composition. All indicators were extracted from the ABS Tablebuilder system using the usual residence profile. For usual residence data, the ABS moves people back to where they live, rather than using the location the data were collected (place of enumeration). Usual residence data is preferred for individual level data because it removes the effect of respondents travelling or holidaying. All rates were calculated as a proportion of all Indigenous people in the area, excluding any Not Stated or Overseas Visitors. Therefore, summing the rates across all categories for an indicator will give a total of 100%. For more information please view the NATSEM Technical Report. Please note: AURIN has spatially enabled the original data provided directly from NATSEM. Where data values are NULL, the data is either unpublished or not applicable mathematically.

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Refugia can facilitate the persistence of species through long-term environmental change, but it is not clear if Pleistocene refugia will remain functional under anthropogenic climate change. Dieback of species within refugia therefore raises concerns about their long-term persistence. Using repeat field surveys, we investigate dieback patterns of an isolated population of Eucalyptus macrorhyncha during two droughts and discuss prospects for its continued persistence in a Pleistocene refugium. We first confirm that the Clare Valley in South Australia has constituted a long-term refugium for the species, with the population being genetically highly distinct from other conspecific populations. However, the population lost >40% of individuals and biomass through the two droughts, with mortality being just below 20% after the Millennium Drought (2000–2009) and almost 25% after the Big Dry (2017-2019). The best predictors of mortality differed after each drought. While the north-facing aspect of a sampling location was a significant positive predictor after both droughts, biomass density and slope were significant negative predictors only after the Millennium Drought, and distance to the north-west corner of the park, which intercepts hot, dry winds, was significant after the Big Dry only. This suggests that more marginal sites with low biomass and located on exposed, flat plateaus were more vulnerable initially, but that heat-stress was an important driver of dieback during the Big Dry. Therefore, the causative drivers of dieback may change during population decline. Regeneration occurred predominantly on southern and eastern aspects, which would receive the least solar radiation. Occurrence in a refugium did not protect this population from dieback. However, gullies with lower solar radiation are continuing to support relatively healthy, regenerating stands of red stringybark, providing hope for persistence in small pockets. Monitoring and managing these pockets during future droughts will be essential to ensure the persistence of this isolated and genetically unique population. Methods The data contains three datasets derived from analysing data from multiple surveys of a red stringybark population (Eucalyptus macrorhyncha) in Spring Gully Conservation Park (SGCP), Clare Valley, Australia. These are the Tree Health Index (THI), Biomass and Drivers datasets, which are used in the analyses of the associated paper. Below I explain how each dataset was obtained. The South Australian Department of Environment and Water (DEW) initiated a tree health monitoring program in 2009, during which four North-South oriented transects were established in SGCP. Each transect (between 1.2 and 1.8 km long) had sampling sites every 50 m. At each sampling site, the four closest canopy trees within a 10 m radius were marked with a permanent aluminum tag, their location recorded with a handheld GPS (brand and model unknown), and various measurements relating to their health status taken (see below). In total, 471 trees were surveyed, 30 of which were South Australian blue gums (Eucalyptus leucoxylon F.Muell.) and the remainder were red stringbark. Transects were surveyed in January and February 2009, March 2010, November 2011, August 2012, November 2013, and September 2014. Parameters recorded included tree status (dead/ alive; trees with dead stems but with living basal sprouts were scored as alive), crown extent (percentage area of assessable crown with live leaves), and crown density (percentage of skylight blocked by the leafy crown). Percentage values were recorded as eight categories: 0 (0%), 1 (1–10%), 2 (11–20%), 3 (21–40%), 4 (41–60%), 5 (61–80%), 6 (81–90%), and 7 (91–100%). The assessable crown was defined as consisting of all living and dead branches of the crown. In addition, epicormic growth and extent of reproductive activity (presence of flowers and/or fruits) were classified into four categories: 0 (absent, not visible), 1 (scarce, present but not readily visible), 2 (common, clearly visible throughout the assessable crown), 3 (abundant, dominates the appearance of the assessable crown). We calculated a summative index consisting of canopy extent, canopy density, and epicormic growth to indicate tree health – hereafter referred to as the tree health index (THI). Because crown extent and density are considered the most important indicators of tree health, we retained them at their larger scale (0–7, compared to 0–3 for epicormic growth), giving a maximum value of 17 for the THI. Trees that appeared dead at some surveys but that later resprouted (i.e., epicormic growth or basal sprouts), were retrospectively awarded a THI score of 1 (instead of zero). To get an indication of the health status of the red stringybark population, the proportion of dead trees and the average THI of all 441 stringybark trees surveyed repeatedly since 2009 were determined and this data is available in the THI dataset. In September and December 2021, we revisited all trees that had been surveyed and tagged previously. Relocation of trees was achieved with high confidence because of the availability of GPS locations for each tree and because tags remained on, or had fallen directly beneath, at least two of the four trees at each site. Six sites consisting entirely of blue gum were not resurveyed (sites T1S02, T1S03, T1S04, T1S05, T1S30, T3S09). Methods for the resurvey focused on replicating the methods used in the earlier surveys (to facilitate comparisons) and on collecting additional information to provide area-based estimates of dieback. To achieve area-based estimates, we determined a center point for each site so that each of the four trees at a site was in a different quarter (delineated using the four cardinal directions). For sites with one or more blue gum trees among the four surveyed trees, blue gums were replaced by the nearest stringybark in the relevant quarter. In one instance, no nearest stringybark neighbour was present within 10 m and this site was excluded from analyses including biomass density. Additional trees were added to four sites that had less than four surveyed trees. This resulted in a total of 112 sites with 448 trees of red stringybark. This allowed estimating the tree density per hectare at each site by measuring, averaging, and squaring the distance of each tree to the center point. The inverse of this average distance was then multiplied by the value of the desired area (in this case 1 ha) to obtain an estimate of tree density, following the point-centered quarter method. To estimate biomass, we recorded diameter at breast height (DBH) and tree height for each stem of a tree (trees regularly had multiple stems), living or dead. A tree with dead stems was considered alive if there was any epicormic or basal growth present. A stem was considered alive if epicormic growth was present above 1.3m in height. The height (Ht) was estimated to the nearest meter using a 1.5 m range pole that was held up vertically overhead to provide a reference of approximately 3.5 m height. The DBH was measured using a diameter tape 1.3 m above the ground. A wood density (WD) of 795 (± 19) kg.m-3 was assumed for all trees. We used these values to calculated the above-ground biomass (AGB) as: AGB = 0.0673 × (WD × DBH2 × Ht)0.976. AGB was determined for every stem and then aggregated per tree, meaning a single individual could be composed of both living and dead biomass. We multiplied the estimate of number of individuals per hectare by the mean AGB per tree to obtain area-based estimates of biomass, i.e., biomass density. These calculations were done for each site (to obtain estimates of biomass density per site) and for all 112 sites combined (to obtain a parkwide estimate) and this data is available in the biomass dataset. As an estimate of regeneration, the occurrence of seedlings (< 1 m tall, woody growth lacking) and saplings (< 1 m tall, woody growth present) within a 3 m radius of the center point was recorded. Seedling and sampling numbers for each site were combined to provide an indicator of recruitment. In addition, aspect (in degrees rounded to 10° intervals and determined with a compass) and slope (in degrees using a clinometer) were recorded for each site. We calculated ‘northness’ and ‘eastness’ as the cosine and sine of the aspect (in radians), respectively. Where trees within a site were located on different slopes in a valley, the aspect and slope were recorded for each slope and then averaged. Distance to the north-west corner of the park (the area most affected by hot, dry summer winds) was calculated as the planar distance between this location and the sampling locations using the “Near (Analysis)” geoprocessing tool in ArcGIS Pro. We calculated the proportion of dead trees per site in 2011 (Mortality 2011) and 2021 (Mortality 2021) and regeneration as indicators of dieback and persistence (response variables). These variables for the 112 sites of the 2021 survey, but including only trees that were surveyed in 2011 as well (a total of 441 trees) are presented in the Drivers dataset.

AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa. To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, https://github.com/traitecoevo/austraits.build/. Further information on the project is available at the project website austraits.org and in the associated publication (see below). CONTRIBUTORS The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University. We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria. AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS). ACCESSING AND USE OF DATA The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community. A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors: Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 In addition, we encourage users you to cite the original data sources, wherever possible. Note that under the license data may be redistributed, provided the attribution is maintained. The downloads below provide the data in two formats: austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python. austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below). For R users, access and manipulation of data is assisted with the austraits R package. The package can both download data and provides examples and functions for running queries. STRUCTURE OF AUSTRAITS The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at https://traitecoevo.github.io/traits.build-book/. CONTRIBUTING We envision AusTraits as an on-going collaborative community resource that: Increases our collective understanding the Australian flora; Facilitates accumulation and sharing of trait data; Builds a sense of community among contributors and users; and Aspires to fully transparent and reproducible research of the highest standard. As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at https://github.com/traitecoevo/austraits.build/. Here are some of the ways you can contribute: Reporting Errors: If you notice a possible error in AusTraits, please post an issue on GitHub. Refining documentation: We welcome additions and edits that make using the existing data or adding new data easier for the community. Contributing new data: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at https://github.com/traitecoevo/austraits.build/. | External Organisations University of New South Wales; Western Sydney University; Royal Botanic Garden Sydney; Macquarie University; Commonwealth Scientific & Industrial Research Organisation; Department of Primary Industries (New South Wales); Centre for Australian National Biodiversity Research; Swinb…

Queensland Top 100 Baby Names

This dataset presents the footprint of the percentage of deaths of infants and young children per 1,000 live births. The data spans every two years between 2010-2016 and is aggregated to Statistical Area Level 3 (SA3) geographic boundaries from the 2011 Australian Statistical Geography Standard (ASGS). The Child and Maternal Health Indicators have been calculated from the Australian Institute of Health and Welfare (AIHW) National Mortality Database and Register of Births and National Perinatal Data Collection. This measure has been calculated with the numerator as the number of deaths from birth to less than 5 years, and the denominator as the total number of live births. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Child and Maternal Health Data Tables. Please note:

Deaths are attributed to the area in which the infant or child usually resided, irrespective of where they died.

Births are attributed to the area of usual residence of the mother, not location of birth.

Deaths are reported by year of registration of death.

Data for 2010 have been adjusted for the additional deaths arising from outstanding registrations of deaths in Queensland in 2010.

Mortality rates for an area are suppressed for publication and marked as 'NP' if the total number of live births for the area is less than 100.

Estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

Please note that these data represent 2025 Alpha release versions, constructed in September 2025

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Australia. Power Plant emissions from all power plants in Australia were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, metro area, lat/lon, and plant id for each individual power plant. Only Power Plants that had a listed longitude and latitude in CARMA's database were mapped. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/18

Context

JJJ, a popular public radio station in Australia, runs an annual survey where the people vote on up to 10 of their favorite songs for the year. The votes are tallied and the 100 most popular songs are played on Australia day weekend.

Content

A simple dataset containing the top 100 songs for years 1993 through to 2017. The data was scraped from http://hottest100.org/ 🥁

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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.

This raster dataset 'NVIS4_1_AUST_MVS_PRE_ALB' provides summary information on Australia's estimated pre-1750 native vegetation classified into Major Vegetation Subgroups. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size.

A comparable Extant (present) vegetation raster dataset is available:

• NVIS4_1_AUST_MVS_EXT_ALB.

State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012.

This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area.

Eighty-five (85) Major Vegetation Subgroups identified were created in v4.1 to summarise the type and distribution of Australia's native vegetation. The classification contains an emphasis on the structural and floristic composition of the dominant stratum (as with Major Vegetation Groups), but with additional types identified according to typical shrub or ground layers occurring with a dominant tree or shrub stratum.

In a mapping sense, the subgroups reflect the dominant vegetation occurring in a map unit from a mix of several vegetation types. Less-dominant vegetation subgroups which are also present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest with a shrubby understorey, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants.

A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Subgroups. These are provided for cartographic purposes, but should not be used for analyses.

This dataset has been provided to the BA Programme for use within the programme only. The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Dataset History

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, each up to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVS. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Subgroup (MVS) by manual interpretation at ERIN and in consultation with data custodians. 12 new MVSs were created for version 4.1 to better represent open woodland formations, more understorey types and forests (in the NT) with no further data available. Also, a number of MVSs were redefined after creation of the new groups to give a clearer and precise description of of the Subgroup e.g. MVS 9 - 'Eucalyptus woodlands with a grassy understorey' became 'Eucalyptus woodlands with a tussock grass understorey' to distinguish it from MVS10 - 'Eucalyptus woodlands with a hummock grass understorey'.. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Warm Temperate Rainforest

• Eucalyptus woodlands with a hummock grass understorey

• Acacia (+/- low) open woodlands and sparse shrublands with a shrubby understorey

• Mulga (Acacia aneura) open woodlands and sparse shrublands +/- tussock grass

• Eucalyptus woodlands with a chenopod or samphire understorey

• Open mallee woodlands and sparse mallee shrublands with a hummock grass understorey

• Open mallee woodlands and sparse mallee shrublands with a tussock grass understorey

• Open mallee woodlands and sparse mallee shrublands with an open shrubby understorey

• Open mallee woodlands and sparse mallee shrublands with a dense shrubby understorey

• Callitris open woodlands

• Casuarina and Allocasuarina open woodlands with a tussock grass understorey

• Casuarina and Allocasuarina open woodlands with a hummock grass understorey

• Casuarina and Allocasuarina open woodlands with a chenopod shrub understorey

• Casuarina and Allocasuarina open woodlands with a shrubby understorey

• Melaleuca open woodlands

• Other Open Woodlands

• Other sparse shrublands and sparse heathlands

• Unclassified Forest

Data values defined as cleared or non-native by data custodians were attributed specific MVS values such as 42 - naturally bare, sand, rock, claypan, mudflat; 43 - salt lakes and lagoons; 44 - freshwater lakes and dams; 46 - seas & estuaries, 90, 91, 92 & 93 - Regrowth Subgroups; 98 - Cleared, non native, buildings; and 99 - Unknown. Note: some of these MVSs are only present in Extant vegetation.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also stored in the NVIS database, but with blank vegetation descriptions. In general, the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M and 1:5M) maps from Commonwealth and other sources. MVSs were then allocated to each description from the available descriptions in accompanying publications and other sources.

Each spatial dataset with joined lookup table (including MVS_NUMBER linked via NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

In the original extant data, parts of New South Wales, South Australia, Tasmania and the ACT have areas of vector "NoData", thus appearing as an inland sea. Where there were gaps in the spatial coverage of Australia, "artificial" estimated pre-1750 layers were created from datasets available to the ERIN Veg Team. These were managed differently based on available information and complexity of work involved. Pre-1750 vector data for other states were supplied for 4.1 or previously, and did not require modelling. The purpose of this artificial pre-1750 modelling was to ensure that the pre-1750 and extant (present) datasets are comparable in the respective MVG and MVS classifications.

Pre1750 Vector Modelling

Large areas in the original South Australia and the ACT extant vector data had 'NoData'. Pre1750 vector layers were created by filling/cutting in these areas with estimated pre-1750 data from other sources such as the Geoscience Australia (AUSLIG,1990) "Natural" vector data layer. This procedure assumes that extant native vegetation has not changed its type since European settlement. Thus, effectively, only the non-native component was modelled/estimated for pre-1750 extent.

All feature classes were then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances e.g. NSW and TAS, areas of 'NoData' had to be modelled in raster (see below).

Raster modelling

For large parts of NSW, the native component of NVIS extant data were cut into the Geoscience Australia (AUSLIG,1990) "Natural" raster data layer and in some smaller areas, existing pre1750 data layers (e.g. Tumut), using a complex series of raster operations. For Tasmania, the NVIS version 2.0 (i.e. the original NVIS with restructured attributes) pre-European layer was rasterised, and used to fill non-native areas of the extant NVIS vegetation

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from the Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions) datasets. You can find a link to the parent dataset in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

Clips and extracts of the IBRAv7 regions and subregions datasets which intersect the Hunter subregion

Interim Biogeographic Regionalisation for Australia (IBRA) version 7.0 represents a landscape based approach to classifying the land surface of Australia. 89 biogeographic regions and 419 sub regions have been delineated, each reflecting a unifying set of major environmental influences which shape the occurrence of flora and fauna and their interaction with the physical environment across Australia and its external territories (excluding Antarctica). IBRA Version 7.0 data consists of two datasets. IBRA bioregions, which is a larger scale regional classification of homogenous ecosystems, and sub regions, which are more localised. IBRA Version 7.0 is the result of both significant changes to certain IBRA 6.1 boundaries, plus refinement of other boundaries due to better data availability amongst some states and territories, and alterations by the states/territories along state borders. The updated boundaries were jointly defined by the Commonwealth, State and Territory nature and conservation agencies. In this respect refinements were carried out to all mainland jurisdictions with significant changes in Queensland and South Australia. In addition the dataset was also updated to more closely conform to the Geoscience Australia 1:100K State borders, and a standard coding/naming convention introduced (for both regions and sub-regions) resulting in differences to both names and codes used in earlier IBRA Versions.

Various sources were used to delineate islands - these included the GA100K Admin layer plus the Australian Maritime Boundaries dataset, a Coral Sea dataset (held in ERIN) and the GA Commonwealth Fisheries 2006 dataset.

Dataset History

Clips and extracts of the IBRAv7 regions and subregions datasets which intersect the Hunter subregion

States and Territories provided the base data for inclusion in IBRA Version 7 Auricht Projects then undertook spatial refinements of the boundaries to ensure that edgematching of boundaries were to the state borders and coastline. The Geoscience Australia 1:100,000 dataset was used to maintain this standard. Draft outputs were provided to State/Territory Agencies for cross-checking. Final checking of topology was undertaken by the Australian Government Department of Sustainability, Environment, Water, Population and Communities.

Dataset Citation

Bioregional Assessment Programme (2013) Hunter bioregion (IBRA Version 7). Bioregional Assessment Derived Dataset. Viewed 28 August 2018, http://data.bioregionalassessments.gov.au/dataset/e73c01b3-c5ef-4fea-8402-2b816d7533b5.

Dataset Ancestors

• Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)

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.

These data provide locational and attribute information for places nominated to and included in the National Heritage List as determined by the Australian Government managed by the Department of Sustainability, Environment, Water, Population and Communities, Heritage and Wildlife Division. National Heritage List polygons with attribute information describing the place name, class (indigenous, natural, historic), and status. Places subject to confidentiality agreements are included in these data but the location is generalised to the bounding 250k mapsheet.

The location data for place nominations that have been rejected, are ineligible, removed or destroyed are not included in the publicly downloadable spatial dataset. Places having current assessment and nomination processes involving boundary revisions being undertaken are not available to the public. Spatial data for listed places are available to the public.

DATA QUALITY REPORT - COMPLETENESS

The database is live and ongoing. There are current assessment and nomination process being undertaken.

DATA QUALITY REPORT - CONCEPTUAL CONSISTENCY

The conversion of the data from the original shapefiles follow existing protocols currently used by the Register of the National Estate. The attribution is assumed to be logically consistent as provided by the Heritage and Wildlife Division of the Australian Government Department of Sustainability, Environment, Water, Population and Communities.

DATA QUALITY REPORT - POSITIONAL ACCURACY

Most features have a positional accuracy of, at most, +/- 100 metres

DATA QUALITY REPORT - ATTRIBUTE ACCURACY

Attribute Information is verified by the Heritage Division.

Dataset History

The original spatial data for some places were captured and copied from the Register of the National Estate, which were digitised by the Australian Surveying and Land Information Group (AUSLIG) from stable-base overlays produced by the Australian Heritage Commission since 1986. Since 1999, data entry and attribution has been undertaken by the Australian Government Department of Sustainability, Environment, Water, Population and Communities, Heritage Division staff. Data are captured using topographic and cadastral data at map scales of up to 1:250,000, depending on the size and detail of the property. The majority of the source datasets are maintained and processed as ESRI shapefiles, in geographic projection using datum GDA94 The final dataset described by this metadata has been transformed to the Geocentric Datum of Australia (GDA94).

This dataset was exported from SDE by ERIN on 17/09/2013 for use in compiling preliminary bioregional assets lists for the Office of Water Science Bioregional Assessment Program. Field "ElemetID" was added and a unique identifier created for each spatial feature for use in the BA Programme.

Dataset Citation

Department of the Environment (2014) National Heritage List Spatial Database (NHL) (v2.1). Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/26daa8d7-a90e-47f3-982b-0df362414e65.