9 datasets found
  1. Data for: Modelling feral pig habitat suitability in Queensland to inform...

    • researchdata.edu.au
    datadownload
    Updated Feb 6, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Matthew Gentle; Cameron Wilson; Justine Murray; Matthew Rees; Jens Froese (2023). Data for: Modelling feral pig habitat suitability in Queensland to inform disease preparedness and response [Dataset]. https://researchdata.edu.au/data-for-modelling-preparedness-response/3374292
    Explore at:
    datadownloadAvailable download formats
    Dataset updated
    Feb 6, 2023
    Dataset provided by
    CSIROhttp://www.csiro.au/
    Authors
    Matthew Gentle; Cameron Wilson; Justine Murray; Matthew Rees; Jens Froese
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This data collection accompanies “Froese, J.G., Rees, M., Murray, J.V., Wilson, C. & Gentle, M. (2022). Modelling feral pig habitat suitability in Queensland to inform disease preparedness and response. Final report prepared for the Queensland Department of Agriculture and Fisheries. Brisbane: CSIRO.”

    It contains a series of spatial data products describing ‘feral pig habitat suitability’, ‘potential feral pig density (carrying capacity)’ and ‘feral/domestic pig interaction risk’ across Queensland for 12 temporal scenarios. Scenarios were selected to represent variability in environmental conditions across Queensland along two axes – intra-annual seasonal cycles (summer, autumn, winter, spring) and inter-annual climate cycles (wet, moderate and dry). They were represented by the periods September 2010 – August 2011 (wet climate cycle), December 2012 – November 2013 (moderate climate cycle) and March 2018 – February 2019 (dry climate cycle). The data are provided in TIFF raster file format (coordinate reference system = EPSG 3577: GDA94 / Australian Albers; spatial resolution = 100m). Lineage: The methods to derive these spatial data products built on previously published research on spatially explicit, resource-based feral pig habitat models (Froese et al. 2017; Murray et al. 2015). ‘Habitat suitability’ for feral pig breeding was modelled in a Bayesian network framework dependent on four fundamental resource requirements: food, water, heat refuge and anthropogenic disturbance. Several improvements to increase the robustness and reproducibility of published research methods were implemented and applied to the 12 modelled scenarios.

    Estimates of ‘potential feral pig density (carrying capacity)’ were derived for each of these 12 modelled scenarios. This was based on a hypothesized sigmoid relationship that took into account the modelled ‘habitat suitability index (HSI)’ in a particular season, cross-seasonal variability in the modelled HSI within a climate cycle (wet, moderate or dry) and pig densities in Queensland previously reported in the empirical literature.

    The potential feral pig density estimates were combined with a ‘weighted domestic pig herd density’ layer that was derived from data on the location and biosecurity status of all registered domestic pigs in Queensland (held by the Queensland Department of Agriculture and Fisheries) to calculate ‘feral/domestic pig interaction risk’ (i.e., the relative risk that feral pig populations at a given landscape location may interact with a nearby domestic pig herd) for each of the 12 modelled scenarios.

    Details on study objectives, methods and results are provided in the client report: “Froese, J.G., Rees, M., Murray, J.V., Wilson, C. & Gentle, M. (2022). Modelling feral pig habitat suitability in Queensland to inform disease preparedness and response. Final report prepared for the Queensland Department of Agriculture and Fisheries. Brisbane: CSIRO.” (contact Matthew Gentle: Matthew.Gentle@daf.qld.gov.au)

  2. Dataset containing incidental koala sightings in South East Queensland,...

    • springernature.figshare.com
    • search.datacite.org
    xlsx
    Updated May 31, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ravi Dissanayake; Joerg Henning; Rachel Allavena; Mark Stevenson (2023). Dataset containing incidental koala sightings in South East Queensland, Australia between 1997-2013. [Dataset]. http://doi.org/10.6084/m9.figshare.7603481.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Ravi Dissanayake; Joerg Henning; Rachel Allavena; Mark Stevenson
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    South East Queensland, Australia, Queensland
    Description

    This dataset consists of a single Excel spreadsheet, reporting the locations of incidental koala sightings in South East Queensland, Australia between 1997-2013.The dataset was extracted from the KoalaBASE database, which was developed by the University of Queensland's School of Veterinary in collaboration with the Department of Environment and Science and is available at www.koalabase.com.au. The data which were extracted from KoalaBASE included geo-referenced sightings of live koalas by members of the public between 1997-2013, with records relating to koalas which were injured, dead or euthanised excluded from the analysis. The aim of the related study was to describe spatial and temporal trends in koala presence, to estimate koala sighting density and to identify biases associated with sightings of koalas by members of the public.The dataset consists of crowdsourced koala sightings data for South East Queensland, and includes the Local Government Areas (LGAs) of the sightings, and the month and the year that the sightings occurred. A total of 14,076 sightings are included in the dataset. The latitude-longitude data which had been available in the original data extracted from KoalaBASE have been removed from the dataset due to the status of koalas as a threatened species.

  3. Population distribution Australia 2024 by age

    • statista.com
    Updated Nov 7, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2024). Population distribution Australia 2024 by age [Dataset]. https://www.statista.com/statistics/608088/australia-age-distribution/
    Explore at:
    Dataset updated
    Nov 7, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Australia
    Description

    In June 2022, it was estimated that around 7.3 percent of Australians were aged between 25 and 29, and the same applied to people aged between 30 and 34. All in all, about 55 percent of Australia’s population was aged 35 years or older as of June 2022. At the same time, the age distribution of the country also shows that the share of children under 14 years old was still higher than that of people over 65 years old. A breakdown of Australia’s population growth Australia is the sixth-largest country in the world, yet with a population of around 26 million inhabitants, it is only sparsely populated. Since the 1970s, the population growth of Australia has remained fairly constant. While there was a slight rise in the Australian death rate in 2022, the birth rate of the country decreased after a slight rise in the previous year. The fact that the birth rate is almost double the size of its death rate gives the country one of the highest natural population growth rates of any high-income country.
    National distribution of the population Australia’s population is expected to surpass 28 million people by 2028. The majority of its inhabitants live in the major cities. The most populated states are New South Wales, Victoria, and Queensland. Together, they account for over 75 percent of the population in Australia.

  4. n

    Data from: Factors influencing nature interactions vary between cities and...

    • data.niaid.nih.gov
    • search.dataone.org
    • +2more
    zip
    Updated Jan 6, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Rui Ying Rachel Oh; Kelly Fielding; Thi Phuong Le Nghiem; Chia-Chen Chang; Danielle Shanahan; Kevin Gaston; Román Carrasco; Richard Fuller (2021). Factors influencing nature interactions vary between cities and types of nature interactions [Dataset]. http://doi.org/10.5061/dryad.z612jm6b9
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 6, 2021
    Dataset provided by
    The University of Queensland
    National University of Singapore
    University of Exeter
    Zealandia
    Authors
    Rui Ying Rachel Oh; Kelly Fielding; Thi Phuong Le Nghiem; Chia-Chen Chang; Danielle Shanahan; Kevin Gaston; Román Carrasco; Richard Fuller
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description
    1. There is mounting concern that people living more urbanised, modern lifestyles have fewer and lower quality interactions with nature, and therefore have limited access to the associated health and wellbeing benefits. Yet, variation in the different types of nature interactions and the factors that influence these interactions across populations are poorly understood.
    2. We compared four types of nature interactions by administering surveys across two cities that differ markedly in urbanisation pattern and population density—Singapore and Brisbane—: (i) indirect (viewing nature through a window at work or at home); (ii) incidental (spending time in nature as part of work); (iii) intentional interactions in gardens; and (iv) intentional interactions in public urban greenspaces.
    3. Our results show that Singapore respondents spent about half as much time (25.8 hours per week) interacting with nature as Brisbane respondents (52.3 hours per week), and indirect interactions were the most prevalent across both cities.
    4. Nature orientation, age, income and gender significantly predicted the duration of nature interactions in both cities, while self-reported health, education and ethnicity additionally predicted duration of nature interactions only for Brisbane. Also, the relationship(s) between each factor and duration could differ in direction and effect size between types of nature interactions.
    5. As such, we conclude that there is much local variation in the dynamics of interactions between people and nature, and that focused studies are needed to develop effective interventions addressing declines in nature interactions in different locations. Methods Please refer to the readme.txt file, and the actual paper for more details.
  5. t

    Australia Used Automobiles Market Overview and Size

    • tracedataresearch.com
    Updated Aug 21, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    TraceData Research (2025). Australia Used Automobiles Market Overview and Size [Dataset]. https://www.tracedataresearch.com/industry-report/australia-used-automobiles-market
    Explore at:
    Dataset updated
    Aug 21, 2025
    Dataset authored and provided by
    TraceData Research
    Area covered
    Australia
    Description

    Several urban centres—particularly Sydney (NSW), Melbourne (VIC), and Brisbane (QLD)—dominate the used car market due to concentrated population density, higher household purchasing power, and well-developed dealership and logistics infrastructure. Moreover, strong digital adoption in these cities enhances online inventory visibility and faster transaction cycles, reinforcing their leadership in the used car ecosystem. The Australia used automobiles market is valued at USD 28,323.8 million in 2024, with a CAGR of approximately 6.4 % during 2018–2023, as implied by estimates projecting growth from earlier historical base and the AUD-to-USD conversions in authoritative reports. This market expansion is driven by rising new-vehicle prices, supply constraints in the new vehicle segment, and the growing affordability of retaining value models through certified pre‑owned programs and online platforms enhancing transaction transparency. Australia Used Automobiles Market Overview and Size

  6. Association between KLK15 SNPs and prostate cancer risk in the QLD and PLCO...

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Jyotsna Batra; Felicity Lose; Tracy O'Mara; Louise Marquart; Carson Stephens; Kimberly Alexander; Srilakshmi Srinivasan; Rosalind A. Eeles; Douglas F. Easton; Ali Amin Al Olama; Zsofia Kote-Jarai; Michelle Guy; Kenneth Muir; Artitaya Lophatananon; Aneela A. Rahman; David E. Neal; Freddie C. Hamdy; Jenny L. Donovan; Suzanne Chambers; Robert A. Gardiner; Joanne Aitken; John Yaxley; Mary-Anne Kedda; Judith A. Clements; Amanda B. Spurdle (2023). Association between KLK15 SNPs and prostate cancer risk in the QLD and PLCO study groups. [Dataset]. http://doi.org/10.1371/journal.pone.0026527.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Jyotsna Batra; Felicity Lose; Tracy O'Mara; Louise Marquart; Carson Stephens; Kimberly Alexander; Srilakshmi Srinivasan; Rosalind A. Eeles; Douglas F. Easton; Ali Amin Al Olama; Zsofia Kote-Jarai; Michelle Guy; Kenneth Muir; Artitaya Lophatananon; Aneela A. Rahman; David E. Neal; Freddie C. Hamdy; Jenny L. Donovan; Suzanne Chambers; Robert A. Gardiner; Joanne Aitken; John Yaxley; Mary-Anne Kedda; Judith A. Clements; Amanda B. Spurdle
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Queensland
    Description

    aSNP identifier based on NCBI dbSNP; SNPs are included in the region of the KLK15 gene including 2 kb of transcription start sites.bThe result of 2-d.f. test based on logistic regression in the Queensland study adjusted for age as continuous variable.cImputed from 1000 Genomes project data and PLCO genotyped data, where actual genotype data not available, [27]; allelic OR and p values are presented.dThe result of 2-d.f. test based on logistic regression in the PLCO study adjusted for age in five-year intervals, study center, and three eigenvectors to control population stratification in an incident density sampling strategy.

  7. A

    Australia Continuous Glucose Monitoring Devices Market Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 30, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Market Report Analytics (2025). Australia Continuous Glucose Monitoring Devices Market Report [Dataset]. https://www.marketreportanalytics.com/reports/australia-continuous-glucose-monitoring-devices-market-94114
    Explore at:
    pdf, doc, pptAvailable download formats
    Dataset updated
    Apr 30, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Australia
    Variables measured
    Market Size
    Description

    The Australian Continuous Glucose Monitoring (CGM) Devices market is experiencing robust growth, projected to reach a market size of $141.96 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 11.80% from 2019 to 2033. This expansion is fueled by several key factors. Rising prevalence of diabetes, particularly Type 1 and Type 2, within Australia's aging population is a primary driver. Increased patient awareness of the benefits of CGM devices, such as improved glycemic control and reduced risk of hypoglycemia, is also significantly contributing to market growth. Furthermore, technological advancements resulting in smaller, more user-friendly devices with enhanced accuracy and connectivity features are making CGMs more appealing and accessible to patients. Government initiatives promoting better diabetes management and reimbursement policies for CGM devices further bolster market expansion. The market is segmented by component (sensors, durables like receivers and transmitters) and geographically by provinces, with New South Wales, Victoria, and Queensland representing significant market shares due to higher population densities and healthcare infrastructure. Key players like Abbott Diabetes Care Inc, Dexcom Inc, Medtronic PLC, and Eversense are driving innovation and competition within this rapidly evolving market. The forecast period (2025-2033) promises continued expansion, driven by ongoing technological innovations, increasing acceptance of CGM technology among healthcare providers and patients, and expanding insurance coverage. However, potential restraints include the relatively high cost of CGM devices, which may limit accessibility for some patients. Furthermore, the market's growth will be influenced by the success of new product launches, regulatory approvals, and the ongoing development of integrated diabetes management solutions that incorporate CGM data with other relevant health information. Future market trajectory will depend heavily on the pace of technological innovation, improvements in device affordability, and the continued proactive engagement of key stakeholders in promoting the adoption of CGM technology in Australia's diabetes management landscape. Recent developments include: July 2023: Dexcom Inc. and its Australian subsidiary, Australasian Medical and Scientific Ltd (AMSL Diabetes), have announced that AMSL Diabetes has been approved by the Department of Veterans Affairs (DVA) to provide fully funded diabetes products, including Dexcom Continuous Glucose Monitoring and Tandem t: slim X2 Insulin Pump systems, to eligible DVA clients. This approval falls under the contract for mobility and functional support (MFS) and home modification products and services., July 2022: NDSS gave access to FreeStyle Libre 2, and Free Style Libre 2 has been subsidized for all Australians with type 1 diabetes.. Key drivers for this market are: Rapidly Increasing Incidence and Prevalence of Diabetes, Technological Advancements in the Market. Potential restraints include: Rapidly Increasing Incidence and Prevalence of Diabetes, Technological Advancements in the Market. Notable trends are: The Durables Segment is Expected to Witness the Highest Growth Rate Over the Forecast Period.

  8. Number of GPs in Australia 2019, by state and territory

    • statista.com
    Updated Jul 18, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Number of GPs in Australia 2019, by state and territory [Dataset]. https://www.statista.com/statistics/1092241/australia-number-of-gps-by-state-and-territory/
    Explore at:
    Dataset updated
    Jul 18, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Australia
    Description

    In 2019, Queensland was the Australian state with the highest density of general practitioners with ***** GPs per 100,000 of the population. The Australian Capital Territory had the fewest number of GPs in relation to its population.

  9. A

    Australia Commercial Real Estate Market Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 30, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Market Report Analytics (2025). Australia Commercial Real Estate Market Report [Dataset]. https://www.marketreportanalytics.com/reports/australia-commercial-real-estate-market-92055
    Explore at:
    doc, ppt, pdfAvailable download formats
    Dataset updated
    Apr 30, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Australia
    Variables measured
    Market Size
    Description

    The Australian commercial real estate market, valued at $34.07 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 8.46% from 2025 to 2033. This expansion is fueled by several key drivers. Strong population growth in major cities like Sydney, Melbourne, and Brisbane is increasing demand for office, retail, and industrial spaces. Furthermore, the burgeoning e-commerce sector is driving significant growth in the logistics and warehousing segments. Government infrastructure investments and a generally positive economic outlook also contribute to this positive market trajectory. While rising interest rates and potential economic slowdown pose some constraints, the long-term fundamentals of the Australian economy and the ongoing need for modern commercial spaces are expected to mitigate these risks. The market is segmented by property type (office, retail, industrial & logistics, hospitality, and others) and by city (Sydney, Melbourne, Brisbane, Adelaide, Canberra, Perth), reflecting diverse investment opportunities and regional variations in growth rates. Sydney and Melbourne are expected to remain dominant, given their established business ecosystems and high population densities. However, other cities such as Brisbane are witnessing significant growth driven by infrastructure development and population influx. The key players in this dynamic market, including Lendlease Corporation, Scentre Group Limited, and Mirvac, are well-positioned to capitalize on these growth opportunities. The segmentation of the market reveals significant potential within specific sectors. The industrial and logistics sector, driven by the e-commerce boom and supply chain optimization efforts, is anticipated to experience particularly strong growth. Similarly, the office sector, while facing some challenges from remote work trends, remains resilient due to the ongoing need for collaborative workspaces and central business district locations. The retail sector will continue to adapt to evolving consumer preferences, with a focus on experience-driven retail and omnichannel strategies. Careful consideration of factors like interest rate fluctuations, construction costs, and regulatory changes will be crucial for investors navigating the complexities of this dynamic market. The forecast period of 2025-2033 offers a promising outlook for sustained growth within this sector. Recent developments include: • October 2023: Costco is planning a major expansion in Australia, with several new warehouses under construction and several prime locations being considered for future locations. Costco currently operates 15 warehouses in Australia, with plans to expand to 20 within the next five years, based on current stores and potential locations., • July 2023: A 45-storey BTR tower will be developed by Lendlease and Japanese developer Daiwa House, completing the final phase of Lendlease's Melbourne Quarter project and its second Build-to-Rent (BTR) project in Australia. The USD 650 million deal, similar to Lend lease's first 443-unit BTR project under construction in the 5.5 hectares of mixed-use space at Brisbane Showground, is a stand-alone investment and is separate from the company's ongoing efforts to build a wider BTR partnership, which will include several assets.. Key drivers for this market are: Rapid Urbanization, Government Initiatives Actively promoting the Construction Activities. Potential restraints include: Rapid Urbanization, Government Initiatives Actively promoting the Construction Activities. Notable trends are: Retail real estate is expected to drive the market.

  10. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Matthew Gentle; Cameron Wilson; Justine Murray; Matthew Rees; Jens Froese (2023). Data for: Modelling feral pig habitat suitability in Queensland to inform disease preparedness and response [Dataset]. https://researchdata.edu.au/data-for-modelling-preparedness-response/3374292
Organization logo

Data for: Modelling feral pig habitat suitability in Queensland to inform disease preparedness and response

Explore at:
datadownloadAvailable download formats
Dataset updated
Feb 6, 2023
Dataset provided by
CSIROhttp://www.csiro.au/
Authors
Matthew Gentle; Cameron Wilson; Justine Murray; Matthew Rees; Jens Froese
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Description

This data collection accompanies “Froese, J.G., Rees, M., Murray, J.V., Wilson, C. & Gentle, M. (2022). Modelling feral pig habitat suitability in Queensland to inform disease preparedness and response. Final report prepared for the Queensland Department of Agriculture and Fisheries. Brisbane: CSIRO.”

It contains a series of spatial data products describing ‘feral pig habitat suitability’, ‘potential feral pig density (carrying capacity)’ and ‘feral/domestic pig interaction risk’ across Queensland for 12 temporal scenarios. Scenarios were selected to represent variability in environmental conditions across Queensland along two axes – intra-annual seasonal cycles (summer, autumn, winter, spring) and inter-annual climate cycles (wet, moderate and dry). They were represented by the periods September 2010 – August 2011 (wet climate cycle), December 2012 – November 2013 (moderate climate cycle) and March 2018 – February 2019 (dry climate cycle). The data are provided in TIFF raster file format (coordinate reference system = EPSG 3577: GDA94 / Australian Albers; spatial resolution = 100m). Lineage: The methods to derive these spatial data products built on previously published research on spatially explicit, resource-based feral pig habitat models (Froese et al. 2017; Murray et al. 2015). ‘Habitat suitability’ for feral pig breeding was modelled in a Bayesian network framework dependent on four fundamental resource requirements: food, water, heat refuge and anthropogenic disturbance. Several improvements to increase the robustness and reproducibility of published research methods were implemented and applied to the 12 modelled scenarios.

Estimates of ‘potential feral pig density (carrying capacity)’ were derived for each of these 12 modelled scenarios. This was based on a hypothesized sigmoid relationship that took into account the modelled ‘habitat suitability index (HSI)’ in a particular season, cross-seasonal variability in the modelled HSI within a climate cycle (wet, moderate or dry) and pig densities in Queensland previously reported in the empirical literature.

The potential feral pig density estimates were combined with a ‘weighted domestic pig herd density’ layer that was derived from data on the location and biosecurity status of all registered domestic pigs in Queensland (held by the Queensland Department of Agriculture and Fisheries) to calculate ‘feral/domestic pig interaction risk’ (i.e., the relative risk that feral pig populations at a given landscape location may interact with a nearby domestic pig herd) for each of the 12 modelled scenarios.

Details on study objectives, methods and results are provided in the client report: “Froese, J.G., Rees, M., Murray, J.V., Wilson, C. & Gentle, M. (2022). Modelling feral pig habitat suitability in Queensland to inform disease preparedness and response. Final report prepared for the Queensland Department of Agriculture and Fisheries. Brisbane: CSIRO.” (contact Matthew Gentle: Matthew.Gentle@daf.qld.gov.au)

Search
Clear search
Close search
Google apps
Main menu