The Melbourne Adaptive Learning Dataset (MALD) is a comprehensive real-world dataset designed to support research in adaptive learning, predictive modeling, and educational analytics. Collected over three academic years from a collaborative educational initiative in Melbourne, Australia, this dataset reflects diverse student demographics, including varying levels of English proficiency, learning outcomes, and socioeconomic backgrounds. It offers a unique opportunity to explore advanced machine learning and deep learning techniques for improving adaptive learning systems, personalized education, and predictive modeling tasks.

The dataset contains 42,600 entries representing anonymized student records, assessment outcomes, and interaction metrics. Each record includes information on learner profiles, adaptive test improvements, skill development, and personalized learning recommendations, making it suitable for classification, regression, and clustering tasks.

Features Overview:

S.No Feature Name Description 1 Student_ID Unique identifier for each student. 2 Age Age of the student at the time of data collection. 3 Gender Gender of the student (Male, Female, or Other). 4 Socioeconomic_Status Student’s socioeconomic category (High, Medium, Low). 5 Native_Language First language spoken by the student. 6 English_Proficiency_Level Proficiency level in English (Beginner, Intermediate, Advanced). 7 Study_Hours Total study hours logged by the student weekly. 8 Device_Type Primary device used for learning (Laptop, Mobile, Tablet). 9 Quiz_Scores Average score achieved by the student in quizzes (0–100). 10 Exercise_Completion_Rate Percentage of assigned exercises completed by the student. 11 Adaptive_Test_Improvement Classification of adaptive test results (Improved, No Change, Declined). 12 Skill_Development_Success Classification of skill development outcomes (Improved, Partially Improved, No Improvement, Declined). 13 Learning_Path Recommended learning path (Advance, Continue Current Level, Remedial Support, Custom Path). 14 Assessment_Performance Classification of overall performance (Poor, Average, Good, Excellent). 15 Final_Exam_Score Final exam score achieved by the student (0–100). 16 Feedback_Rating Rating given by students on the quality of teaching (scale of 1–5). This dataset is meticulously curated, anonymized, and balanced to ensure reliability while adhering to ethical guidelines and privacy regulations. It is ideal for academic research, algorithm development, and benchmarking in the domain of adaptive learning and educational technology.

This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Student Performance Prediction in Higher Education Dataset Description This dataset contains data representing student performance in higher education institutions across Australia. The dataset is designed to aid in the prediction of student performance based on a variety of academic, personal, and socio-economic factors. some data including university names have been removed for privacy concerns.

Dataset Summary Total Records: 100,256 Total Features: 51 Target Variable: Performance Features Student ID: Unique identifier for each student. University ID: Unique identifier for each university. University Name: Name of the university. Age: Age of the student. Gender: Gender of the student. Major: Student's major or field of study. Year of Study: Current year/level of study (e.g., freshman, sophomore). GPA: Grade Point Average. High School GPA: GPA from high school. Entrance Exam Score: Score on university entrance exams. Attendance Rate: Percentage of classes attended. Participation in Extracurricular Activities: Whether the student participates in extracurricular activities (0 = No, 1 = Yes). Part-time Job: Whether the student has a part-time job (0 = No, 1 = Yes). Hours of Study per Week: Average number of hours spent studying per week. Family Income: Family's annual income. Parental Education Level: Highest education level attained by parents. Accommodation Type: Type of accommodation (Dormitory, Off-campus, With family). Distance from Home to University: Distance between student's home and the university. Internet Access at Home: Whether the student has internet access at home (0 = No, 1 = Yes). Library Usage: Frequency of library usage (number of visits per week). Access to Academic Resources: Availability of academic resources (0 = No, 1 = Yes). Health Condition: Student's health condition (Excellent, Good, Fair, Poor). Mental Health Status: Self-reported mental health status (Excellent, Good, Fair, Poor). Scholarship: Whether the student receives a scholarship (0 = No, 1 = Yes). Financial Aid: Whether the student receives financial aid (0 = No, 1 = Yes). Tutor Support: Whether the student has access to a tutor (0 = No, 1 = Yes). Counseling Services: Whether the student uses counseling services (0 = No, 1 = Yes). Transportation Mode: Mode of transportation to university (Walking, Biking, Public Transport, Car). Hours of Sleep per Night: Average number of hours slept per night. Diet Quality: Self-reported diet quality (Excellent, Good, Fair, Poor). Exercise Frequency: Frequency of exercise per week. Social Integration: Level of social integration within the university (Excellent, Good, Fair, Poor). Peer Support: Availability of peer support (0 = No, 1 = Yes). Language Proficiency: Proficiency in the language of instruction (Excellent, Good, Fair, Poor). Use of Online Learning Platforms: Frequency of using online learning platforms. Class Participation: Level of participation in class discussions (Excellent, Good, Fair, Poor). Project/Assignment Scores: Average scores on projects and assignments. Midterm Exam Scores: Scores on midterm exams. Final Exam Scores: Scores on final exams. Attendance at Office Hours: Frequency of attending professors' office hours. Group Work Participation: Participation in group work (0 = No, 1 = Yes). Research Involvement: Involvement in research projects (0 = No, 1 = Yes). Internship Experience: Whether the student has internship experience (0 = No, 1 = Yes). Peer Reviews: Scores or feedback from peer reviews. Academic Advising: Frequency of meetings with academic advisors. Learning Style: Preferred learning style (Visual, Auditory, Kinesthetic, Reading/Writing). Study Environment: Quality of study environment (Excellent, Good, Fair, Poor). Core Course Average: Average scores in core courses. Extracurricular Participation: Level of participation in extracurricular activities (0 = No, 1 = Yes). Peer Evaluations: Peer feedback on collaborative work. Performance: Overall performance label (Excellent, Good, Satisfactory, Needs Improvement, Poor). Target Variable - Performance The target variable Performance is a categorical feature representing the overall performance of the student. The possible values are:

Excellent: Top-performing students. Good: Above-average performance. Satisfactory: Average performance. Needs Improvement: Below-average performance. Poor: Poor performance.

Usage

This dataset can be used for:

Predictive modeling to identify factors influencing student performance. Analyzing trends and patterns in student performance across different universities. Developing interventions to support students at risk of poor performance. Acknowledgements This dataset provides a rich resource for researchers and educators interested in student performance prediction and the factors that influence academic success in higher education institutions in Australia.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

Abstract This dataset is a subset of Surface Geology of Australia 2012,1:2.5M Scale symbolised by age classification. Attributes include summary geological information for each unit polygon, and metadata about the capture and recommended portrayal of the polygons. The 1:2.5M scale geology of Australia data documents the distribution and age of major stratigraphic, intrusive and medium to high-grade metamorphic rock units of onshore Australia. This edition contains the same geological content as previous editions (1998 to 2010), but is structured according to Geoscience Australia's 2012 data standards. The dataset was compiled to use at scales between 1:2,500,000 and 1:5,000,000 inclusive. The units distinguished/mapped mainly represent stratigraphic supergroups, regional intrusive associations and regional metamorphic complexes. Groupings of Precambrian units in the time-space diagram are generally separated by major time breaks; Phanerozoic units are grouped according to stratigraphic age i.e. System/Period. The time-space diagram has the added benefit that it provides a summary of units currently included on the themes. The method used to distinguish sedimentary and many volcanic units varies for each geological eon as follows:

Cenozoic units are morphological units which emphasise the relationship of the sedimentary fill to the landscape; Mesozoic units are regionally extensive to continent-wide time-rock units which emphasise the System of Period(s); Paleozoic units are stratotectonic units that emphasise either the dominant System or Period(s) or the range of Periods; Proterozoic units are commonly regional stratotectonic units - separated by major time breaks and split into the Paleoproterozoic, Mesoproterozoic and Neoproterozoic Eras - which are generally unique to each cratonic region; and Archean units are regional lithological units grouped into broad time divisions.

Metamorphic units are lithological units which emphasise the metamorphic facies and timing of the last major metamorphic event. Igneous units are regional units which emphasise the dominant lithology and are grouped into broad time divisions. Currency Date modified: December 2014 Modification frequency: As needed Data extent Spatial extent North: -8.8819° South: -47.1937° East: 163.1921° West: 109.2335° Source information Geoscience Australia catalog entry: Surface Geology of Australia 1:2.5 million scale dataset 2012 edition Lineage statement The geological content of the 2012 edition of the 1:2.5M surface geology of Australia is the same as the previous 2010 edition (ANZLIC dataset ID = ANZCW0703013817), restructured to comply with 2012 Geoscience Australia and international data standards. The original data was compiled from digital data, mainly at 1:2 500 000 scale, supplied by AGSO, GSWA, NTGS, PIRSA, GSQ, GSTAS, GSNSW and GSVIC and from data obtained from many other groups. In order to synthesise data from a variety of sources into a coherent product, the degree and nature of modification of the source data varied from case to case. Cenozoic and Mesozoic units were derived from sources, including the Cenozoic Paleogeographic Atlas of Australia (Landford et al., 1995), the Geology of Australia 1986 and a compilation of Cenozoic basins in the Alice Springs region by B.R. Senior et al. (AGSO Record 1994/66). The Phanerozoic units of southeastern Australia are substantially a modification of the 1:2 500 000 scale map entitled "Stratotectonic and Structural Elements of the Tasman Fold Belt System". The geology of Tasmania is a generalisation of data assembled as part of the TASGO project (a GSTAS and AGSO/AGCRC venture completed in 1997). The geology of South Australia is a highly generalised modification of the 1993 1:2 000 000 scale Geological Map of South Australia. For the Precambrian compilation, much of the geology of Western Australia has been derived from the Geological Map of Western Australia, 1988 with some modifications. The geology of the Kimberley, Halls Creek, Tanami and Arunta regions has been updated in line with recent mapping and some input from magnetic interpretation to emphasise relationships with the Tanami region. The geology of the Amadeus region has been generalised from the 1:1 000 000 scale "Structural Map of the Amadeus Basin" (Compiler A.J. Stewart). The geology of the Musgrave region has been re-compiled and simplified. The geology of North Queensland has been generalised by D. Palfreyman and D. Pillinger from the "North Qld Geology, 1997" 1:1 000 000 scale map (compilers J.H.C. Bain & D. Haipola). Data dictionary

Attribute name Description

mapSymbol Letter symbol or code representing the geologic unit

plotSymbol Letter symbol or code representing the geologic unit for display on a map. May be a simplified version of mapSymbol

stratno Unique unit number from the Australian Stratigraphic Units Database

name Name of the geologic unit

description Text description of the geologic unit

geologicUnitType The type of geologic unit. (eg, lithostratigraphic, chronostratigraphic, etc) Term from a controlled vocabulary.

geologicUnitType_uri URI link to a controlled vocabulary term for geologic unit type

geologicHistory Text summary description of the geologic history of the geologic unit

representativeAge_uri URI link to a controlled vocabulary term for the representative summary age for the geologic unit

representativeYoungerAge_uri URI link to a controlled vocabulary term for the older named age for the geologic unit

representativeOlderAge_uri URI link to a controlled vocabulary term for the younger named age for the geologic unit

lithology A summary description of the lithological composition of the geologic unit

representativeLithology_uri URI link to a controlled vocabulary term for the primary lithological composition of the geologic unit

bodyMorphology Description of the type of occurrence of the geologic unit (eg, pluton, dyke, sill, markerbed, vein, etc)

observationMethod Description of the observation method or compilation method used compile the mapped geologic unit

identityConfidence Description of the confidence in the interpretation of the geologic unit

source Text describing feature-specific details and citations to source materials, and if available providing URLs to reference material and publications describing the geologic feature. This could be a short text synopsis of key information that would also be in the metadata record referenced by metadata_uri.

metadata_uri URI referring to a metadata record describing the provenance of data.

mappingFrame Description of the frame of reference of the mapped data (eg, earth surface, top of bedrock, top of Neoproterozoic basement)

resolutionScale The denominator of the scale at which the mapped data is designed to be represented

captureScale The denominator of the scale of data from which the mapped feature has been compiled

captureDate The date of original data capture for this mapped feature Metadata Statement - Surface Geology of Australia, 1:2.5 million scale, 2012 edition 6

modifiedDate The date of modification of this mapped feature, if applicable

plotRank A numeric indicator of the intention for how this mapped feature is to be plotted on a map. (1 = normal plotting feature; 2 = non-plotting feature)

mappedFeatureID Unique identifier (URI) for the mapped line segment

geologicUnitID Unique identifier (URI) for the geologic unit

Contact Geoscience Australia, clientservices@ga.gov.au

Retirement Age Women in Australia remained unchanged at 67 Years in 2025 from 67 Years in 2024. This dataset provides - Australia Retirement Age Women - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset presents data on the population of a region by age group for the Statistical Area Level 4 (SA4) regions as of December 2021. The boundaries for this dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS).

The Australian Department of Education, Skills and Employment publishes a range of labour market data on its Labour Market Information Portal. The data provided includes unemployment rate, employment rate, participation rate, youth unemployment rate, unemployment duration, population by age group and employment by industry and occupation.

AURIN has spatially enabled the original data. Data Source: ABS Labour Force Survey, 12 month average, December 2021. The ABS advises that analysis of regional labour force estimates should typically be based on annual averages, which are important for understanding the state of the labour market and providing medium and long-term signals. The application of annual averages, however, is unlikely to accurately or quickly detect turning points in the regional data during periods of significant change (such as during the onset of the COVID-19 pandemic). Original data at the ABS Statistical Area 4 (SA4) level can be found in Table 16. The region named "Western Australia - Outback (North and South)" in the original data has been omitted as it did not match a region within the SA4 2016 ASGS.

Retirement Age Men in Australia remained unchanged at 67 Years in 2025 from 67 Years in 2024. This dataset provides - Australia Retirement Age Men - actual values, historical data, forecast, chart, statistics, economic calendar and news.

AbstractThis dataset is a subset of Surface Geology of Australia 2012, 1:1M scale symbolised by age classification.The Surface Geology of Australia 1:1M scale dataset (2012 edition) is a seamless national coverage of outcrop and surficial geology, compiled for use at or around 1:1 million scale.The data maps outcropping bedrock geology and unconsolidated or poorly consolidated regolith material covering bedrock.Geological units are represented as polygon and line geometries, and are attributed with information regarding stratigraphic nomenclature and hierarchy, age, lithology, and primary data source.The dataset also contains geological contacts, structural features such as faults and shears, and miscellaneous supporting lines like the boundaries of water and ice bodies.The 2012 dataset has been updated from the previous 2010 data by updating geological unit data to 2012 information in the Australian Stratigraphic Units Database, incorporating new published mapping in the Northern Territory and Queensland, and correcting errors or inconsistent data identified in the previous edition, particularly in the Phanerozoic geology of Western Australia.The attribute structure of the dataset has also been revised to be more compatible with the GeoSciML data standard, published by the IUGS Commission for Geoscience Information.The first edition of this national dataset was first released in 2008, with map data compiled largely from simplifying and edgematching existing 1:250 000 scale geological maps.Where these maps were not current, more recent source maps ranging in scale from 1:50 000 to 1:1 million were used.In some areas where the only available geological maps were old and poorly located, some repositioning of mapping using recent satellite imagery or geophysics was employed.CurrencyDate modified: December 2014Modification frequency: As neededData extentSpatial extentNorth: -8.47206°South: -58.4495°East: 171.8011°West: 67.05399°Source informationGeoscience Australia catalog entry: Surface Geology of Australia 1:1 million scale dataset 2012 editionLineage statementThe 2012 dataset has been updated from the previous 2010 data by updating geological unit data to 2012 information in the Australian Stratigraphic Units Database, incorporating new published mapping in the Northern Territory and Queensland, and correcting errors or inconsistent data identified in the previous edition, particularly in the Phanerozoic geology of Western Australia.The attribute structure of the dataset has also been revised to be more compatible with the GeoSciML data standard, published by the IUGS Commission for Geoscience Information.The first edition of this national dataset was first released in 2008, with map data compiled largely from simplifying and edgematching existing 1:250 000 scale geological maps.Where these maps were not current, more recent source maps ranging in scale from 1:50 000 to 1:1 million were used.In some areas where the only available geological maps were old and poorly located, some repositioning of mapping using recent satellite imagery or geophysics was employed.Data dictionaryAttribute nameDescriptionmapSymbolLetter symbol or code representing the geologic unitplotSymbolLetter symbol or code representing the geologic unit for display on a map. May be a simplified version of mapSymbolstratnoUnique unit number from the Australian Stratigraphic Units DatabasenameName of the geologic unitdescriptionText description of the geologic unitgeologicUnitTypeThe type of geologic unit. (eg, lithostratigraphic, chronostratigraphic, etc) Term from a controlled vocabulary.geologicUnitType_uriURI link to a controlled vocabulary term for geologic unit typegeologicHistoryText summary description of the geologic history of the geologic unitrepresentativeAge_uriURI link to a controlled vocabulary term for the representative summary age for the geologic unitrepresentativeYoungerAge_uriURI link to a controlled vocabulary term for the older named age for the geologic unitrepresentativeOlderAge_uriURI link to a controlled vocabulary term for the younger named age for the geologic unitlithologyA summary description of the lithological composition of the geologic unitrepresentativeLithology_uriURI link to a controlled vocabulary term for the primary lithological composition of the geologic unitbodyMorphologyDescription of the type of occurrence of the geologic unit (e.g. pluton, dyke, sill, markerbed, vein, etc)observationMethodDescription of the observation method or compilation method used compile the mapped geologic unitidentityConfidenceDescription of the confidence in the interpretation of the geologic unitsourceText describing feature-specific details and citations to source materials, and if available providing URLs to reference material and publications describing the geologic feature. This could be a short text synopsis of key information that would also be in the metadata record referenced by metadata_uri.metadata_uriURI referring to a metadata record describing the provenance of data.mappingFrameDescription of the frame of reference of the mapped data (e.g. earth surface, top of bedrock, top of Neoproterozoic basement)resolutionScaleThe denominator of the scale at which the mapped data is designed to be representedcaptureScaleThe denominator of the scale of data from which the mapped feature has been compiledcaptureDateThe date of original data capture for this mapped feature Metadata Statement - Surface Geology of Australia, 1:2.5 million scale, 2012 edition 6modifiedDateThe date of modification of this mapped feature, if applicableplotRankA numeric indicator of the intention for how this mapped feature is to be plotted on a map. (1 = normal plotting feature; 2 = non-plotting feature)mappedFeatureIDUnique identifier (URI) for the mapped line segmentgeologicUnitIDUnique identifier (URI) for the geologic unitContactGeoscience Australia, clientservices@ga.gov.au

The age with depth of core MD032607 is calculated through 18O values, which are then correlated with the SPECMAP age-model. Based upon these measurements the age at the deepest part of the core (3270cm) is ~176,590yrs BP. The age vs. depth profile for this core indicates a major increase in the sedimentation rate during the initial phase of MIS 6, when the sedimentation rate is ~41cm/1000yrs. The average sedimentation rate over the length of the core is 18.7cm/1000yrs.

Introduction

This Australian English Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English -speaking Real Estate customers. With over 40 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 40 hours of dual-channel call center recordings between native Australian English speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for English real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Background The aim of the study was to measure knowledge about the symptoms, prevalence and natural history of stroke; the level of concern about having a stroke; understanding of the possibilities for preventing stroke, and the relationship between age, sex, country of origin, educational level, income, self-reported risk factors, and the above factors. Methods A random sample of households was selected from an electronic telephone directory in Newcastle and Lake Macquarie area of New South Wales, Australia, between 10 September and 13 October 1999. Within each household the person who was between 18 and 80 years of age and who had the next birthday was eligible to participate in the study (1325 households were eligible). The response rate was 62%. Results The most common symptoms of stroke listed by respondents were "Sudden difficulty of speaking, understanding or reading" identified by 60.1% of the respondents, and "paralysis on one side of body" identified by 42.0% of the respondents. The level of knowledge of the prevalence of a stroke, full recovery after the stroke, and death from stroke was low and generally overestimated. 69.9% of the respondents considered strokes as being either moderately or totally preventable. There were few predictors of knowledge. Conclusion The study suggests that educational strategies may be required to improve knowledge about a wide range of issues concerning stroke in the community, as a prelude to developing preventive programmes.

Abstract This dataset is a subset of Surface Geology of Australia 2012,1:2.5M Scale symbolised by lithology classification. Attributes include summary geological information for each unit polygon, and metadata about the capture and recommended portrayal of the polygons. The 1:2.5M scale geology of Australia data documents the distribution and age of major stratigraphic, intrusive and medium to high-grade metamorphic rock units of onshore Australia. This edition contains the same geological content as previous editions (1998 to 2010), but is structured according to Geoscience Australia's 2012 data standards. The dataset was compiled to use at scales between 1:2,500,000 and 1:5,000,000 inclusive. The units distinguished/mapped mainly represent stratigraphic supergroups, regional intrusive associations and regional metamorphic complexes. Groupings of Precambrian units in the time-space diagram are generally separated by major time breaks; Phanerozoic units are grouped according to stratigraphic age i.e. System/Period. The time-space diagram has the added benefit that it provides a summary of units currently included on the themes. The method used to distinguish sedimentary and many volcanic units varies for each geological eon as follows:

Cenozoic units are morphological units which emphasise the relationship of the sedimentary fill to the landscape; Mesozoic units are regionally extensive to continent-wide time-rock units which emphasise the System of Period(s); Paleozoic units are stratotectonic units that emphasise either the dominant System or Period(s) or the range of Periods; Proterozoic units are commonly regional stratotectonic units - separated by major time breaks and split into the Paleoproterozoic, Mesoproterozoic and Neoproterozoic Eras - which are generally unique to each cratonic region; and Archean units are regional lithological units grouped into broad time divisions.

Metamorphic units are lithological units which emphasise the metamorphic facies and timing of the last major metamorphic event. Igneous units are regional units which emphasise the dominant lithology and are grouped into broad time divisions. Currency Date modified: December 2014 Modification frequency: As needed Data extent Spatial extent North: -8.8819° South: -47.1937° East: 163.1921° West: 109.2335° Source information Geoscience Australia catalog entry: Surface Geology of Australia 1:2.5 million scale dataset 2012 edition Lineage statement The geological content of the 2012 edition of the 1:2.5M surface geology of Australia is the same as the previous 2010 edition (ANZLIC dataset ID = ANZCW0703013817), restructured to comply with 2012 Geoscience Australia and international data standards. The original data was compiled from digital data, mainly at 1:2 500 000 scale, supplied by AGSO, GSWA, NTGS, PIRSA, GSQ, GSTAS, GSNSW and GSVIC and from data obtained from many other groups. In order to synthesise data from a variety of sources into a coherent product, the degree and nature of modification of the source data varied from case to case. Cenozoic and Mesozoic units were derived from sources, including the Cenozoic Paleogeographic Atlas of Australia (Landford et al., 1995), the Geology of Australia 1986 and a compilation of Cenozoic basins in the Alice Springs region by B.R. Senior et al. (AGSO Record 1994/66). The Phanerozoic units of southeastern Australia are substantially a modification of the 1:2 500 000 scale map entitled "Stratotectonic and Structural Elements of the Tasman Fold Belt System". The geology of Tasmania is a generalisation of data assembled as part of the TASGO project (a GSTAS and AGSO/AGCRC venture completed in 1997). The geology of South Australia is a highly generalised modification of the 1993 1:2 000 000 scale Geological Map of South Australia. For the Precambrian compilation, much of the geology of Western Australia has been derived from the Geological Map of Western Australia, 1988 with some modifications. The geology of the Kimberley, Halls Creek, Tanami and Arunta regions has been updated in line with recent mapping and some input from magnetic interpretation to emphasise relationships with the Tanami region. The geology of the Amadeus region has been generalised from the 1:1 000 000 scale "Structural Map of the Amadeus Basin" (Compiler A.J. Stewart). The geology of the Musgrave region has been re-compiled and simplified. The geology of North Queensland has been generalised by D. Palfreyman and D. Pillinger from the "North Qld Geology, 1997" 1:1 000 000 scale map (compilers J.H.C. Bain & D. Haipola). Data dictionary

Attribute name Description

mapSymbol Letter symbol or code representing the geologic unit

plotSymbol Letter symbol or code representing the geologic unit for display on a map. May be a simplified version of mapSymbol

stratno Unique unit number from the Australian Stratigraphic Units Database

name Name of the geologic unit

description Text description of the geologic unit

geologicUnitType The type of geologic unit. (eg, lithostratigraphic, chronostratigraphic, etc) Term from a controlled vocabulary.

geologicUnitType_uri URI link to a controlled vocabulary term for geologic unit type

geologicHistory Text summary description of the geologic history of the geologic unit

representativeAge_uri URI link to a controlled vocabulary term for the representative summary age for the geologic unit

representativeYoungerAge_uri URI link to a controlled vocabulary term for the older named age for the geologic unit

representativeOlderAge_uri URI link to a controlled vocabulary term for the younger named age for the geologic unit

lithology A summary description of the lithological composition of the geologic unit

representativeLithology_uri URI link to a controlled vocabulary term for the primary lithological composition of the geologic unit

bodyMorphology Description of the type of occurrence of the geologic unit (eg, pluton, dyke, sill, markerbed, vein, etc)

observationMethod Description of the observation method or compilation method used compile the mapped geologic unit

identityConfidence Description of the confidence in the interpretation of the geologic unit

source Text describing feature-specific details and citations to source materials, and if available providing URLs to reference material and publications describing the geologic feature. This could be a short text synopsis of key information that would also be in the metadata record referenced by metadata_uri.

metadata_uri URI referring to a metadata record describing the provenance of data.

mappingFrame Description of the frame of reference of the mapped data (eg, earth surface, top of bedrock, top of Neoproterozoic basement)

resolutionScale The denominator of the scale at which the mapped data is designed to be represented

captureScale The denominator of the scale of data from which the mapped feature has been compiled

captureDate The date of original data capture for this mapped feature Metadata Statement - Surface Geology of Australia, 1:2.5 million scale, 2012 edition 6

modifiedDate The date of modification of this mapped feature, if applicable

plotRank A numeric indicator of the intention for how this mapped feature is to be plotted on a map. (1 = normal plotting feature; 2 = non-plotting feature)

mappedFeatureID Unique identifier (URI) for the mapped line segment

geologicUnitID Unique identifier (URI) for the geologic unit

Contact Geoscience Australia, clientservices@ga.gov.au

BackgroundOne prominent barrier faced by healthcare consumers when accessing health services is a common requirement to complete repetitive, inefficient paper-based documentation at multiple registration sites. Digital innovation has a potential role to reduce the burden in this area, through the collection and sharing of data between healthcare providers. While there is growing evidence for digital innovations to potentially improve the effectiveness and efficiency of health systems, there is less information on the willingness of healthcare consumers to embrace and utilise technology to provide data.AimThe study aims to improve understanding of consumers’ preference for utilising a digital health administration mobile app.MethodsThe online study used a stated preference experiment design to explore aspects of consumers’ preference for a mobile health administration app and its impact on the likelihood of using the app. The survey was answered by a representative sample (by age and gender) of Australian adults, and sociodemographic factors were also recorded for analysis. Each participant answered eight choice sets in which a hypothetical app (defined by a set of dimensions and levels) was presented and the respondent was asked if they would be willing to provide data using that app. Analysis was conducted using bivariate logistic regression.ResultsFor the average respondent, the two most important dimensions were the time it took to register on the app and the electronic governance arrangements around their personal information. Willingness to use any app was found to differ based on respondent characteristics: people with higher education, and women, were relatively more willing to utilise the mobile health app.ConclusionThis study investigated consumers’ willingness to utilise a digital health administration mobile app. The identification of key characteristics of more acceptable apps provide valuable insight and recommendations for developers of similar digital health administration technologies. This would increase the likelihood of achieving successful acceptance and utilisation by consumers. The results from this study provide evidence-based recommendations for future research and policy development, planning and implementation of digital health administration mobile applications in Australia.

Abstract Surface Geology of Australia 2012 - Lines contains geological units (outcrop or near-outcrop) that are too narrow to be represented by polygons. The Surface Geology of Australia 1:1M scale dataset (2012 edition) is a seamless national coverage of outcrop and surficial geology, compiled for use at or around 1:1 million scale. The data maps outcropping bedrock geology and unconsolidated or poorly consolidated regolith material covering bedrock. Geological units are represented as polygon and line geometries, and are attributed with information regarding stratigraphic nomenclature and hierarchy, age, lithology, and primary data source. The dataset also contains geological contacts, structural features such as faults and shears, and miscellaneous supporting lines like the boundaries of water and ice bodies. The 2012 dataset has been updated from the previous 2010 data by updating geological unit data to 2012 information in the Australian Stratigraphic Units Database, incorporating new published mapping in the Northern Territory and Queensland, and correcting errors or inconsistent data identified in the previous edition, particularly in the Phanerozoic geology of Western Australia. The attribute structure of the dataset has also been revised to be more compatible with the GeoSciML data standard, published by the IUGS Commission for Geoscience Information. The first edition of this national dataset was first released in 2008, with map data compiled largely from simplifying and edgematching existing 1:250 000 scale geological maps. Where these maps were not current, more recent source maps ranging in scale from 1:50 000 to 1:1 million were used. In some areas where the only available geological maps were old and poorly located, some repositioning of mapping using recent satellite imagery or geophysics was employed. Currency Date modified: December 2014 Modification frequency: As needed Data extent Spatial extent North: -8.47206° South: -58.4495° East: 171.8011° West: 67.05399° Source information Geoscience Australia catalog entry: Surface Geology of Australia 1:1 million scale dataset 2012 edition Lineage statement The 2012 dataset has been updated from the previous 2010 data by updating geological unit data to 2012 information in the Australian Stratigraphic Units Database, incorporating new published mapping in the Northern Territory and Queensland, and correcting errors or inconsistent data identified in the previous edition, particularly in the Phanerozoic geology of Western Australia. The attribute structure of the dataset has also been revised to be more compatible with the GeoSciML data standard, published by the IUGS Commission for Geoscience Information. The first edition of this national dataset was first released in 2008, with map data compiled largely from simplifying and edgematching existing 1:250 000 scale geological maps. Where these maps were not current, more recent source maps ranging in scale from 1:50 000 to 1:1 million were used. In some areas where the only available geological maps were old and poorly located, some repositioning of mapping using recent satellite imagery or geophysics was employed. Data dictionary

Attribute name Description

mapSymbol Letter symbol or code representing the geologic unit

plotSymbol Letter symbol or code representing the geologic unit for display on a map. May be a simplified version of mapSymbol

stratno Unique unit number from the Australian Stratigraphic Units Database

name Name of the geologic unit

description Text description of the geologic unit

geologicUnitType The type of geologic unit. (eg, lithostratigraphic, chronostratigraphic, etc) Term from a controlled vocabulary.

geologicUnitType_uri URI link to a controlled vocabulary term for geologic unit type

geologicHistory Text summary description of the geologic history of the geologic unit

representativeAge_uri URI link to a controlled vocabulary term for the representative summary age for the geologic unit

representativeYoungerAge_uri URI link to a controlled vocabulary term for the older named age for the geologic unit

representativeOlderAge_uri URI link to a controlled vocabulary term for the younger named age for the geologic unit

lithology A summary description of the lithological composition of the geologic unit

representativeLithology_uri URI link to a controlled vocabulary term for the primary lithological composition of the geologic unit

bodyMorphology Description of the type of occurrence of the geologic unit (eg, pluton, dyke, sill, markerbed, vein, etc)

observationMethod Description of the observation method or compilation method used compile the mapped geologic unit

identityConfidence Description of the confidence in the interpretation of the geologic unit

positionalAccuracy_m Estimate of the accuracy of the mapped feature, in metres

source Text describing feature-specific details and citations to source materials, and if available providing URLs to reference material and publications describing the geologic feature. This could be a short text synopsis of key information that would also be in the metadata record referenced by metadata_uri.

metadata_uri URI referring to a metadata record describing the provenance of data.

mappingFrame Description of the frame of reference of the mapped data (eg, earth surface, top of bedrock, top of Neoproterozoic basement)

resolutionScale The denominator of the scale at which the mapped data is designed to be represented

captureScale The denominator of the scale of data from which the mapped feature has been compiled

captureDate The date of original data capture for this mapped feature Metadata Statement - Surface Geology of Australia, 1:2.5 million scale, 2012 edition 6

modifiedDate The date of modification of this mapped feature, if applicable

plotRank A numeric indicator of the intention for how this mapped feature is to be plotted on a map. (1 = normal plotting feature; 2 = non-plotting feature

symbol Identifier for a line symbol from symbolization scheme for portrayal (eg, dykes, markerbeds, veins)

mappedFeatureID Unique identifier (URI) for the mapped line segment

geologicUnitID Unique identifier (URI) for the geologic unit

Contact Geoscience Australia, clientservices@ga.gov.au

This dataset presents the summary preliminary estimates of the resident population by age and sex as at 30 June 2017, this includes population by sex, median age by sex and percentage of the population within a certain age range. The data is aggregated to the 2017 edition of the Local Government Areas (LGA). Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period. This data is ABS data (catalogue number: 3235.0) available from the Australian Bureau of Statistics. For more information please visit the Explanatory Notes. AURIN has spatially enabled the data. Regions which contain unpublished data have been left blank in the dataset. Where regions have zero population, the relating ratio and percentage columns have been left blank.

This dataset presents the summary preliminary estimates of the resident population by age and sex as at 30 June 2019, this includes population by sex, median age by sex and percentage of the …Show full descriptionThis dataset presents the summary preliminary estimates of the resident population by age and sex as at 30 June 2019, this includes population by sex, median age by sex and percentage of the population within a certain age range. The data is aggregated to the 2019 edition of the Local Government Areas (LGA). Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period. This data is ABS data (catalogue number: 3235.0) available from the Australian Bureau of Statistics. For more information please visit the Explanatory Notes. AURIN has spatially enabled the data. Regions which contain unpublished data have been left blank in the dataset. Where regions have zero population, the relating ratio and percentage columns have been left blank. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2018): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)

This dataset presents the summary preliminary estimates of the resident population by age and sex as at 30 June 2019, this includes population by sex, median age by sex and percentage of the …Show full descriptionThis dataset presents the summary preliminary estimates of the resident population by age and sex as at 30 June 2019, this includes population by sex, median age by sex and percentage of the population within a certain age range. The data is aggregated to Statistical Areas Level 2 (SA2), according to the 2016 edition of the Australian Statistical Geography Standard (ASGS). Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period. This data is ABS data (catalogue number: 3235.0) available from the Australian Bureau of Statistics. For more information please visit the Explanatory Notes. AURIN has spatially enabled the data. Regions which contain unpublished data have been left blank in the dataset. Where regions have zero population, the relating ratio and percentage columns have been left blank. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2020): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)

Abstract Surface Geology of Australia 2012 - Faults contains all brittle to ductile style structures, represented as lines, along which displacement has occurred, from a simple, single 'planar' brittle or ductile surface to a fault system comprised of many strands of both brittle and ductile nature. The Surface Geology of Australia 1:1M scale dataset (2012 edition) is a seamless national coverage of outcrop and surficial geology, compiled for use at or around 1:1 million scale. The data maps outcropping bedrock geology and unconsolidated or poorly consolidated regolith material covering bedrock. Geological units are represented as polygon and line geometries, and are attributed with information regarding stratigraphic nomenclature and hierarchy, age, lithology, and primary data source. The dataset also contains geological contacts, structural features such as faults and shears, and miscellaneous supporting lines like the boundaries of water and ice bodies. The 2012 dataset has been updated from the previous 2010 data by updating geological unit data to 2012 information in the Australian Stratigraphic Units Database, incorporating new published mapping in the Northern Territory and Queensland, and correcting errors or inconsistent data identified in the previous edition, particularly in the Phanerozoic geology of Western Australia. The attribute structure of the dataset has also been revised to be more compatible with the GeoSciML data standard, published by the IUGS Commission for Geoscience Information. The first edition of this national dataset was first released in 2008, with map data compiled largely from simplifying and edgematching existing 1:250 000 scale geological maps. Where these maps were not current, more recent source maps ranging in scale from 1:50 000 to 1:1 million were used. In some areas where the only available geological maps were old and poorly located, some repositioning of mapping using recent satellite imagery or geophysics was employed. Currency Date modified: December 2014 Modification frequency: As needed Data extent Spatial extent North: -8.47206° South: -58.4495° East: 171.8011° West: 67.05399° Source information Geoscience Australia catalog entry: Surface Geology of Australia 1:1 million scale dataset 2012 edition Lineage statement The 2012 dataset has been updated from the previous 2010 data by updating geological unit data to 2012 information in the Australian Stratigraphic Units Database, incorporating new published mapping in the Northern Territory and Queensland, and correcting errors or inconsistent data identified in the previous edition, particularly in the Phanerozoic geology of Western Australia. The attribute structure of the dataset has also been revised to be more compatible with the GeoSciML data standard, published by the IUGS Commission for Geoscience Information. The first edition of this national dataset was first released in 2008, with map data compiled largely from simplifying and edgematching existing 1:250 000 scale geological maps. Where these maps were not current, more recent source maps ranging in scale from 1:50 000 to 1:1 million were used. In some areas where the only available geological maps were old and poorly located, some repositioning of mapping using recent satellite imagery or geophysics was employed. Data dictionary

Attribute name Description

faultType URI referring to a controlled vocabulary term defining the fault/shear type

faultType_uri URI link to a controlled vocabulary term for fault/shear type

name Display name for the fault or shear

description Text description of the fault or shear

exposure Indication of whether the mapped contact is exposed at the Earth surface. (ie, exposed, concealed)

faultFill Secondary or deformed material which may fill the structure. Term from a controlled vocabulary of earth material types

deformationStyle Describes the style of deformation (eg brittle, ductile etc) for the fault/shear

deformationStyle_uri URI referring to a controlled concept from a vocabulary defining the fault/shear deformation style

movementType Summarises the type of movement (eg dip-slip, strike-slip) on the fault/shear

movementType_uri URI referring to a controlled concept from a vocabulary defining the fault/shear movement type

movementSense Term describing the sense of movement (eg, dextral, sinistral) on the fault/shear

displacement Summarises the displacement across the fault/shear

dip Dip of the fault surface. Range = 0-90

dipDirection Dip direction of the fault surface. Range = 0-360

width True width (in metres) of the structure. Must be a number > 0, or null.

geologicHistory Text summary of the geologic history of the fault/shear. May include geologic age periods and deformation phase notation (ei, D1, D2, D3)

representativeAge_uri URI link to a controlled vocabulary term for the representative summary age for the fault or shear

representativeYoungerAge_uri URI link to a controlled vocabulary term for the older named age for the fault/shear

representativeOlderAge_uri URI link to a controlled vocabulary term for the younger named age for the fault/shear

faultSystemName The name of a larger fault system to which this structure may belong

faultSystemID Unique ID of a larger fault system to which this structure may belong

observationMethod Description of the observation method or compilation method used compile the mapped geologic structure

identityConfidence Description of the confidence in the interpretation of the geologic structure

positionalAccuracy_m Estimate of the accuracy of the mapped feature, in metres

source Text describing feature-specific details and citations to source materials, and if available providing URLs to reference material and publications describing the geologic feature. This could be a short text synopsis of key information that would also be in the metadata record referenced by metadata_uri.

metadata_uri URI referring to a metadata record describing the provenance of data

mappingFrame Description of the frame of reference of the mapped data (eg, earth surface, top of bedrock, top of Neoproterozoic basement)

resolutionScale The denominator of the scale at which the mapped data is designed to be represented

captureScale The denominator of the scale of data from which the mapped feature has been compiled

captureDate The date of original data capture for this mapped feature

modifiedDate The date of modification of this mapped feature, if applicable

plotRank A numeric indicator of the intention for how this mapped feature is to be plotted on a map. (1 = normal plotting feature; 2 = non-plotting feature)

symbol Identifier for a symbol from symbolization scheme for portrayal

mappedFeatureID Unique identifier (URI) for the mapped line segment

faultID Unique identifier (URI) linking to a GeoSciML geologic feature instance which describes this mapped feature. Maps to 'SpecificationID' in GeoSciML-Portrayal

Contact Geoscience Australia, clientservices@ga.gov.au

This dataset presents the summary preliminary estimates of the resident population by age and sex as at 30 June 2018, this includes population by sex, median age by sex and percentage of the population within a certain age range. The data is aggregated to the 2018 edition of the Local Government Areas (LGA).

Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period.

This data is ABS data (catalogue number: 3235.0) available from the Australian Bureau of Statistics.

For more information please visit the Explanatory Notes.

• AURIN has spatially enabled the data.

• Regions which contain unpublished data have been left blank in the dataset.

• Where regions have zero population, the relating ratio and percentage columns have been left blank.