Student attendance during semester 1 in SA Government schools by year level. Data represents attendance rates for semester 1 of each year from 2012. Important notes: • Attendance rate = (number of days attending school / number of days enrolled) x 100. • Semester 1 Attendance rates are only calculated for full time students who were enrolled or left during Semester 1. • Both whole day and part day absences are counted. • Attendance data is not collected from schools 1717 Watarru Anangu School (non operational), 849 Open Access College, 810 Thebarton Senior College , 583 Marden Senior College, 1012 Northern Adelaide Senior College and 195 Youth Education Centre. • Attendance rates in 2020 are lower than anticipated due to Covid-19 lockdowns.

This Cost of International Education dataset compiles detailed financial information for students pursuing higher education abroad. It covers multiple countries, cities, and universities around the world, capturing the full tuition and living expenses spectrum alongside key ancillary costs. With standardized fields such as tuition in USD, living-cost indices, rent, visa fees, insurance, and up-to-date exchange rates, it enables comparative analysis across programs, degree levels, and geographies. Whether you’re a prospective international student mapping out budgets, an educational consultant advising on affordability, or a researcher studying global education economics, this dataset offers a comprehensive foundation for data-driven insights.

Description

Potential Uses

• Budget Planning Prospective students can filter by country, program level, or university to forecast total expenses and compare across destinations.
• Policy Analysis Educational policymakers and NGOs can assess the affordability of international education and design support programs.
• Economic Research Economists can correlate living-cost indices and tuition levels with enrollment rates or student demographics.
• University Benchmarking Institutions can benchmark their fees and ancillary costs against peer universities worldwide.

Notes on Data Collection & Quality

• Currency Conversions All monetary values are unified to USD using contemporaneous exchange rates to facilitate direct comparison.
• Living Cost Index Derived from reputable city-index publications (e.g., Numbeo, Mercer) to standardize disparate cost-of-living metrics.
• Data Currency Exchange rates and fee schedules should be periodically updated to reflect market fluctuations and policy changes.

Feel free to explore, visualize, and extend this dataset for deeper insights into the true cost of studying abroad!

The Electoral Integrity Project at Harvard University and the University of Sydney (www.electoralintegrityproject.com) developed the AVE data, release 1.0. The dataset contains information from a three-wave panel survey designed to gather the views of a representative sample of ordinary Australians just before and after the 2nd July 2016 Australian federal elections. The survey monitored Australian voters’ experience at the polls, perceptions of the integrity and convenience of the registration and voting process, patterns of civic engagement, public confidence in electoral administration, and attitudes towards reforms, such as civic education campaigns and convenience voting facilities. Respondents were initially contacted in the week before the election between 28 June and 1 July and completed an online questionnaire lasting approximately 15 minutes. This forms the pre-election base line survey (wave 1). The same individuals were contacted again after the election to complete a longer survey, an average of 25 minutes in length. Respondents in wave 2 were contacted between 4 July and 19 July, with two thirds completing the survey after the first week. About six weeks later, the same respondents were interviewed again (wave 3) beginning on 23 August and ending on 13 September. The initial sample contains 2,139 valid responses for the first wave of questionnaires, 1,838 for the second wave (an 86 percent retention rate), and 1,543 for the third wave (84 percent retention rate). Overall, 72 percent of the respondents were carried over from the pre-election wave to the final wave. The following files can be accessed: a) dataset in Stata and SPSS formats; b) codebook; c) questionnaire. The EIP acknowledges support from the Kathleen Fitzpatrick Australian Laureate from the Australian Research Council (ARC ref: FL110100093). **** EIP further publications: BOOKS • LeDuc, Lawrence, Richard Niemi and Pippa Norris. Eds. 2014. Comparing Democracies 4: Elections and Voting in a Changing World. London: Sage Publications. • Nai, Alessandro and Walter, Annemarie. Eds. 2015 New Perspectives on Negative Campaigning: Why Attack Politics Matters. Colchester: ECPR Press. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. Eds. 2014. Advancing Electoral Integrity. New York: Oxford University Press. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. Eds. 2015. Contentious Elections: From Ballots to the Barricades. New York: Routledge. • Norris, Pippa. 2014. Why Electoral Integrity Matters. New York: Cambridge University Press. • Norris, Pippa. 2015. Why Elections Fail. New York: Cambridge University Press. • Norris, Pippa and Andrea Abel van Es. Eds. 2016. Checkbook Elections? Political Finance in Comparative Perspective. Oxford University Press. ARTICLES AND CHAPTERS • W. Frank. 2013. ‘Assessing the quality of elections.’ Journal of Democracy. 24(4): 124-135.• Lago, Ignacio and Martínez i Coma, Ferran. 2016. ‘Challenge or Consent? Understanding Losers’ Reactions in Mass Elections’. Government and Opposition doi:10.1071/gov.3015.31 • Martínez i Coma, Ferran and Lago, Ignacio. 2016. 'Gerrymandering in Comparative Perspective’ Party Politics DOI: 10.1177/1354068816642806 • Norris, Pippa. 2013. ‘Does the world agree about standards of electoral integrity? Evidence for the diffusion of global norms.’ Special issue of Electoral Studies. 32(4):576-588. • Norris, Pippa. 2013. ‘The new research agenda studying electoral integrity’. Special issue of Electoral Studies. 32(4): 563-575.57 • Norris, Pippa. 2014. ‘Electoral integrity and political legitimacy.’ In Comparing Democracies 4. Lawrence LeDuc, Richard Niemi and Pippa Norris. Eds. London: Sage. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. 2014. ‘Measuring electoral integrity: A new dataset.’ PS: Political Science & Politics. 47(4): 789-798. • Norris, Pippa. 2016 (forthcoming). ‘Electoral integrity in East Asia.’ Routledge Handbook on Democratization in East Asia. Tun-jen Cheng and Yun-han Chu. Eds. Routledge: New York. • Norris, Pippa. 2016 (forthcoming). ‘Electoral transitions: Stumbling out of the gate.’ In Rebooting Transitology – Democratization in the 21st Century. Mohammad-Mahmoud Ould Mohamedou and Timothy D. Sisk. Eds. • Pietsch, Juliet; Michael Miller and Jeffrey Karp. 2015. ‘Public support for democracy in transitional regimes.’ Journal of Elections, Public Opinion and Parties. 25(1): 1–9. DOI: 10.1080/17457289.2014. • Smith, Rodney. 2016 (forthcoming). ‘Confidence in paper-based and electronic voting channels: Evidence from Australia.’ Australian Journal of Political Science. ID: 1093091 DOI: 10.1080/10361146.2015.1093091 dx.doi.org/10.1080/07907184.2015.1099097 • Van Ham, Carolien and Staffan Lindberg. 2015. ‘From sticks to carrots: Electoral manipulation in Africa, 1986-2012’, Gover... Visit https://dataone.org/datasets/sha256%3A9efcfe40123531a7f785369bae96a30beb0f41c1ce7334bc7c398a54be5e69f5 for complete metadata about this dataset.

Attendance rate for semester 1 in SA Government schools by school and year level, collected as part of the annual enrolment data collection in Term 3. Data provided each year from 2018. Important notes: • Attendance rate = (number of days attending school / number of days enrolled) x 100. • Attendance rates are only calculated for full time students who were enrolled or left during Semester 1. • Both whole day and part day absences are counted. • Attendance data is not collected from schools 1717 Watarru Anangu School (non operational), 849 Open Access College, 810 Thebarton Senior College , 583 Marden Senior College, 1012 Northern Adelaide Senior College and 195 Youth Education Centre. • Attendance rates in 2020 are lower than anticipated due to Covid-19 lockdowns.

This data collection relates to project 3.4 of the Centre for Global Higher Education: The transformative potential of MOOCs and contrasting online pedagogies. The response of higher education systems to the possibilities of digital technologies has been sporadic and localised. System-level initiatives relate more to administration and research than to education, while institution-level responses focus mainly on installing virtual learning environments. One area where digital innovation in HE has been rapid and large-scale is the phenomenon of the spread of massive, open, online courses (MOOCs). The top universities in the US, a few in the UK, the EU, the Far East, Australia, and now also in parts of the Global South, have experimented with this form of HE. The transformative potential of MOOCs, while widely forecast, is still uncertain, for several reasons: MOOCs have done little to transform undergraduate education, as some 80 per cent of participants are highly qualified professionals. MOOC affordances and the large-scale participation rates are incompatible with the personal nurturing and scaffolding that supports high quality student learning. Universities and platform developers are still developing the business models they need to make MOOCs sustainable, and financially viable. In order to explore what features of MOOCs have most potential to transform Higher Education, in depth interviews with MOOC participants were conducted online.The last two generations have seen a remarkable world-wide transformation of higher education (HE) into a core social sector with continually expanding local and global reach. Most nations are moving towards, or have already become, 'high participation' HE systems in which the majority of people will be educated to tertiary level. In the UK HE is at the same time a pillar of science and the innovation system, a primary driver of productivity at work, a major employer and a mainstay of cities and regions, and a national export industry where 300,000 non-EU students generated over £7 billion in export-related earnings for the UK in 2012-13. In 2012, 60 per cent of UK school leavers were expected to graduate from tertiary education over the lifetime, 45 per cent at bachelor degree level, compared to OECD means of 53/39 per cent. Higher education and the scientific research associated with universities have never been more important to UK society and government. HE is large and inclusive with a key role in mediating the future. Yet it is poorly understood. Practice has moved ahead of social science. There has been no integrated research centre dedicated to this important part of the UK. The Centre for Engaged Global Higher Education (CEGHE), which has been funded initially for five years by the Economic and Social Research Council (ESRC), now fills that gap. On behalf of the ESRC CEGHE conducts and disseminates research on all aspects of higher education (HE), in order to enhance student learning and the contributions of Higher Education Institutions (HEIs) to their communities; develop the economic, social and global engagement of and impacts of UK HE; and provide data resources and advice for government and stakeholder organisations in HE in the four nations of the UK and worldwide. CEGHE is organised in three closely integrated research programmes that are focused respectively on global, national-system and local aspects of HE. CEGHE's team of researchers work on problems and issues with broad application to the improvement of HE; develop new theories about and ways of researching HE and its social and economic contributions; and respond also to new issues as they arise, within the framework of its research programmes. An important part of CEGHE's work is the preparation and provision of data, briefings and advice to national and international policy makers, for HEIs themselves, and for UK organisations committed to fostering HE and its engagement with UK communities and stakeholders. CEGHE's seminars and conferences are open to the public and it is dedicated to disseminating its research findings on a broad basis through published papers, media articles and its website and social media platform. CEGHE is led by Professor Simon Marginson, one of the world's leading researchers on higher education matters with a special expertise in global and international aspects of the sector. It works with partner research universities in Sheffield, Lancaster, Ireland, Australia, South Africa, Netherlands, China, Hong Kong SAR, Japan and USA. Among the issues currently the subject of CEGHE research projects are inquiries into ways and means of measuring and enhancing HE's contribution to the public good, university-industry collaboration in research, the design of an optimal system of tuition loans, a survey of the effects of tuition debt on the life choices of graduates such as investment in housing and family formation, the effects of widening participation on social opportunities in HE especially for under-represented social groups, trends and developments in HE in Europe and East Asia and the implications for UK HE, the emergence of new HE providers in the private and FE sectors, the future academic workforce in the UK and the skills that will be needed, student learning and knowledge in science and engineering, and developments in online HE

Data support a paper of this title:

A Geotemporospatial and Causal Inference Epidemiological Exploration of Substance and Cannabinoid Exposure as Drivers of Rising US Pediatric Cancer Rates

Data represent a compilation of various data inputs from numerous sources including the National Cancer Institute SEER*Stat National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database: NPCR and SEER Incidence – U.S. Cancer Statistics Public Use Research Database, 2019 submission (2001-2017), United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Released June 2020. Available at www.cdc.gov/cancer/public-use program; the National survey of Drug Use and Health conducted by the Substance Abuse and Mental Health Services Administration; and the US Census bureau.

Data also include inverse probability weights for cannabis exposure.

Data also include their geospatial linkage network constructed for all US states which makes Alaska and Hawaii spatially connected to the contiguous USA.

Data also include the R script used to conduct and prepare the analysis.

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally the researchers chose to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, researchers combined some subcategories. First, they were interested in total tax revenue, as well as the shares of total revenue coming from direct and indirect taxes. Further, they measured two sub-categories of direct taxation, namely taxes on property and income. For indirect taxes, they separated excises, consumption, and customs.

This dataset, released August 2017, contains Aboriginal population as a percentage of the total usual resident population, 2016. The data is by Local Government Area (LGA) 2016 geographic …Show full descriptionThis dataset, released August 2017, contains Aboriginal population as a percentage of the total usual resident population, 2016. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

This dataset, released August 2017, contains Aboriginal population as a percentage of the total usual resident population by 5 year age groups: 0-4 years to 65+ years, 2016. The data is by Local …Show full descriptionThis dataset, released August 2017, contains Aboriginal population as a percentage of the total usual resident population by 5 year age groups: 0-4 years to 65+ years, 2016. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

A catch and mark–recapture survey was conducted over 4–6 successive days every month over an 8-month study period between May and December in 2018. A minimum of 15 baited wooden pots enclosed by mesh were used to capture individuals at each study location, with each pot site being sampled twice on each trip. Additional mark–recapture data were obtained from a simultaneous and comparable on-going research program conducted by the DPIRD (Fisheries Research) at Seven Mile Beach between May 2017 and November 2018. Historical data from a previous tagging study between November 2017 and December 2017 conducted by the University of Western Australia (UWA) contributed an additional 1682 tagged individuals.

This abstract briefly summarizes data collected from a series of experiments forming a total of 6 chapters in Teka Dewo’s PhD thesis (2022). The data were collected from about 8 different field and laboratory experiments conducted at different times on chicken gastrointestinal nematodes and cover worm egg recovery, storage, propagation in chickens and in vivo/ in vitro anthelmintic efficacy testing. One category of data was generated from an online survey of producers which was implemented using SurveyMonkey platform. Data from the first experiment (Chapter 3) were generated from studies on worm eggs/larvae and include in vitro egg production by mature female worms (total count), egg embryonation (described as five egg morphological stages), and viability and hatching, most of which were expressed as percentage (%). The second experiment (Chapter 4) was a worm propagation study and measurements included chicken body weight and excreta egg count (EEC, expressed as eggs/gram of excreta/EPG) recorded over time until 10 weeks post-infection, and excreta consistency score (ECS) and worm counts recorded at 8 and 10 weeks post-infection. For in vivo anthelmintic efficacy studies (chapters 5, 6 and 7 which involved 5 experiments) the measurements were mainly pre- and post-treatment EEC (expressed as EPG) recorded on the day of anthelmintic treatment (day 0) and then 10 days post-treatment, and worm counts (adult, luminal larvae and histotrophic larvae) recorded 10 days post-treatment. Two experiments (Chapter 6) also assessed effect of anthelmintics on embryonation of eggs recovered from worms eliminated following treatment and measurements were percentage egg embryonation (described as five egg morphological stages). Measurements for the in vitro anthelmintic efficacy study (chapter 8) included worm egg recovery efficiency from excreta (%), morphological quality of eggs (% intact or damaged eggs), developmental ability of eggs (%) described as five egg morphological stages, larval hatching (%) and survival over time (%), inhibition of egg embryonation (%) and inhibition of larval migration (%). All data collected from each experiment were entered into excel file separately with detailed information and keys of different abbreviations used on separate sheets. Data from some parameters such as EEC, worm count and drug concentrations were log transformed before analysis to attain normality of data distribution.