This dataset is about universities in Australia. It has 39 rows. It features 3 columns: country, and graduate students.

Universities are now observed using social media communications channels for a variety of purposes, including marketing, student recruitment, student support and alumni communication. This paper presents an investigation into the use of the Twitter social media platform by universities in Australia, using publicly available Twitter data over a two year period. A social media network visualisation method is developed to make visible the interactions between a university and its stakeholders in the Twitter environment. This analysis method provides insights into the differing ways Australian universities are active on Twitter, and how universities might more effectively use the platform to achieve their individual objectives for institutional social media communications.

A common feature of many citizen science projects is the collection of data by unpaid contributors with the expectation that the data will be used in research. Here we report a teaching strategy that combined citizen science with inquiry-based learning to offer first year university students an authentic research experience. A six-year partnership with the Australian phenology citizen science program ClimateWatch has enabled biology students from the University of Western Australia to contribute phenological data on plants and animals, and to conduct the first research on unvalidated species datasets contributed by public and university participants. Students wrote scientific articles on their findings, peer-reviewed each other’s work and the best articles were published online in a student journal. Surveys of more than 1500 students showed that their environmental engagement increased significantly after participating in data collection and data analysis. However, only 31% of students agreed with the statement that “data collected by citizen scientists are reliable” at the end of the project, whereas the rate of agreement was initially 79%. This change in perception was likely due to students discovering erroneous records when they mapped data points and analysed submitted photographs. A positive consequence was that students subsequently reported being more careful to avoid errors in their own data collection, and making greater efforts to contribute records that were useful for future scientific research. Evaluation of our project has shown that by embedding a research process within citizen science participation, university students are given cause to improve their contributions to environmental datasets. If true for citizen scientists in general, enabling participants as well as scientists to analyse data could enhance data quality, and so address a key constraint of broad-scale citizen science programs.

The data measures/assesses mental health professionals' and students' perspectives regarding mental health recovery. The data were collected between February 2021 and June 2022 from Australian mental health professionals and students. It consists of both qualitative and quantitative components. Specifically, the data were used to develop and test a tool to assess knowledge and attitudes toward recovery.

Rental is Australia’s emerging tenure. Each year the proportion of Australians who rent increases, many of us will rent for life, and for the first time in generations there are now more renters than home owners. Though the rental sector is home to almost one-third of all Australians, researchers and policy-makers know little about conditions in this growing market because there is currently no systematic or reliable data. This project provides researchers and policy stakeholders with an essential database on Australia’s rental housing conditions. This data infrastructure will provide the knowledge base for national and international research and allow better urban, economic and social policy development. Building on The 2016 Australian Housing Conditions Dataset, in 2020 we collected data on the housing conditions of 15,000 rental households, covering all Australian states and territories. The project is funded by the Australian Research Council and The University of Adelaide, in partnership with the University of South Australia, the University of Melbourne, Swinburne University of Technology, Curtin University and Western Sydney University and is led by Professor Emma Baker at the University of Adelaide. The Australian Housing and Urban Research Institute provided funding for the focussed COVID-19 Module.

CRICOS is the official list of all Australian education providers that offer courses to people studying in Australia on student visas and the courses offered. This export is a snapshot of the CRICOS. For the latest version, you must visit the live website: https://cricos.education.gov.au/default.aspx

This dataset does not contain any resources hosted on data.gov.au. It provides a link to the location of the Australian National University Freedom of Information (FOI) disclosure log to aide in information and data discovery. You can find the FOI Disclosure log here and the Agency's Information Publication Scheme here.

The data.gov.au team is not responsible for the contents of the above linked pages.

This data comes from an online survey of 527 Australian tertiary education students completing questionnaires on faith in God and depression, anxiety and stress via meaning of life, hope and resilience.

This is the dataset for Shereen Metry's Master of Philosophy, entitled "Faith in God as a Protective Factor Against Mental Illness Among University Students Within Australia". The dataset contains raw data underpinning the manuscript.

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.

Housing serves many purposes in our society. It provides space for raising families, for leisure and rest, and increasingly, our housing doubles as a workspace. Housing also impacts our mental and physical health due to factors such as cold, mould, poorly managed maintenance issues, unaffordability, and inequality. Despite the centrality of housing in our everyday lives, we as researchers are yet to have a systematic understanding of Australian housing conditions and changes over time. Building on the earlier housing conditions projects in this series, including the Australian Housing Conditions Dataset (2016) and the Australian Rental Housing Conditions Dataset (2020), in 2022 we collected data on the housing conditions of 15,000 rental (including private and public) households and 7,500 homeowners, covering all Australian states and territories. Recognising the emerging importance of renting in Australia, the sampling was weighted to oversample rental households. This data infrastructure will provide the knowledge base for national and international research and allow better urban, economic and social policy development. The project is funded by the Australian Research Council through the Linkage Infrastructure, Equipment and Facilities (LIEF) grant program, in partnership with The University of Adelaide, the University of South Australia, the University of Melbourne, Swinburne University of Technology, Curtin University and Torrens University Australia and is led by Professor Emma Baker at the University of Adelaide.

QUT Research Data Respository Dataset and Resources

GENERATION is a new study of young Australians to understand youth transitions from school to post school destinations, including a focus on how the COVID-19 pandemic may influence decisions and pathways. GENERATION tracks the interests, hopes and aspirations of young people. A representative group of Year 10 students (around 15 years of age), from over 300 schools across Australia, participated in the first wave of the study in 2022. Two additional surveys were completed in 2023 (Wave 2) and 2024 (Wave 3). The study aims to run for a decade, concluding in 2032 when the cohort is aged 25. The GENERATION survey, is conducted in partnership between the Australian National University and the Australian Department of Education, with advice from educational units of all Australian state and territory governments and a scientific advisory group.

The learning management system (LMS) is claimed to be a crucial strategy for creating successful e-learning and teaching methods, enhancing students’ learning satisfaction and achieving academic outcomes. Although the Indonesia Open University has implemented e-learning innovation through LMS in higher education, it has not gained popularity. Hence, the present research aimed to investigate the updated DeLone and McLean information system (IS) success model on the effectiveness of LMS implementation, focusing on system quality, information quality, service quality, perceived usefulness and user satisfaction. A total of 386 respondents from undergraduate and postgraduate programs were selected through stratified random sampling. Subsequently, research data were collected through online survey questionnaires administered to students enrolled at the Indonesia Open University. Structural equation modeling with AMOS version 24 software was deployed to analyze data and test hypothesis. Findings revealed that the updated DeLone and McLean IS success model had a positive and significant influence on the effectiveness of LMS. Therefore, the top-level management of the Indonesia Open University and decision-makers in the Indonesian government could provide a better LMS practice environment infrastructure utilizing the updated DeLone and McLean IS success model.

SAIVT-Campus Dataset

Overview

The SAIVT-Campus Database is an abnormal event detection database captured on a university campus, where the abnormal events are caused by the onset of a storm. Contact Dr Simon Denman or Dr Jingxin Xu for more information.

Licensing

The SAIVT-Campus database is © 2012 QUT and is licensed under the Creative Commons Attribution-ShareAlike 3.0 Australia License.

Attribution

To attribute this database, please include the following citation: Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) Activity analysis in complicated scenes using DFT coefficients of particle trajectories. In 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2012), 18-21 September 2012, Beijing, China. available at eprints.

Acknowledging the Database in your Publications

In addition to citing our paper, we kindly request that the following text be included in an acknowledgements section at the end of your publications: We would like to thank the SAIVT Research Labs at Queensland University of Technology (QUT) for freely supplying us with the SAIVT-Campus database for our research.

Installing the SAIVT-Campus database

After downloading and unpacking the archive, you should have the following structure:

SAIVT-Campus +-- LICENCE.txt +-- README.txt +-- test_dataset.avi +-- training_dataset.avi +-- Xu2012 - Activity analysis in complicated scenes using DFT coefficients of particle trajectories.pdf

Notes

The SAIVT-Campus dataset is captured at the Queensland University of Technology, Australia.

It contains two video files from real-world surveillance footage without any actors:

training_dataset.avi (the training dataset)
test_dataset.avi (the test dataset).

This dataset contains a mixture of crowd densities and it has been used in the following paper for abnormal event detection:

Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) Activity analysis in complicated scenes using DFT coefficients of particle trajectories. In 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2012), 18-21 September 2012, Beijing, China. Available at eprints. 
This paper is also included with the database (Xu2012 - Activity analysis in complicated scenes using DFT coefficients of particle trajectories.pdf) Both video files are one hour in duration.

The normal activities include pedestrians entering or exiting the building, entering or exiting a lecture theatre (yellow door), and going to the counter at the bottom right. The abnormal events are caused by a heavy rain outside, and include people running in from the rain, people walking towards the door to exit and turning back, wearing raincoats, loitering and standing near the door and overcrowded scenes. The rain happens only in the later part of the test dataset.

As a result, we assume that the training dataset only contains the normal activities. We have manually made an annotation as below:

the training dataset does not have abnormal scenes
the test dataset separates into two parts: only normal activities occur from 00:00:00 to 00:47:16 abnormalities are present from 00:47:17 to 01:00:00. We annotate the time 00:47:17 as the start time for the abnormal events, as from this time on we have begun to observe people stop walking or turn back from walking towards the door to exit, which indicates that the rain outside the building has influenced the activities inside the building. Should you have any questions, please do not hesitate to contact Dr Jingxin Xu.

This dataset, released February 2021, contains education information relating to Preschool participation, 2018; People who left school at Year 10 or below, or did not go to school, 2016; Full-time participation in secondary school education at age 16, 2016; Participation in vocational education and training, 2019; Load Pass Rates of vocational education and training subjects, 2019; Government-funded vocational education and training subjects, 2019; School leavers enrolled in higher education, 2019. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on data from the ABS Census of Population and Housing, August 2016; the National Centre for Vocational Education Research Ltd., 2019; the ABS Estimated Resident Population, 30 June 2019; the Universities Admissions Centre (NSW & ACT), Victorian Tertiary Admissions Centre, South Australian Tertiary Admission Centre (SA & NT), Tertiary Institutions Service Centre (WA), The University of Notre Dame Australia (WA & NSW), the University of Tasmania. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

The Sea Around Us is a research initiative at The University of British Columbia (located at the Institute for the Oceans and Fisheries, formerly Fisheries Centre) that assesses the impact of fisheries on the marine ecosystems of the world, and offers mitigating solutions to a range of stakeholders.

The Sea Around Us was initiated in collaboration with The Pew Charitable Trusts in 1999, and in 2014, the Sea Around Us also began a collaboration with The Paul G. Allen Family Foundation to provide African and Asian countries with more accurate and comprehensive fisheries data.

The Sea Around Us provides data and analyses through View Data, articles in peer-reviewed journals, and other media (News). The Sea Around Us regularly update products at the scale of countries’ Exclusive Economic Zones, Large Marine Ecosystems, the High Seas and other spatial scales, and as global maps and summaries.

The Sea Around Us emphasizes catch time series starting in 1950, and related series (e.g., landed value and catch by flag state, fishing sector and catch type), and fisheries-related information on every maritime country (e.g., government subsidies, marine biodiversity). Information is also offered on sub-projects, e.g., the historic expansion of fisheries, the performance of Regional Fisheries Management Organizations, or the likely impact of climate change on fisheries.

The information and data presented on their website is freely available to any user, granted that its source is acknowledged. The Sea Around Us is aware that this information may be incomplete. Please let them know about this via the feedback options available on this website.

If you cite or display any content from the Site, or reference the Sea Around Us, the Sea Around Us – Indian Ocean, the University of British Columbia or the University of Western Australia, in any format, written or otherwise, including print or web publications, presentations, grant applications, websites, other online applications such as blogs, or other works, you must provide appropriate acknowledgement using a citation consistent with the following standard:

When referring to various datasets downloaded from the website, and/or its concept or design, or to several datasets extracted from its underlying databases, cite its architects. Example: Pauly D., Zeller D., Palomares M.L.D. (Editors), 2020. Sea Around Us Concepts, Design and Data (seaaroundus.org).

When referring to a set of values extracted for a given country, EEZ or territory, cite the most recent catch reconstruction report or paper (available on the website) for that country, EEZ or territory. Example: For the Mexican Pacific EEZ, the citation should be “Cisneros-Montemayor AM, Cisneros-Mata MA, Harper S and Pauly D (2015) Unreported marine fisheries catch in Mexico, 1950-2010. Fisheries Centre Working Paper #2015-22, University of British Columbia, Vancouver. 9 p.”, which is accessible on the EEZ page for Mexico (Pacific) on seaaroundus.org.

To help us track the use of Sea Around Us data, we would appreciate you also citing Pauly, Zeller, and Palomares (2020) as the source of the information in an appropriate part of your text;

When using data from our website that are not part of a typical catch reconstruction (e.g., catches by LME or other spatial entity, subsidies given to fisheries, the estuaries in a given country, or the surface area of a given EEZ), cite both the website and the study that generated the underlying database. Many of these can be derived from the ’methods’ texts associated with data pages on seaaroundus.org. Example: Sumaila et al. (2010) for subsides, Alder (2003) for estuaries and Claus et al. (2014) for EEZ delineations, respectively.

The Sea Around Us data are (where not otherwise regulated) under a Creative Commons Attribution Non-Commercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/). Notices regarding copyrights (© The University of British Columbia), license and disclaimer can be found under http://www.seaaroundus.org/terms-and-conditions/. References:

Alder J (2003) Putting the coast in the Sea Around Us Project. The Sea Around Us Newsletter (15): 1-2.

Cisneros-Montemayor AM, Cisneros-Mata MA, Harper S and Pauly D (2015) Unreported marine fisheries catch in Mexico, 1950-2010. Fisheries Centre Working Paper #2015-22, University of British Columbia, Vancouver. 9 p.

Pauly D, Zeller D, and Palomares M.L.D. (Editors) (2020) Sea Around Us Concepts, Design and Data (www.seaaroundus.org)

Claus S, De Hauwere N, Vanhoorne B, Deckers P, Souza Dias F, Hernandez F and Mees J (2014) Marine Regions: Towards a global standard for georeferenced marine names and boundaries. Marine Geodesy 37(2): 99-125.

Sumaila UR, Khan A, Dyck A, Watson R, Munro R, Tydemers P and Pauly D (2010) A bottom-up re-estimation of global fisheries subsidies. Journal of Bioeconomics 12: 201-225.

This dataset presents regional surface hydrological in-land waterbodies in Australia as polygons which describe the area of the body of water at a higher resolution than that of the national dataset. The following features are included in this dataset:Canal Area - An artificial open channel which provides the supply, distribution or removal of water for irrigation purposes, or for a significant infrastructure function (such as salt interception, land reclamation, or drainage between water features for environmental management purposes). Estuary - The part of the mouth or lower course of a river in which its current meets the sea's tides, and is subject to their effects. Swimming Pool - An artificial body of water intended for swimming or water-based recreation, situated either above or in-ground. Flood Irrigation Storage - A body of water collected and stored behind constructed barriers, for the specific use of flooding pastures via internal irrigation systems. Town Water Storage - A body of water collected and stored behind a constructed barrier for some specific use (with the exception of Flood Irrigation Storage). Town Water Storage are bodies of water primarily stored for the consumption of urban, semi urban and rural township populations. The water is treated post storage by government, or private authorities, and connected to government regulated water networks. Rural Water Storage - A body of water collected and stored behind a constructed barrier for some specific use. Rural Water Storage are bodies of water stored for rural farming and agricultural practices (with the exception of Flood Irrigation Storage) and/or for the consumption of the associated land owners. The water is not treated by government authorities or connected to government regulated water networks. Lake - A naturally occurring body of mainly static water surrounded by land. Watercourse Area - A natural channel along which water may flow from time to time. Aquaculture Area - Shallow beds, usually segmented by constructed walls, for the use of aquaculture. Salt Evaporator - A flat area, usually segmented, used for the commercial production of salt by evaporation. Settling Pond - Shallow beds, usually segmented by constructed walls, for the treatment of sewage or other wastes. Swamp - Land which is so saturated with water that it is not suitable for agricultural or pastoral use and presents a barrier to free passage. Marine Swamp - That low lying part of the backshore area of tidal waters, usually immediately behind saline coastal flat, which maintains a high salt water content, and is covered with characteristic thick grasses and reed growths. Foreshore Flat - That part of the seabed or estuarine areas, between mean high water and the line of lowest astronomical tide. Saline Coastal Flat - That nearly level tract of land between mean high water and the line of the highest astronomical tide.Land Subject to Inundation - Low lying land usually adjacent to lakes or watercourses, which is regularly covered with flood water for short periods. For more information please visit the Geoscience Australia Web Service Portal.

Abstract copyright UK Data Service and data collection copyright owner. The surveys were commissioned by the Committee of Inquiry into Education and Training, set up by the Australian Government, in order to gather information from students and staff in all sectors of Australian post-secondary education. Areas covered include characteristics of students and staff, career choice, preparation and planning and attitudes towards issues of importance to post-secondary education in the seventies. Main Topics: Attitudinal/Behavioural Variables A. National Educational Survey, 1977 Date left secondary school, type and location, qualifications attained, whether specialised in any subjects, relative standard achieved (e.g. above/below average). Whether intended to enter university/college or follow a specific career. Whether entered a university/college, qualifications obtained. Whether worked full-time for more than six months. Whether currently enrolled in a university or college and details of courses, whether currently in employment and details. Whether recently changed or given up any courses - if so, reasons, future plans if not currently enrolled. Attributes of a typical teacher at respondent's college/university (e.g. inspires confidence, displays enthusiasm), whether agrees/disagrees with several statements concerning courses, reasons for current enrolment, whether present course and institution was first preference, overall evaluation of course and institution, relative standard achieved, whether committed to work for a particular employer when graduated, expected ease of obtaining a job, expected and preferred occupation. Time of choosing career, whether choice was restricted by subjects taken at secondary school, highest qualification would like to acquire, perceived differences between Universities, Colleges of Advanced Education and Technical Colleges. General comments were elicited concerning education and training and the effect of financial factors on career development. Background Variables Sex, age, country of birth of self and parents, no. of years lived in Australia, religion, marital status, whether has children, sources of financial support, comparison of own income (current and expected) and parents' income with the average, parents' occupations and highest level of education. B. National Survey of Post-Secondary Teaching Staff, 1977 Separate questionnaires were sent to the three sectors of tertiary education. Questions asked in each include: Position, field of teaching, length of time in university/college teaching/at present institution, positions/ qualifications held, publications, whether currently enrolled for a degree, details of teaching responsibilities, breakdown of activities each week. Attitude towards certain activities (e.g. research, teaching, administration), assessment of the goals of higher education, opinion of ability of incoming and whether affected by expansion in post-secondary education. Expected ease with which students will obtain jobs. Opinion of courses taught and student participation in decision-making, main reasons for students giving up courses, opinion of size of institution. Attributes of a typical teacher at own institution, ways in which university/college education could be improved, whether numbers in post-secondary education/in own discipline should continue to expand and by how much, whether admission standards should be relaxed/tightened, whether student transfers between institutions should be made easier/more difficult. Perceived differences between Universities, Colleges of Advanced Education and Technical Colleges, opinion of plans to amalgamate Universities and Colleges. Areas in which expenditure cutbacks should/should not fall. Expected date of leaving current institution, whether would support early retirement schemes, overall job satisfaction, whether would accept another position elsewhere, at what salary and for what reasons. General comments regarding education and training. Background Variables Age, sex, country of birth and of first degree, length of time lived in Australia, educational and occupational background of parents.

This dataset is designed to explore multistreaming social media video as a research method used to collect semi-structured interview data. The data are provided by Dr Karen E. Sutherland and Ms Krisztina Morris from the School of Business and Creative Industries at the University of the Sunshine Coast in Queensland, Australia. The dataset is drawn from the publicly available video recording of an interview undertaken as part of the research project called: ‘Like, Share, Follow’, a multistreaming show, featuring Dr Sutherland interviewing university graduates about their career journeys, that is broadcast across Facebook, LinkedIn, and Twitter and later uploaded to YouTube. This dataset examines how multistreaming video interview data can be used to answer research questions and the benefits and challenges this specific method of data collection can pose in the process of data analysis. The video example is accompanied by a teaching guide and a student guide.

Using an anonymous, online survey students provided demographic data and self-reported their stress, anxiety, resilience, coping strategies, mental health and exposure to COVID-19. Students' stress, anxiety, resilience, coping strategies and mental health were assessed using the Impact of Event Scale-Revised, the Coronavirus Anxiety Scale, the Brief Resilience Scale, the Brief Cope and the DASS21. Descriptive and regression analyses were conducted to investigate whether stress, anxiety, resilience and coping strategies explained variance in mental health impact. Ethical Approval was obtained from the University of New England Human Research Ethics Committee (No: HE20-188). All participating universities obtained reciprocal approval.