Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

This dataset provides summary statistics for case-detection and revenue generated from a TB social-enterprise model in Pakistan. This data was generated by collating statistics from the project partners HMIS that is available online. Summary statistics were complied using MS Excel.

This dataset supports a research project in the field of digital medicine, which aims to quantify the impact of disseminating scientific information on social media—as a form of "meta-intervention"—on public adherence to Non-Pharmaceutical Interventions (NPIs) during health crises such as the COVID-19 pandemic. The research encompasses multiple sub-studies and pilot experiments, drawing data from various global and China-specific social media platforms.The data included in this submission has been collected from several sources:From Sina Weibo and Tencent WeChat, 189 online poll datasets were collected, involving a total of 1,391,706 participants. These participants are users of Sina Weibo or Tencent WeChat.From Twitter, 187 tweets published by scientists (verified with a blue checkmark) related to COVID-19 were collected.From Xiaohongshu and Bilibili, textual content from 143 user posts/videos concerning COVID-19, along with associated user comments and specific user responses to a question, were gathered.It is important to note that while the broader research project also utilized a 3TB Reddit corpus hosted on Academic Torrents (academictorrents.com), this specific Reddit dataset is publicly available directly from Academic Torrents and is not included in this particular DataHub submission. The submitted dataset comprises publicly available data, formatted as Excel files (.xlsx), and includes the following:Filename: scientists' discourse (source from screenshot of tweets)Description: This file contains screenshots of tweets published by scientists on Twitter concerning COVID-19 research, its current status, and related topics. It also includes a coded analysis of the textual content from these tweets. Specific details regarding the coding scheme can be found in the readme.txt file.Filename: The links of online polls (Weibo & WeChat)Description: This data file includes information from online polls conducted on Weibo and WeChat after December 7, 2022. These polls, often initiated by verified users (who may or may not be science popularizers), aimed to track the self-reported proportion of participants testing positive for COVID-19 (via PCR or rapid antigen test) or remaining negative, particularly during periods of rapid Omicron infection spread. The file contains links to the original polls, links to the social media accounts that published these polls, and relevant metadata about both the poll-creating accounts and the online polls themselves.Filename: Online posts & comments (From Xiaohongshu & Bilibili)Description: This file contains textual content from COVID-19 related posts and videos published by users on the Xiaohongshu and Bilibili platforms. It also includes user-generated comments reacting to these posts/videos, as well as user responses to a specific question posed within the context of the original content.Key Features of this Dataset:Data Type: Mixed, including textual data, screenshots of social media posts, web links to original sources, and coded metadata.Source Platforms: Twitter (global), Weibo/WeChat (primarily China), Xiaohongshu (China), and Bilibili (video-sharing platform, primarily China).Use Case: This dataset is intended for the analysis of public discourse, the dissemination of scientific information, and user engagement patterns across different cultural contexts and social media platforms, particularly in relation to public health information.

Survey file 1 and 2 Survey file 1 (before/first survey) and Survey file 2 (after/second survey) through online survey software Qualtrics XM. Online Qualtrics Survey file 1 (before/first survey) - CSV format to be opened in Microsoft Excel. Online Qualtrics Survey file 2 (after/second survey) - CSV format to be opened in Microsoft Excel. Survey data have been anonymized by removing personal and sensitive data (see MetaData file) and data from the survey files have been merged into one SPSS file for analysis purposes.

Semi-structured Interviews Interviews were recorded through MS Teams (with a Dictaphone for backup recording) and transcribed by an external company (Just Delegate) in Microsoft Word. Interview data have been anonymized by removing name, location and profession. Data has not been processed for analytical purposes. Interview transcripts to be opened in Microsoft Word.

Platform analytics Platform analytics were captured using MixPanel software. Platform analytics have been anonymized by removing email address and region. Data has been transferred into SPSS to generate descriptive statistics.Platform analytics - CSV format to be opened in Microsoft Excel.

With the introduction of accounting standards AASB 2010-2 and AASB 1053 in 2010, some Australian Small and Medium-sized Entities (SMEs) are allowed reduced disclosure for general purpose Annual Financial statements.

A 2010-2011 Institute of Chartered Accountants in Australia Research Grant titled "Standards setting for Australian Small and Medium-Sized Entities (SMEs): What are user requirements and how were users involved in the creation of the differential reporting standard?" funded Dr Sue Wright, Dr Elaine Evans and PhD candidate Karen Handley, all of the Department of Accounting and Corporate Governance in the Faculty of Business and Economics at Macquarie University, to investigate the needs of consumers of SME Annual Financial Reports, and whether these consumers were currently using what was being reported. The questions were based on a previous European study.

A number of consumers were sourced through the professional Accounting bodies of Australia and over the period from December 2010 through March 2011, around 200 of these were surveyed anonymously through an online survey made available through the "Survey Monkey" online survey tool. The survey included questions asking respondents to identify what aspects of the SME Financial reports were important to them, and why they wanted this information. Results were exported from Survey Monkey into an Excel spreadsheet, and descriptive statistics were added.

This collection includes the Survey Questions, an Excel spreadsheet file containing Respondents' survey results and descriptive statistics, and two publications reporting the results of the research:

1. A report generated in response to an Australian government national enquiry

2. An article in an Australian accounting practitioners journal:

Handley. K (2010), Reporting for SMEs, The Charter (Magazine of the Institute of Chartered Accountants in Australia), Vol. 81 No. 7, August, pp. 68-69.

Restrictions:

- The survey may be released under an open licence with Attribution/Credit required

- The Excel spreadsheet cannot be released due to the terms of the Macquarie University Ethics application, but the original CIs can analyse the data in response to specific research questions posed to them, and report on the results.

- The National Enquiry report terms of release are unknown

- The article is available via subscription to "The Charter"

Please note:

This collection relates to research undertaken by Karen Handley as part of her PhD candidature in the Department of Accounting and Corporate Governance in the Faculty of Business and Economics at Macquarie University, and was conducted under the supervision of Dr Elaine Evans and Dr Sue Wright. The PhD thesis is scheduled for submission in early 2013 under the title "" Accounting standards for Australian SMEs: Identifying, considering and incorporating the needs of users into financial statements".

Background: Coronavirus disease 2019 (COVID-19) is a global health threat and caused a universal psychosocial impact on the general population. Therefore, the knowledge, attitude, and perceptions (KAPs) of the general population are critical for the development and effective implementation of standard operating procedures (SOP) to contain the contagion and minimize the losses. Therefore, the current study was conducted to understand and evaluate the KAPs of Pakistani populations toward the COVID-19.Methods: An online cross-sectional study was carried out among participants from 1 May to 30 July 2020 in different areas of Pakistan. The respondents of the study were the general population with age ≥ 18 years. The poll URL was posted on several channels after a call for participation. Other social media platforms such as WeChat, WhatsApp, Facebook, Twitter, Instagram, Messenger, and LinkedIn were engaged to maximize general population engagement. The questionnaire included details about sociodemographic, knowledge about COVID-19, perceptions toward universal safety precautions of COVID-19, and beliefs attitude toward the COVID-19. The obtained data were exported into a Microsoft Excel spreadsheet and SPSS software version 21 for windows. The descriptive statistics values were presented in frequencies and percentages. Binary logistic regression, Chi-square test, and one-way ANOVA were applied to analyze the participants' socio-demographic characteristics and variables related to KAPs. P-value < 0.05 was recorded as significant.Results: A total of 1,000 participants were invited of which 734 participated in this study. The response rate was 73.4% (734/1,000). The gender, marital status, education, and residence showed a significant association with the knowledge score. The majority of the study participants were thinking that COVID-19 may be more dangerous in elderly individuals 94.5% (n = 700), and individuals with chronic diseases or severe complications 96.7% (n = 710) (p = 0.00). More than half of the participants 52.5% (n = 385) showed their concern that either they or their family members might get the infection. More than 98% (n = 703), (P-value = 0.00) of the participants held that COVID-19 would be successfully controlled in Pakistan by following the standard SOPs and government guidelines.Conclusion: This study showed that the general population of Pakistan has good awareness and reasonable attitudes and perceptions toward the full features of the COVID-19. The current study suggests that mass-level effective health education programs are necessary for developing countries to improve and limit the gap between KAP toward COVID-19.

Description and PurposeThese data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2022):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethodsThe survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and LimitationsThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. This data is the weighted data provided by the ETC Institute, which is used in the final published PDF report.The 2022 Annual Community Survey report is available on data.tempe.gov. The individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Wydale HolmesContact E-Mail (author): wydale_holmes@tempe.govContact (maintainer): Wydale HolmesContact E-Mail (maintainer): wydale_holmes@tempe.govData Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

The Department of Health published quarterly and annual summary reports online. The Department of Human Services provides an interactive online tool that allows for searching on services provided …Show full descriptionThe Department of Health published quarterly and annual summary reports online. The Department of Human Services provides an interactive online tool that allows for searching on services provided and benefits paid for items in the MBS. Data is available for research purposes at detailed aggregate and unit record levels under terms and conditions that ensure compliance with the relevant legislation under which the collection has been made. A range of aggregate data has been made available on the Internet for public use. Excel spreadsheets of quarterly and annual summary reports covering: Number of services and bulk billing services; Benefit paid and fee charged Schedule fee and schedule fee observance; and patient contribution for out-of-hospital and patient billed services Interactive online searching on the Internet that returns services and benefits for items and groups in the MBS. These can be viewed by State, patient gender, age group and over time (e.g. by month).

Cleaned raw data in Excel csv collected on consumer perceptions and behavior towards fast food businesses in Zambia. The dataset includes a closed ended questionnaire with four sections enough to answer all the 3 research questions. The questionnaire was distributed in such a way that it included all the sections of the 2 cities of Lusaka and Kitwe in order to have a very representative sample of the population in the urban parts of the 2 cities where fast food businesses are established.

The data gathered using a an online Google questionnaire had likert scale items that answered three main questions on perception, behavior and purchase intentions of fast foods with an overall objective of providing a descriptive analysis of the consumer perceptions, behaviors and intention to purchase fast-foods in Zambia. Needless to state that the dataset also captured some socio-economic and demographic characteristics of consumers with codes such as 1 for males and 2 for females.

Apart from the demographics section, all the other 3 sections of the questionnaire had likert scale levels that captured levels for 1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree. Factor analysis was then conducted using SPSS IBM version 23 in order to have a deep understanding of the role each item captured in the questionnaire played. Reliability and consistency of the data was done using Cronbach Alpha which had a significant value of 0.7.

One of the notable findings was the fact that consumption of fast food increased by 54%, post Covid-19 pandemic. This simply means that there has been an increase in the demand for fast food in Zambia after the end of Covid-19. It is therefore, viable to increase investment in fast food businesses in Zambia in order to meet up with the increased demand for fast food.

Lastly but not the least, the dataset can be used to conduct a descriptive analysis study of the 2 major cities of Zambia (Kitwe and Lusaka) in the fast food industry. There is also potential to expand the study in order to understand more about the consumer behavior and perceptions.

Introduction Little is known about the consequences of the COVID-19 pandemic on the life of university students in Sub Saharan Africa (SSA). The objective of this study was to evaluate the socioeconomic and academic consequences of the COVID-19 pandemic on medical students studying at the University of Rwanda. Methods This was a cross-sectional study. An online survey using google form was sent to medical students in clinical training (year 3 till year 5) using convenience sampling followed by snowball sampling method. We collected data on participants’ demographics, general knowledge on the COVID-19 pandemic and perception on mitigation measures, and socio-economic and academic consequences of the COVID-19 pandemic. Descriptive statistics were used in excel 2015 software to calculate participants’ responses and categorical data were presented using frequencies and percentages. Results A total 187 participants completed the survey. Most participants described disruption in routine activities (72.7%), reduced travelling (69%), church closing (64.2%), and loss of freedom (57.2%) as examples of negative social consequences. While financial uncertainty (64.7%), decrease in income (49.7%), and increase in poverty rate (42.2%) were the main economic consequences. Issues with academic progress (95.7%), limited social life (56.1%), and repeating the year (42.8%) were examples of negative academic consequences. Conclusion The results of this study suggest that the COVID-19 had a negative social, economic, and academic consequences on medical students at the University of Rwanda. These finding may guide the design of interventions to mitigate the consequences of COVID-19 and to protect medical students against future pandemics and crises.

We used the standard and validated Pittsburgh Sleep Quality Index (PSQI), which was developed by researchers at the University of Pittsburgh in 1988 AD. The questionnaire included baseline variables like age, sex, academic year, and questions addressing participants’ sleep habits and quality i.e. PSQI. The PSQI assesses the sleep quality during the previous month and contains 19 self-rated questions that yield seven components: subjective sleep quality sleep, latency, sleep duration, sleep efficiency and sleep disturbance, and daytime dysfunction. Each component is to be assigned a scored that ranges from zero to three, yielding a PSQI score in a range that goes from 0 to 21. A total score of 0 to 4 is considered as normal sleep quality; whereas, scores greater than 4 are categorized as poor sleep quality.Data collected from students through the Google forms were extracted to Google sheets, cleaned in Excel, and then imported and analyzed using STATA 15. Simple descriptive analysis was performed to see the response for every PSQI variable. Then calculation performed following PSQI form administration instructions.

Data Source Type: ExcelPreparation Method: Pdf reports reviewed online and data entered into Excel

The Australian Public Service Statistical Bulletin 2011-12 presents a summary of employment under the Public Service Act 1999 at 30 June 2012 and during the 2011-12 financial year, as well as summary data for the past 15 years. This Excel dataset consists of tables used to create the Statistical Bulletin. You can view the Bulletin online at "http://www.apsc.gov.au/about-the-apsc/parliamentary/aps-%0Astatistical-bulletin/2011-12">http://www.apsc.gov.au/about- the-apsc/parliamentary/aps-statistical- bulletin/2011-12

Introduction Little is known about the consequences of the COVID-19 pandemic on the life of university students in Sub Saharan Africa (SSA). The objective of this study was to evaluate the socioeconomic and academic consequences of the COVID-19 pandemic on medical students studying at the University of Rwanda. Methods This was a cross-sectional study. An online survey using google form was sent to medical students in clinical training (year 3 till year 5) using convenience sampling followed by snowball sampling method. We collected data on participants’ demographics, general knowledge on the COVID-19 pandemic and perception on mitigation measures, and socio-economic and academic consequences of the COVID-19 pandemic. Descriptive statistics were used in excel 2015 software to calculate participants’ responses and categorical data were presented using frequencies and percentages. Results A total 187 participants completed the survey. Most participants described disruption in routine activities (72.7%), reduced travelling (69%), church closing (64.2%), and loss of freedom (57.2%) as examples of negative social consequences. While financial uncertainty (64.7%), decrease in income (49.7%), and increase in poverty rate (42.2%) were the main economic consequences. Issues with academic progress (95.7%), limited social life (56.1%), and repeating the year (42.8%) were examples of negative academic consequences. Conclusion The results of this study suggest that the COVID-19 had a negative social, economic, and academic consequences on medical students at the University of Rwanda. These finding may guide the design of interventions to mitigate the consequences of COVID-19 and to protect medical students against future pandemics and crises.

The Australian Public Service Statistical Bulletin 2014-15 presents a summary of employment under the Public Service Act 1999 at 30 June 2015 and during the 2014-15 financial year, as well as summary data for the past 15 years. This Excel dataset consists of tables used to create the Statistical Bulletin. You can view the Bulletin online at http://www.apsc.gov.au/about-the-apsc/parliamentary/aps-statistical-bulletin/statistics-2015

CONTEXT: Basic Life Support is an essential skill which should be mastered by individuals related to healthcare delivery system. Adequate training is necessary during their study period as several instances have been reported regarding cardiac arrest during the dental treatment and it is important that the dental practitioners are well versed with BLS. AIM: The present study was designed to assess the awareness and knowledge about basic life support amongst postgraduate students of dentistry in India. SETTINGS AND DESIGN: This was a descriptive questionnaire study carried out amongst 270 postgraduate (MDS degree) students of various dental colleges across India using online platform( Google forms). METHODS& MATERIALS: A questionnaire was designed based on American Heart Association (AHA) guidelines. It consisted of 15 questions regarding awareness and knowledge about BLS. The questionnaire included questions regarding abbreviations, sequences, skills, diagnosis and devices used. STATISTICAL ANALYSIS: Data obtained was tabulated using Microsoft Excel. Descriptive analysis was computed RESULT: 270 postgraduate students from various departments of dentistry participated in this study. 1.8% of the total students participated answered all 15 questions correctly. 16% of the students who participated in the study scored above seventy-five percentage, 68%students scored between fifty to seventy-five percentage and 14.8%students scored less than fifty percentage. CONCLUSION: Awareness and knowledge about BLS need to be improved among the dental postgraduate students. Hence, a structured BLS training program should be part of the curriculum.