COVID-19: Public health, and societal and psychological impacts datasets
Description of this collection
We selected Public health, and societal and psychological impacts datasets indexed by the Mendeley Data Search engine on the 2019-present COVID-19 / Coronavirus pandemic. The aim was to make it easier to find potentially relevant datasets for this specific topic
Published: 13 Apr 2020
Psychology, Sociology, Public Health, Coronavirus, Coronavirus Disease 2019
This survey aimed to evaluate the cyber-security culture readiness of organizations from different countries and business domains when teleworking became a necessity due to the COVID-19 crisis. A targeted questionnaire was designed and a web-based survey was conducted addressing employees while working from home during the COVID-19 spread over the globe. The questionnaire contained no more than 23 questions and was available for almost a month, from 7th April 2020 until 3rd May 2020. During that period, 264 participants from 13 European countries spent approximately 8 minutes to answer it. Gathered data are being hosted in this dataset along with their analysis and graphical representation.
The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 27, 2020. Note that this version uses 20-40-60-80-day time windows and the first test data are based on the first country reports on tests.
Please cite as:
• Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881.
• Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.
• Country data (country_data.txt): Country data.
• Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators.
• Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators.
• Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset.
Raw data of figures and tables:
• Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2.
• Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3.
• Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1.
• Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2.
• Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.
This supplementary material presents questionnaires, variables, and raw data about family demographics, positive and negative emotions, and family resilience during COVID-19 isolation among Indonesian families. A survey was conducted to measure the family’s positive or negative emotional reactions and the degree of their resilience. The data were categorized into age, sex, type of family, family size, length of marriage, family’s environment, and Family COVID-19 status. The samples were gathered from 365 parents of Indonesian students who were willing to fill an online questionnaire. Further, the data analysis involved demographic sample, descriptive statistics, median test, Kruskal-Wallis, and an inter-variable correlation.
One of the sectors that felt the impact of the Corona Virus Disease 2019 (COVID-19) pandemic was the educational sector. The outbreak led to the immediate closure of schools at all levels thereby sending billions of students away from their various institutions of learning. However, the shut down of academic institutions was not a total one as some institutions that were solely running online programmes were not affected. Those who were running face to face and online modes quickly switched over to the online mode. Unfortunately, institutions that have not fully embraced online mode of study were greatly affected. 85% of academic institutions in Nigeria are operating face to face mode of study, therefore, majority of Nigerian students at all levels were affected by the COVID-19 lockdown.
Social media platforms and emerging technologies were the major backbones of institutions that are running online mode of study, therefore, this survey uses the unified theory of acceptance and use of technology (UTAUT) model to capture selected Face to face Nigerian University students accessibility, usage, intention and willingness to use these social media platforms and emerging technologies for learning. The challenges that could mar the usage of these technologies were also revealed. Eight hundred and fifty undergraduate students participated in the survey.
The dataset includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file and the descriptive statistics for all the variables captured.
This second version contains the reliability statistics of the UTAUT variables using Cronbach's alpha. This measured the reliability as well as the internal consistency of the UTAUT variables. This was measured in terms of the reliability statistics, inter-item correlation matrix and item-total statistics.
Authors believed that the dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies.
In this version 3, the SPSS file in SAV format was made available in csv format. Also, the .rar file was made opened to make data accessibility easier.
Contributors:David De Coninck, Leen d'Haenens, Koen Matthijs
During the COVID-19 pandemic, people have become increasingly fearful of the disease as death tolls rise, while governments attempt to combat it by installing restrictive measures. News media play a vital role as they are the main sources from which people gather information regarding the disease and the public health measures. The present longitudinal data reflect a bird’s eye view of people’s fears towards getting ill, their news media consumption, and their attitudes regarding the (Belgian) government’s handling of the COVID-19 crisis. Data were collected at three key moments in the pandemic among adults in Flanders, Belgium: in the middle of March (when the first restrictive measures went into effect; N = 1,000), early April (as hospital admissions and death toll peaked; N = 870), and at the end of May and beginning of June (as several measures were lifted or relaxed; N = 768). With only 23.2% drop-out across the three waves, these data may be of interest to researchers who wish to explore dynamics of fear and attitudes towards public health measures during this particularly challenging time.
This data provides information about both daily case COVID-19 case and Google RSV (Relative Search Volume) in Indonesia between January 21 - April 5. In this data we found that Google RSV for search term of category coronavirus, disease prevention, nonpharmaceutical intervention, and personal protective equiptment is strongly significant toward COVID-19 case in Indonesia (p0.7; p<0.05). Therefore, Google Trend is able to recognize public interest toward COVID-19 in order to reduce the transmission.
Currently, due to the COVID-19 pandemic, serious games are an excellent learning resource with benefits for students to learn in a fun way. We present a set of data related to the literature review on accessibility in serious games. We apply PRISMA, the results revealed that not all serious games use the standards defined by WCAG 2.1 and 2.2 specific to a video game.