A joint dataset of official COVID-19 reports and the governance, trade and competitiveness indicators of World Bank group platforms

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 (European Centre for Disease Prevention and Control, 2020 [1] and Beltekian et al. [2]), as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360(World Bank Group, 2020 [3]) and TCdata360(World Bank Group, 2020 [4]) platforms. From these platforms, only annual indicators from 2015 and later were collected, and their missing values were replaced with previous annual values, in descending order by year, until 2015. During preprocessing, indicators (columns) were filtered out when the ratio of missing values exceeded 50%. Then, the same filtration was applied for the ratio of missing values above 25% in the case of countries (rows). Finally, duplicated variables were removed from the dataset. As a result of these steps, the missing value rate of the employed indicators was reduced to 4.25% on average. In addition to the database, the Kendall rank correlation matrix is provided to facilitate subsequent analysis. The dataset and the correlation matrix can be updated and customized with an R Notebook file, which is also available publicly in Mendeley Data (Kurbucz, 2020 [5]).


a b s t r a c t
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 (European Centre for Disease Prevention and Control, 2020 [1] and Beltekian et al. [2]), as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 (World Bank Group, 2020 [3]) and TCdata360 (World Bank Group, 2020 [4]) platforms. From these platforms, only annual indicators from 2015 and later were collected, and their missing values were replaced with previous annual values, in descending order by year, until 2015. During preprocessing, indicators (columns) were filtered out when the ratio of missing values exceeded 50%. Then, the same filtration was applied for the ratio of missing values above 25% in the case of countries (rows). Finally, duplicated variables were removed from the dataset. As a result of these steps, the missing value rate of the employed indicators was reduced to 4.25% on average. In addition to the database, the Kendall rank correlation matrix is provided to facilitate subsequent analysis. The dataset and the correlation matrix can be updated and customized with an R Notebook file, which is also available publicly in Mendeley Data (Kurbucz, 2020 [5] ). driven and discipline-specific research. The preprocessed indicators of World Bank Group platforms can be used separately in various research fields (see, e.g., [ 7 , 8 ]).
• The Kendall rank correlation matrix is also provided to facilitate an in-depth analysis of the data.

Data description
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 [ 1 , 2 ], as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 [3] and TCdata360 [4] platforms. Besides, the Kendall rank correlation matrix is provided to facilitate subsequent analysis. These datasets are complemented by the metadata of selected GovData360 and TCdata360 indicators, as well as country data that includes geographic coordinates, making it easier to visualize the results of subsequent analyses. These datasets can be generated in a contemporary form using the provided R Notebook. The current version was compiled on May 25, 2020. The complete list of uploaded files (including the raw data of figures and tables) is as follows. Datasets: a

R Notebook:
• 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.

Raw data of figures and tables:
• Raw data of Fig. 2 Table 3 . Fig. 1 illustrates the relationships between the R Notebook and datasets listed above. A detailed description of the extracted variables, their origin, the ratio of their missing values, and the ID of their datasets are shown in Table 1 . Table 2 summarizes the generation process of these variables. Table 3 , Figs. 2 , and 3 relate to the Kendall rank correlation matrix. Table 3 includes the correlations between COVID-19 variables. Fig. 2 compares the connection of each COVID-19 variable with different governance, trade, and competitiveness indicators using table plots. Finally, Fig. 3 presents one of the many relationships contained by the correlation matrix that require further analysis. It illustrates the correlation between the air transport indicators of the Global Competitiveness Index (GCI) and the variable for the number of days since the first COVID-19 case.

Experimental design, materials and methods
To obtain the GovData360 and TCdata360 indicators, the data360r (version: 1.0.8) R package [6] was used. Only annual indicators from 2015 and later were collected, and their missing values were replaced with previous annual values, in descending order by year, until 2015. During The IDs of indicators obtained from TCdata360 . * * 5.22% [ 4 , 6 ] c, d * These variables were generated by the author. Note that if the given number of days has not yet elapsed since the specified event, the value is missing. The R Notebook is used to update the dataset. * * The complete list of GovData360 and TCdata360 indicators is contained by the metadata. For these variables, the averages of the ratio of missing values are indicated.

Table 2
The steps of the data generation. Step Description Remark Collecting COVID-19 variables 6 Generating new COVID-19 variables 7 Compiling and preprocessing the joint dataset 8 Compiling the correlation matrix Kendall τ b correlation matrix is calculated.

9
Compiling the country dataset and metadata 10 Writing datasets into TSV files New files have the same name as uploaded ones. * The data generation process can be customized with these parameters. lastyr marks the last year whose values were still taken into account when indicators were collected from the GovData360 and TCdata360 platforms and their missing values were replaced. During preprocessing, we filtered out those indicators for which the missing value ratio exceeds cmaxmissing . Then, the same filtration was applied above rmaxmissing in the case of countries. preprocessing, indicators (columns) were filtered out when the ratio of missing values exceeded 50%. Then, the same filtration was applied for the ratio of missing values above 25% in the case of countries (rows). Finally, these data were connected with 18 COVID-19 variables. The Kendall rank correlation matrix was calculated using the preprocessed dataset and the cor function of the stats (version: 3.5.3) R package [9] . Before this calculation, COVID-19 variables (except for dyssincefstcase , dyssincefstdeath , and dyssincefsttest ) were divided by the population of the respective countries, and the use argument of the cor function was set up to pairwise.complete.obs (for more information, see [10] ). A detailed description of the extracted variables, their origin, the ratio of their missing values, and the ID of their datasets (see Fig. 1 ) are shown in Table 1 .

Data generation
Datasets were generated in R. The process of data generation is summarized in Table 2 . Table 3 Kendall rank correlation between COVID-19 variables.

Correlation matrix
In this subsection, the relationships between the variables are presented by using the Kendall rank correlation matrix. Table 3 contains the correlation matrix of COVID-19 variables. To compare the relationship of each COVID-19 variable with different governance, trade, and competitiveness indicators, the tabplot (version: 1.3-4) R package [11] is used. Tabplot allows the exploration and analysis of large multivariate datasets with table plots. In our case, each column of this plot represents a COVID-19 variable, and each row represents a bin containing 100 indicators from GovData360 and TCdata360 platforms. Bars show the mean and the standard deviation of the correlations between the given COVID-19 variable and indicators contained in the bins. COVID-19 variables of cases, deaths, and tests are illustrated in different subplots. The last bar of these subplots displays the ratio of the GovData360 and TCdata360 indicators for each bin. For easier comparison, the correlation matrix is arranged in descending order of the first variable of the subplots (see Fig. 2 ).
The complete correlation matrix contains many relationships that require further analysis. Fig. 3 illustrates such a relationship between the air transport indicators of the Global Competitiveness Index (GCI) and the variable for the number of days since the first COVID-19 case.

Declaration of Competing Interest
The author declares that he has no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.