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In a digital economy, data becomes the centerpiece of economic activity and the most valuable resource. Achievements of scientific-technological progress today are so outstanding that they allow data mining based on artificial intelligence (AI) with the use of Big Data processing technologies. The Internet of Things (IoT), Ubiquitous Computing (UC) and other breakthrough technologies in Industry 4.0 allow keeping a complete record of economic activity, ensuring its transparency, controllability and forecastability.

However, a severe barrier on the way to optimization of the information management is its inconsistency and fragmentation. Through advanced technological capabilities, some international organizations create datasets to simplify their management of statistics that is presented to them. The dataset of the International Monetary Fund can be cited as the most successful example (IMF), as it breaks up the countries into economic and geographic categories, which allows them selecting any indicators from the proposed list and contains the forecast data for the period until 2022.

In 2018-2019 The annual dataset (Report Reader) was released by the World Economic Forum (WEF). This dataset had limited capacities – it allowed sorting data either by countries or by indicators; the list of indicators was available in a pop-up window and was not immediately visible, which made the management of the dataset more complex. The World Bank (WB) offers data for a wide range of fields, but the only way to optimize the data management it offers is to import it into Microsoft Excel. Apart from that, the data of the World Bank are unstructured; even combining samples in Microsoft Excel is difficult due to the fact that the number of countries for which the data are presented is different in each sample.

In the current Russian statistics, for example, Federal Service of State Statistics (Rosstat) (GKS) and the Scientific Research Institute “Higher School of Economics” (SRI HSE: https://www.hse.ru/primarydata/ice2019kr) most of the data is not presented in digitalized form (they are presented in the form of pictures that cannot be edited); moreover, changes are made annually in the composition and the names of statistically identifiable indicators. This fact significantly complicates the analysis of the time series (studying the dynamics of economic phenomena and processes) or even makes it completely unfeasible.

Thus, datasets are highly relevant for the contemporary science, since they form the backbone of highly-efficient empirical research, and the lack of datasets complicate and hampers applied researches, resulting in the prevailing development of theoretical science and the inability to identify new problems and significant consistent patterns for their solution. International organizations recognize the growing need of contemporary science for datasets, but some of their small-scale initiatives fail to solve this problem.

The Institute for Scientific Communication (ISC) contributes to solving the problem raised and creates the first dataset in Russian, combining the leading world statistics. The ISC dataset generates Big Data of the modern global economy and constitutes a digital platform for data mining.

The dataset contains indicators for the most relevant lines of economic research:

•  Industry 4.0:

•  Digital Competitiveness Index (IMD)(2019-2021);

•  Readiness for frontier technologies index (UNCTAD) (2021);

•  Knowledge Economy:

•  Global Innovation Index (WIPO)(2019-2021);

•  Knowledge index (UNDP) (2020-2021);

•  Economic growth

•  GDP (IMF)(2019-2021);

•  Rate of economic growth (IMF)(2019-2021);

•  GDP per capita (IMF) (2019-2021);

•  Sustainable development:

•  Quality of Life Index (Numbeo) (2019-2022);

•  Happiness Index (UN) (2019-2020);

•  Sustainable Development Index (Sustainable Development Solutions Network)(2019-2022).

Advantages of the ISC dataset:

•  Reliability: the dataset combines statistics of relevant international organizations, in particular, IMD, WEF, WIPO, WB, Numbeo, New Economic Foundation и Sustainable Development Solutions Network, UNDP and UNCTAD);

•  Expert ranking: we have added to the dataset the Ranking of the countries by the level of industry 4.0 development in 2021 (according to the results of the year), relevant for 2022;

•  Relevance and representativeness: the dataset contains the up-to-date figures (as at year-end 2019), which form the basis of empirical research in 2020;

•  Worldwide coverage : the dataset contains statistics for the complete sample of modern countries of the world, thanks to which it offers ample opportunities for the worldwide analytics;

•  Consistency: collection and systematization of basic statistical data in the common dataset;

•  Distinct structure: in order to make the management of datasets the most simple, fast and user-friendly, topics were distinguished in its structure;

•  Informational content: the dataset presents relevant international statistics in Russian;

•  Availability of templates: the dataset offers two data templates: G7 countries (developed countries) and BRICS countries (developing countries), CIS countries, EEU countries, which enables accelerated selection of the necessary data for economic experiments aimed at comparing countries of the main categories in real-time mode;

•  Data import: the dataset allows selecting the necessary information and importing it into Microsoft Excel for further analysis;

•  Interactivity: the dataset allows sorting and combining various data, integrating them into a common array in exactly the way that is required by every user;

•  Work on the blockchain principle: the dataset allows sharing information, changing and processing it on demand of users, and the initial data remain unchanged at that, which is very convenient and safe.

The dataset was developed by Elena G. Popkova, D.Sc. Economics, Professor, President of the Institute of Scientific Communications.

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Russia is characterized by the high degree of regionalization, which is due to its geographic, cultural and historical characteristics and causes contradictory consequences. On the one hand, the decentralization of state management supports the multinational nature of Russian society and allows each region to promote, legislate and uphold its unique norms and values. Therein lies the guarantee of social tolerance of Russian population.

On the other hand, differences in the level and the rate of social and economic development of regions are responsible for the lack of balance in the regional economy of Russia, tampering with its integrity. As a result, instability is caused, that is, an increased susceptibility of Russia to various crises: social – in case of escalation of popular discontent in the regions with low standard of life which is manifested in social protests and intensified outgoing migration flows from these regions – and economic – under accelerated rate of inflation in certain regions, a critical fall of living standards of their population and the institutionalization of their subsidized dependence.

In order to achieve the transition of modern Russia from the category of leading developing countries (BRICS) into the category of developed countries (OECD), it is necessary to ensure the balanced state of the regional economy, which found its way into the Strategy of spatial development of the Russian Federation until 2025, ratified by the Decree of the Government of the Russian Federation No. 207-R of February 13, 2019. This means that flexible regulation of the regional economy of Russia is required, which would promote moderate differences in the regulatory framework to maintain cultural identity, tolerance of population of regions and their self-sufficiency (preservation of federalism) and, at the same time, level out differences in the level and the rate of social and economic development of regions (achieving the balanced state).

In this regard, the critical task of economic science is to develop a concept and applied recommendations to fill up the gap in the social and economic situation of regions. However, this is hindered by insufficient and inconsistent statistics of the regional economy of Russia. The main source of official empirical data on the regional economy of Russia is the annual statistical book “Russian regions. Social and economic indicators”.

Although it is available electronically, the indicators that are contained in it are presented in separate sections and are not prepared for automatic processing. The advantage of this statistical book consists in the clearly defined structure and large period of time for which the data are available (2002-2018), and its drawback consists in the difference in values ​​of indicators in the reports for various periods (because the data are recalculated and only the latest reports are updated), and fragmentarity: each report contains data only for the current year and for several random previous periods, which greatly complicates the comparison of data and the study of their dynamics.

Furthermore, there are a number of thematic ratings of Russian regions, the most common of which is the Rating of the social and economic situation of regions – 2019, in which the regions are ranked according to such indicators as the size of the economy, efficiency of the economy, public sector and social sphere. The advantage of this rating consists in the interactivity and the presence of the map of the regional economy of Russia, and its drawbacks are the non-transparency of the methodology (the methodology is presented in general terms only, values ​​of indicators were converted into points) and the static nature of the rating – it is only reflective of the current situation of the region in the economy of Russia, but it is not reflective of the long-term dynamics (all data are presented for 2017-2018 only) and future prospects for its development.

Advanced technologies open up opportunities for extended automated and intelligent data analytics, but these opportunities cannot be implemented in practice due to the lack of special databases of clearly structured digital data integrated into a unified system (interrelated) – data sets. The Institute of Scientific Communications (ISC) contributes to solving this problem and creates the first data set which combines statistics on the topic of the balanced state of the regional economy of Russia, forms Big Data of it, that are logically structured and systematized according to the blockchain principle, and are ready for processing with the use of Artificial Intelligence.

The data set contains indicators, most of which were presented by Rosstat, for the most topical areas of scientific economic studies on the topic of the balanced state of the regional economy of Russia:

•  Level of social and economic development:

•  GDP per capita and “underdevelopment whirlpools” calculated on the base of it;

•  Balanced financial result of enterprises;

•  Occupational level;

•  Regional budget balance.

•  Potential for social and economic development:

•  Fixed investment per capita;

•  The share of innovatively active organizations;

•  The share of innovative products;

•  Digitization level ( Digital Russia Index, calculated by the Center for Financial Innovations and Cashless Economy of the Moscow School of Management SKOLKOVO).

Advantages of the ISC data set:

•  Consistency and accuracy: collection and systematization of the main statistical data in the common data set, as well as their analysis with the use of a proprietary methodology for the calculation of “underdevelopment whirlpools” , which allows determining both static (for a particular period of time) and dynamic (taking into account changes for a number of periods) balanced state of the regional economy;

•  Topicality and representativeness: the data set contains up-to-date information (at year-end 2018), as well as forecasts for 2019-2024, which form the basis for empirical studies in 2020;

•  Reliability and objectivity: the data set contains combined statistics from the reliable sources of statistical data, namely the Federal State Statistics Service (Rosstat) and the Center for Financial Innovations and Cashless Economy of the Moscow School of Management SKOLKOVO;

•  Clearly defined structure: to make the work with the data set the most simple, fast and convenient for users, thematic sections were distinguished in its structure;

•  Presence of templates: the data set offers data templates with two different criteria of classification of Russian regions: geographic (patterns by federal districts) and rating-based (templates by categories), which makes it possible to quickly select the necessary data for economic experiments aimed at comparison of regions in real-time mode;

•  Data import: the data set allows selecting necessary information and importing it in Microsoft Excel for subsequent analysis;

•  Interactivity: the data set allows sorting and arranging various data, combining them into the common data array in exactly the manner that is necessary to every user, and automatically plotting diagrams ; the data are available on the maps of the regional economy of Russia (by regions and federal districts) and in the form of a rating ;

•  Operation according to the blockchain principle: first, the data as such are logically structured according to the blockchain principle; second, the data set allows sharing information, changing and processing it at requests of the users, while the initial data remain unchanged, which is extremely convenient and secure.

Proprietary methodology of the Doctor of Economics, Professor E. Popkova , described and well proven in the following publications (but not limited to them) is used for the calculation of the depth of “underdevelopment whirlpools” in the regions and federal districts of the Russian Federation:

•  Popkova, E. (2004a). Spatial-temporal approach in the concept of the new quality of economic growth. Ekonomicheskie Nauki, 7, pp. 33-40.

•  Popkova, E. (2004b). The interaction of the concepts of sustainable development and the new quality of economic growth. Finansy i Kredit 25(163), pp. 43-47.

•  Popkova, E. (2004c). The interaction of the concepts of sustainable development and the new quality of economic growth. Ekonomicheskiy Analiz: Teoriya i Praktika, 25, p. 43.

•  Popkova, E. (2005). Formulation of the concept of the new quality of economic growth in a climate of modern globalization: a doctoral thesis, Penza.

•  Shakhovskaya, L., Popkova, E. (2004). New quality of economic growth in a climate of modern globalization. Finansy i Kredit, 23(16), pp. 71-77.

•  Popkova, E. (2014). New prospects of economic growth in context of underdevelopment whirlpools phenomena. Applied Econometrics and International Development, 14(1), pp. 5-25.

•  Popkova, E. (2017a). Problem analysis of 'underdevelopment whirlpools' as an obstacle for economic growth in Asian countries. International Journal of Business and Globalisation, 19(2), pp. 167-174.

•  Popkova, E. (2017b). Use of methodology of “underdevelopment whirlpools” with analysis of problems and perspectives of economic growth in Asian countries . Journal of Entrepreneurship in Emerging Economies , 9(1).

•  Popkova, E. ,  Gornostaeva, Z. ,  Tregulova, N. (2018). Role of innovations in provision of competitiveness and innovational development of economy and overcoming of “underdevelopment whirlpools” in Russia and countries of Eastern Europe . Journal of Entrepreneurship in Emerging Economies , 10(3), pp. 511-523.

The “underdevelopment whirlpool” be understood as the degree of lagging of the economic system from the reference system in terms of the level of GDP per capita – in this particular case, of a region or federal district from the average level of GDP per capita in the Russian Federation. The “underdevelopment whirlpool” has a static measure – the depth in the specified year – and dynamic indicator – the sucking-in rate of the “underdevelopment whirlpool” for 1 year.

The depth of the “underdevelopment whirlpool” is determined as follows. First, the value of GDP per capita based on current (market) prices for a particular year is taken from the official statistics. Then a year is determined in which the closest value of GDP per capita for the entire Russian Federation (reference value) could be observed. After that, the depth of the “underdevelopment whirlpool” is determined as the difference between the current year and the relevant year. Then the sucking-in rate of the “underdevelopment whirlpool” is determined as the difference between the depth of the “underdevelopment whirlpool” in the current year and its depth in the previous year.

The values of GDP per capita in the Russian Federation are taken as reference values. Since official statistics present data for this indicator only for the period from 1995 till 2016 these data are supplemented for the computational analysis with author's estimation for the period from 1965 till 1994, as well as author's forecast for 2017-2024.

Table 1. Inter-temporal changes in GDP per capita in the Russian Federation in 1965-2024

*author's estimation;

**author's forecast “all other factors held equal”.

When forecasting all indicators presented in the data set, ISC is guided both by the inter-temporal changes in these indicators over the recent years and by the logic of their changes in the period until 2024 in accordance with the strategic course of social and economic development of the regional economy of Russia. The data set presents the data for the “underdevelopment whirlpools” for 2005-2024 and the data for other indicators for 2016-2024 (figures of digitization level are shown starting from 2017).

The dataset was developed by Elena G. Popkova, D.Sc. Economics, Professor, President of the Institute of Scientific Communications.

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The newest phenomena in the global economy changed the course of its development. Under the influence of the global recession (2008) and adopted goals of sustainable development, the capitalistic principles were reconsidered. In the near future, “pure” market is to be replaced by the social market economy. This will ensure transition from entrepreneurship, involved into “battle royale”, which is peculiar for highly-competitive market environment, to social entrepreneurship, involved into social problems of a territory on which it is located and contributing voluntarily into their solution.

The change of the model of entrepreneurial activities creates a serious challenge for modern economic subjects: entrepreneurs have to adopt a new philosophy of business, society has to form new consumer preferences for stimulating development of social entrepreneurship, and government should develop highly-effective “rules of the game” in the business environment with participation of social companies. Despite the acknowledgment of the necessity for development of social entrepreneurship, its scientific concept has not been formed.

Firstly, the very notion of social entrepreneurship has no scientific definition: there are a lot of scientific treatments that contradict each other. One view of social entrepreneurship envisages its definition by the criterion of the organizational & legal form and assigning the subjects of non-profit entrepreneurship. Another view focuses on the necessity for wide development of social entrepreneurship and envisages its treatment as entrepreneurship that manifests corporate social responsibility.

Secondly, the scientific and methodological provision for evaluation and analysis of social entrepreneurship is not determined, and there are no parameters for its measuring. The international statistical reports have no data on the topic of social entrepreneurship – small and medium entrepreneurship is traditionally distinguished in the structure of business, with hi-tech, digital, and innovative entrepreneurship.

Absence of empirical basis does not allow for monitoring of social entrepreneurship and thus hinders evaluation of the current level and tendencies of its development, as well as inter-territorial and international comparisons. Scientific research of social entrepreneurship is also complicated, as scholars have to use their own expert opinions (virtual evaluations, which are far from reality) and scatted and mostly incompatible data of different statistical sources, paying more attention to collection of accessible information than its processing and analysis.

The topicality of the problem is confirmed by the fact that there are attempts on overcoming the deficit of empirical data on the topic of social entrepreneurship. Let us provide the most well-known and successful examples. Charities Aid Foundation calculated the number of non-commercial companies in the USA in 2016 and issued a report “Gross domestic philanthropy: an international analysis of GDP, tax and giving”. Johns Hopkins Center for Civil Society Studies in their report “The 2019 nonprofit employment report” provided the number of volunteers in non-commercial organizations in the USA.

Supporting the current government initiatives on science's transfer into the digital form and bringing it in accordance to the Fourth technological mode, the Institute of Scientific Communications developed a data set on social entrepreneurship (the first one in the global practice). It contains statistical data on social entrepreneurship its evaluation with the help of specially developed index.

We hope that the presented data set will allow raising scientific interest in the topic of social entrepreneurship, increasing the number of performed studies, as well as their efficiency and effectiveness, and forming an empirical basis for automatized R&D in the sphere of social entrepreneurship with application of the technologies of Big Data processing and artificial analytics.

The data set contains the following indicators of social entrepreneurship in the global economy (for values – the higher the better):

1. Indicators of social responsibility of entrepreneurship:

•  Share of protected employment. The indicator is calculated by the UN (UNDP) within the “Human development report 2019. Beyond income. Inequality in human development”. Its original title is “unprotected employment”. The data set contains reverse values – different of the values of the original indicator. The indicator in the data set shows per cent of the officially employed with legal protection of their labor rights.

•  Hiring and firing practices. The indicator is calculated by the World Economic Forum within evaluation of global competitiveness (number 8.02). It shows companies' responsibility and seriousness during personnel management: how they strive to keep employees and prevent brain drain and how they are flexible during personnel selection and hiring, as well as the individual approach to personnel management ;

•  Redundancy costs weeks of salary. The indicator is calculated by the World Economic Forum within evaluation of global competitiveness (number 8.01). Its shows companies' obligations on material provision and benefits for redundant workers: complexity of the organized measures for early warning of workers about their reduction, the volume of dismissal pay, and the period of payment of dismissal pay ;

•  Cooperation in labour-employer relations. The indicator is calculated by the World Economic Forum within evaluation of global competitiveness (number 8.03). Its shows companies' loyalty to the activities of unions, their support for these activities, and execution of their requirements;

•  Flexibility of wage determination. The indicator is calculated by the World Economic Forum within evaluation of global competitiveness (number 8.04). Its shows the level of individualization of the approach to wages and the effectiveness of the system of motivation and stimulation of labor of companies' workers;

•  Reliance on professional management. The indicator is calculated by t he World Economic Forum within evaluation of global competitiveness (number 8.09). Its shows the level of qualification and experience (competence) of companies' managers and their ability to create favorable conditions and to organize efficient work at a company, as well as to develop the labor and creative potential of the workers;

•  Pay and productivity. The indicator is calculated by the World Economic Forum within evaluation of global competitiveness (number 8.10). Its shows connection between wages and efficiency, reflecting the justice of the system of wages at companies and its stimulation of workers for manifesting high efficiency;

•  Ratio of wage and salaried female workers to male workers. The indicator is calculated by the World Economic Forum within evaluation of global competitiveness (number 8.11). It shows gender barriers at companies and gender differences in career building and wages;

•  Multi-stakeholder collaboration. The indicator is calculated by the World Economic Forum within evaluation of global competitiveness (number 12.04). It shows involvement and collaboration of interested parties – employees, populations of the territory on which the company is located, creditors, investors, intermediaries, representatives of civil society, and consumers – in the company's activities and reflects the level of business's taking into account the interested parties' opinions.

2. Indicators of non-commercial activities of entrepreneurship:

•  Number of non-profit enterprises. The indicator is calculated by the expert and analytics organization CSR Hub within the Overview of corporate social responsibility for regions and countries;

•  Share of non-profit enterprises. The indicator was calculated by Gross Domestic Philanthropy in 2016. Its original title is “Charitable donation”. It shows the share of non-commercial organizations in the structure of entrepreneurship in 20 countries in 2016;

•  Business extent of disclosure index. The indicator is calculated by the World Bank. Its original title is “ Business extent of disclosure index (0=less disclosure to 10=more disclosure)”. Its shows the level of disclosure of entrepreneurship, sufficiency of corporate reports, and level of disclosure of information on companies' activities in the official documents .

•  World giving index. The indicator is calculated by the Charities Aid Foundation within the report “CAF World giving index: ten years of giving trends”. Its shows charity (non-profit, social) activities in the socio-economic system, including help for underprivileged people, donations, and volunteer work.

Advantages of the data set of the Institute of Scientific Communications :

•  System and correctness: collection and systematization of the main statistical data in one data set and their analysis with application of the proprietary methodology of calculation of the social entrepreneurship index , which allows determining the level of sociality of entrepreneurship in economy and making international comparisons;

•  Topicality and representation: the data set contains new data (as of year-end 2019), which form the basis for empirical studies in 2020;

•  Reliability and objectivity: the data set contains the statistics of respectable sources on the topic of social entrepreneurship: Gross Domestic Philanthropy, CSR Hub, Charities Aid Foundation, World Economic Forum, UNDP, and World Bank;

•  Clarity of the structure: for simple, quick, and convenient work for users, thematic parts are distinguished in its structure;

•  Templates: the data set offers two data templates: countries of G7 (developed) and countries of BRICS (developing), countries of the CIS, countries of the EAEU, and geographic templates of regions of the world, due to which it is possible to quickly select the necessary data for economic experiments that are aimed at comparison of countries of the main categories in real time;

•  Import of data: the data set allows selecting the necessary information and importing it in Microsoft Excel for further analytics;

•  Interactivity: the data set allows sorting and combining various data, unifying them into a general array according to user's needs and allows forming and visualizing the interactive profiles of countries of the world by the level of development of social entrepreneurship;

•  Ranking: based on the data set, a ranking of social entrepreneurship in countries of the world for 2020 is compiled;

•  Work by the blockchain principle: firstly, the data are structured by the blockchain principle; secondly, the data set allows sharing information, changing it, and processing it according to users' queries – the initial data remain unchanged, which is very convenient and safe.

The data set was developed by Elena G. Popkova, D.Sc. Economics, Professor, President of the Institute of Scientific Communications and by Bruno S. Sergi, D.Sc. Economics, Professor, Harvard University (USA) and University of Messina (Italy)

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Year 2020 has started from the global pandemic of novel coronavirus infection COVID-2019. Despite the sophisticated medicine, it emerged that humankind is unprepared for its onset. Concerted action by the world's governments and international organizations makes it possible to contain the spread of the infection, and the dedication of doctors makes it possible to keep the mortality rate at an unprecedented low level compared to previous epidemics and pandemics. Full-scale information support under the auspices of the World Health Organization has enabled the international cooperation in combating the virus and the active search for vaccine and medications for people who contracted a disease.

The security measures being implemented are so strong, and the responsibility for their implementation is so high that the new coronavirus pandemic is being compared to war. But this is no ordinary war. Instead of calling for a gathering of people, it requires people to self-isolate. Instead of unsanitary conditions and the shortage of drugs typical of warfare, sanitary control is getting increasingly stringent everywhere, antiviral agents as well as advanced medicines and medical devices have been used. Instead of intensifying the confrontation, this conditional war rallied the whole world in a struggle against the global viral threat.

Medicine is the weapon in this war, as it is aimed at controlling the virus, stopping its further spread and protecting humanity from it. While the media has shifted the attention of great masses of population to the health of themselves and their loved ones, the current mission of science is to find a solution to the multifaceted problem of the pandemic. This is a challenging task not only for medical science, but also for other sciences. Quarantine measures pose a strong challenge to the economy. At the end of the first trimester (as of the end of March) 2020, it may safely be said that a new global crisis has come.

Against the backdrop of business slowdown, the state needs to allocate funds to finance health care, support businesses and the general population that suffer losses. The economic science is aimed at assessing the impact of the pandemic and setting out financial and managerial prospects of dealing with it. Uncertainty gives rise to feverish demand, which in turn leads to artificial scarcity and inflation. The underlying cause of these phenomena is the poor information support for the current situation and the prospects for further progression of events.

Hypotheses that are made by the media have no scientific basis and simply increase uncertainty even more. For example, according to the original hypothesis, COVID-2019 is a biological weapon against Asian countries, and it can only be contracted by representatives of Asians countries contrary to the basic concepts of modern virology, and the confidence of the inhabitants of other regions of the world in this fact prevented them from taking measures to prevent the spread of the infection. This may have been one of the causes of the Italian site of infection.

In order to provide the full scientific support in a struggle against the pandemic of novel coronavirus and future epidemics and pandemics, the Institute of Scientific Communications (ISC) has created a dataset that brings together all relevant and up-to-date statistics on infectious diseases and their impact. We urge all scientists to refocus their attention on research on the pandemic in order to mitigate the crisis and overcome it as soon as possible, and we hope that the ISC dataset will help them in this mission. The dataset consists of two sections.

Section 1 provides detailed information on epidemics and pandemics and their impact since the second half of the 20th century. It examines the temporal dynamics of aggravation of all infectious diseases reported by the World Health Organization throughout the world, as well as the key indicators of the global economy based on the World Bank data (for the period from 1960 to 2018) and the International Monetary Fund (estimates for 2019-2020).

This section presents, first, the ordered data on infectious diseases based on information of the World Health Organization:

•  List of infectious diseases;

•  Brief description of each infectious disease;

•  Countries where an epidemic or pandemic of infectious disease occurred;

•  Category according to prevalence of disease and its focalization (epidemic or pandemic);

•  Current status of infectious disease;

•  The period of the last epidemic or pandemic of infectious disease;

•  Mortality rate based on the total number of cases of infectious disease.

Second, the statistics of key indicators of the international economy and health care based on the World Bank data :

•  The number of infectious diseases, epidemics or pandemics of which occur in a given year (calculated by the ISC);

•  The average mortality rate due to infectious diseases in a given year (calculated by the ISC);

•  Global annual GDP growth;

•  Global GDP per capita;

•  Global annual consumer-price inflation;

•  Global unemployment rate;

•  Global current health expenditure;

•  The number of physicians per 1,000 people throughout the world;

•  The number of hospital beds per 1,000 people throughout the world.

Section 2 presents countrywise data. First, the incidence statistics for infections that are most relevant today:

•  Mortality rate due to infectious diseases based on the World Bank data;

•  The total incidence of HIV, % of population aged 15-49 based on the World Bank data;

•  The incidence of tuberculosis per 100,000 people based on the World Bank data;

•  The number of coronavirus cases as at the end of March 2020 based on the latest up-to-date data of the World Health Organization and RND.

Second, the statistics of key indicators of potential economic impact of epidemics and pandemics based on the World Bank data :

•  Annual GDP growth;

•  GDP per capita;

•  Annual consumer-price inflation;

•  Unemployment rate;

•  Gini index as an indicator of income inequality (social stratification) in the economy.

Third, statistics on preparedness of the countries of the world for epidemics and pandemics based on the World Bank data:

•  Current health expenditure;

•  The number of physicians per 1,000 people;

•  The number of hospital beds per 1,000 people.

Advantages of the dataset from the ISC:

•  Timeliness: this dataset presents the latest data on pandemic of novel coronavirus infection COVID-2019. The statistics on COVID-2019 will be updated every trimester (3 months) until the end of pandemic; the primary data are presented according to the results of the 1st trimester as at the beginning of April 2020;

•  Detailed elaboration : the dataset is not limited to the only morbidity statistics available at the official website of the World Health Organization, but also gives the data on the recovery, incidence and mortality rate of COVID-2019, for which the RND data are used; this allows describing the pandemic as comprehensively as possible;

•  Consistency and accuracy: collection and systematization of the basic statistical data in the common dataset;

•  Extensive timeliness: the dataset contains the data for 1960-2020, which allows conducting research over time and provide a detailed coverage of epidemics and pandemics in research activities;

•  Reliability and fairness: the dataset brings together the statistics from reliable sources of statistical and expert analytic data on social entrepreneurship, namely the World Bank, the International Monetary Fund and the World Health Organization;

•  Information content: the dataset presents the latest international statistics in Russian;

•  Clear structure: in order to make working with the dataset more simple, fast and convenient for the users, the topic sections have been identified in its structure;

•  Availability of templates: the dataset offers two data templates: G7 countries (developed countries) and BRICS countries (developing countries), CIS countries, EEU countries, geographical templates of parts of the world which enable the expedited collection of necessary data for economic experiments aimed at real-time comparison of countries of main categories, and selection of COVID-2019 incidence rate patterns ;

•  Data import: the dataset allows selecting the necessary information and importing it to Microsoft Excel for subsequent analysis;

•  Interactivity: the dataset allows sorting and combining various data, bringing them together in a common data array in exactly the manner that is necessary to every user, and automatically making and visualizing virtual country profiles in the context of the COVID-2019 pandemic in 2020;

•  Ranking: the dataset has been used as a basis for making a ranking of countries' positions in the context of the COVID-2019 pandemic in 2020;

•  Operation according to the block chain principle: first, the data as such are logically structured according to the block chain principle; second, the dataset allows sharing information, modifying and processing it at requests of the users; moreover, initial data remain unchanged, which is very convenient and safe.

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Climate change is a common problem of all humankind, which always existed but became especially important due to a range of reasons connected to high economic activities and technological progress. Firstly, over the whole history of life on the Earth and up until the First Industrial Revolution, which started in the 18 th century, climate change was predetermines by the natural processes. Industrial revolutions, which replaced each other, caused the artificial reasons of climate change.

Industrial production and consumption of its products led to thinning of the ozone layer, large dump sites (with a lot of products with large decomposition periods – e.g., polyethylene), and ecological disasters (e.g., oil leaks, usage of nuclear weapon, and atomic power stations accidents). This resulted in change of the Earth's polarity, global warming, decrease of biodiversity, critical depletion of resources, and aggravation of environment for human life. While natural factors cannot be controlled, artificial factors, which are created by a human, could and should be controlled for fighting climate change, preventing the depletion of natural resources, and restoring and supporting favorable conditions for life.

Secondly, recently climate change became especially vivid and led to clear negative consequences. Melting of glaciers became a reality, causing the risk of flooding of continents and defrosting of historical viruses, which could cause new global pandemics. Growth of air temperature makes new territory unfit for agriculture. The level of environment pollution has reached a critical point and requires mandatory regulation.

Thirdly, the opportunities that appeared due to dissemination of the leading technologies allow for significant progress in fighting climate change. In 2015, adoption of the new goals of sustainable development (successors of the millennium development goals) against the background of the Fourth Industrial Revolution reflected the current problems of the humankind and set achievable tasks before people. Thus, it is possible to expect that the sustainable development goals will be fully achieved by 2030.

Thus, it is necessary to take unprecedented measures for fighting climate change. An important role in this process belongs to information support for the regulatory measures of national governments and international organizations. Manifestations, factors, and consequences of climate change are shown in detail in the UN Sustainable Development Report, the World Bank data base on climate change, OECD analytics on Green growth and eco-innovation, and statistical data bases of various international organizations, among which are Dual Citizen and New Economics Foundation.

At the same time, the practices of fighting climate change are not sufficiently covered in the existing statistical data bases. Most of them focus on the practices of state regulation, which are brought down to implementation of ecological standards and financing of measures in the sphere of environment protection. A lot of covered practices cannot be measured and are brought down to qualitative description, which complicates conducting quantitative studies and forming an evidential basis of scientific works on the topic of sustainable development and fighting climate change.

A vivid example of filling the gaps in statistics is data set “European eco-innovation scoreboard interactive tool” of the European Commission. It contains detailed data on the topic of eco-innovations, “green” investments, eco-standardization and its observation at European companies, eco-innovation scientific activities (R&D), and effectiveness of using resources in the activities of entrepreneurship and social results. The data for 2010-2018 are available only for 28 countries of the EU, which allows only for narrow research, without the possibility of obtaining results on the global economy and inter-regional comparisons at the global level.

In order to solve the above problem, which consists in the fragmentary character and deficit of factual data on the global economy, the Institute of Scientific Communications created a data set, which covers all practices of provision of sustainable development and fighting climate change, which are predetermined by state regulation and market self-regulation. Special attention is paid to a new practice – corporate social responsibility, one of which directions if ecological responsibility as socially important activities.

The data set contains the following indicators on the implemented practices of corporate social responsibility, sustainable development, and fighting climate change (the higher the indicators' values, the better – unless otherwise specified):

1. Sustainable development and fighting climate change:

•  Energy Trilemma Index. It is calculated by Visual Capitalist; it shows energy security, energy accessibility, and ecological sustainability and effectiveness of the energy sphere (the data for 2019 are available);

•  Industrial profile of economy. This indicator is calculated by the Institute of Scientific Communications based on the World Bank data; it shows the structure of the extracting (industry) and manufacturing (manufacturing) industries in the structure of GDP (the lower the indicator's value, the better). The data for 2019 are available;

•  Total natural resources rent (% of GDP). It is calculated by the World Bank; it shows the share of natural resources extraction in the structure of GDP (the lower the indicator's value, the better). The d ata for 2017 are available;

•  Pollution Index. This indicator is calculated by Numbeo within the Quality of Life Index and shows the scale of environment pollution (the lower the indicator's value, the better). The data for 2016-2020 are available;

•  Climate Index. This indicator is calculated by Numbeo within the Quality of Life Index and shows the scale of climate changes and the general state of the environment (the lower the indicator's value, the better). The data for 2016-2020 are available;

•  Sustainable Development Index. This indicator is calculated by the UN and shows achievement of the sustainable development goals. The indicator for 2016-2019 is available.

2. State regulation of corporate social and ecological responsibility:

•  Energy efficiency regulation – original title is “ 1.24. Energy efficiency regulation”. It is calculated by the Global Economic Forum, is included in “ The Global Competitiveness Report 2019”, and reflects the intensity of the government measures on providing energy efficiency by regulation of entrepreneurship. The indicator for 2019 is available;

•  Renewable energy regulation - original title is “ 1.25. Renewable energy regulation”. It is calculated by th e Global Economic Forum, is included in “ The Global Competitiveness Report 2019”, and shows government requirements to circular use of energy in entrepreneurship. The indicator for 2019 is available;

•  Environment-related treaties in force – original title is “1.26. Environment-related treaties in force”. It is calculated by t he Global Economic Forum, is included in “The Global Competitiveness Report 2019”, and shows the number of the existing standards on environment protection. The indicator for 2019 is available.

3. Market self-management of corporate social and ecological responsibility:

•  Buyer sophistication – original title is “ 12.09 Buyer sophistication”. It is calculated by the Global Economic Forum, is included in “The Global Competitiveness Report 2019”, and reflects consumers' market power and the level of buyer sophistication. The data for 2018-2019 are available;

•  Extent of market dominance - the original title is “7.02 Extent of market dominance”. It is calculated by t he Global Economic Forum, is included in “The Global Competitiveness Report 2019”, and shows the influence of competitive forces on the activities of entrepreneurship. The data for 2018-2019 are available;

•  Trade openness. This indicator is calculated by the Global Economic Forum, is included in “The Global Competitiveness Report 2019”, and shows openness of customs borders;

•  Entrepreneurial culture. This indicator is calculated by the Global Economic Forum, is included in “The Global Competitiveness Report 2019”, and shows susceptibility and inclination of entrepreneurship for changes, risk, and innovations. The data for 2018-2019 are available.

Advantages of the data set of the ISC :

•  Consistency and authenticity: collection and systematization of the main statistical data in the data set, as well as their analysis with application of the proprietary methodology of calculation of the index of corporate fight against climate change , which allows determining the contribution of corporate social and ecological responsibility into provision of sustainable development in countries of the world and performing international comparisons;

•  Topicality and representativeness: the data set contains the latest data, which form the basis of empirical studies in 2020;

•  Reliability and objectivity: the data set contains the statistics of the respectable sources of statistical and expert & analytical data on the topic of social entrepreneurship – namely, the World Bank , Visual Capitalist, Numbeo, and the UN (UNDP);

•  Informative value: the data set presents the current international statistics in the Russian language;

•  Clarity of the structure: in order to make the work with the data set simple, quick, and convenient for users, it consists of thematic parts;

•  Templates: the data set offers two templates of data: countries of G7 (developed) and countries of BRICS (developing), countries of the CIS, countries of the EAEU, geographical templates of regions of the world, and templates of categories of countries that are distinguished by the ISC by the criterion of sustainable development and fighting climate change – which allows for quick selection of the necessary data for economic experiments, aimed at comparison of countries of different thematic categories in real time;

•  Import of data: the data set allows selecting the necessary information and importing it in Microsoft Excel for further analytics;

•  Interactivity: the data set allows sorting and combining various data, unifying them in the general data array in the way that is required by each user;

•  Ranking: the data has been used for compiling a ranking of sustainable development and fighting climate change based on corporate social and ecological responsibility in countries of the world in 2020;

•  Blockchain principle: firstly, the data are structured logically according to the blockchain principle; secondly, the data set allows sharing, changing, and processing information as per users' queries'; the initial data remain unchanged, which is very convenient and secure;

•  Imitation modeling and neural network analysis: the data models the ratio of the regulatory and market factors in provision of corporate social and ecological responsibility, achievement of sustainable development, and fighting climate changes in geographical regions of the world in 2020, which allows for thorough research on the topic of the data set in view of the specific features of the regional economy. The results of modeling are shown in the form of a cognitive map.

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The problem of humanization of economic growth through achievement of the balance of social and economic, public and private interests has been present during the whole history of industrial revolutions. In the 20 th century, in the period of initial industrialization and prosperity of industry, the set problem was solved in some countries (USSR, Germany, France, Italy, and Cuba) by building socialism with a perspective of transition to communism, and in other countries (UK and USA) – by building capitalism and imperialism.

As the practical experience of implementation of the both historical models of humanization of economic growth showed, they are not very effective and the priorities that they proclaim cannot be achieved in practice. Later practices of solving the formulated problem, which envisage combination of the historical models, showed their effectiveness – but they are not universal and could be applied only in a strict context. For example, the Chinese model of communist market economy is based on a unique social mode and traditional culture. It could be implemented only on China, and its copying and dissemination in other countries might not ensure humanization of economic growth, and might even lead to socio-economic crises.

In the 21 st century, the start of the Fourth industrial revolution, which marked the transition to the digital technological mode (Industry 4.0) announced a new age of domination of industry. Neo-industrialization based on breakthrough technologies and “smart” management is rather dangerous for humanity, as it opens expanded opportunities for flexible setting of the parameters of industrial production. If the target goals for “smart” companies are commercial interests (profit, profitability, and efficiency) without consideration of public interests (support for employment, realization of human potential, and rational nature use), this might cause a social crisis, as quality of life and living standards of the population will decrease to a critically low level.

The “market gap” during humanization of economic growth in the conditions of the digital economy is to be overcome by the state. For this, Sustainable Development Goals have been adopted, and the corresponding national initiatives are implemented. In the modern economic conditions and the near future, the problem of humanization of economic growth will be solved based on formation of the social market economy, in which the balance of interests of economy's development and social progress is achieved. Though a concept of the social market economy has been actively developing in recent years, this process is slowed down by underdevelopment of the empirical basis.

At present, certain social indicators of economy within large-scale reports are available: “The Global Competitiveness Report” of the World Economic Forum and “Human Development Report” of the United Nations Development Program (UNDP), or thematic studies of humanization of economic growth for a narrow list of countries. An example is “Sustainable Governance Indicators”, calculated by Bertelsmann Stiftung, which reflect the effectiveness of state management, quality of democracy, and other social characteristics of economy in the OECD and EU countries. Here it is also necessary to note “Social Justice in the EU and OECD Index Report 2019”, prepared by Thorsten Hellmann, Pia Schmidt, and Sascha Matthias Heller – experts of Bertelsmann Stiffung.

For the purpose of creation of a reliable empirical basis, which would allow for global scientific studies on the topic of humanization of economic growth, the Institute of Scientific Communications has developed a dataset which contains the most relevant indictors on this topics, which are available for most countries. The dataset contains the most up-to-date values of the indicators for 2020, which could be used for R&D on the topic of humanization of economic growth. The dataset is an interactive digital data base, which could be processed with the help of intellectual analytics based on Dig Data technologies and could be used for digital modeling of the process of humanization of economic growth in the modern national and global economies.

The dataset contains the following indicators of humanization of economic growth (the higher the indicators' values, the better – unless stated otherwise):

1. Humanization of state regulation of economy:

•  Liberality of economy. The original title of this indicator is “checks and balances”. It is calculated by the World Economic Forum within “The Global Competitiveness Report 2019” and reflects the transparency of state budget, independence of judicial system, effectiveness of the legal base in complex normative acts, and freedom of speech and press;

•  Efficiency of regulation. The original title of this indicator is “public-sector performance”. It is calculated by the World Economic Forum within “The Global Competitiveness Report 2019” and reflects the burden of state regulation, effectiveness of the legal basis during resolution of economic disputes, and opportunities for online participation of population in state management (e-participation);

•  Transparency of regulation. The original title of this indicator is “transparency”. It is calculated by the World Economic Forum within “The Global Competitiveness Report 2019” and reflects the level of corruption and efficiency of measures on fighting corruption;

•  Protection of property rights. The original title of this indicator is “property rights”. It is calculated by the World Economic Forum within “The Global Competitiveness Report 2019” and reflects the general protection of property rights, quality of land administration, and protection of intellectual property rights;

•  Future orientation of regulation. The original title of this indicator is “future orientation of government”. It is calculated by the World Economic Forum within “The Global Competitiveness Report 2019” and reflects stability of state regulation, state's responsibility for the changes and before future generations, support for digital business, long-term vision (strategic orientation), energy efficiency, use of renewable energy, and environment protection;

•  Eco-efficiency index. The original title is “Environmental Performance Index”. It is calculated by Yale University and reflects the current state of the environment, eco-system's vitality, and efficiency of the activities on ecological situation improvement .

2. Favorability of social environment for realization of human potential:

•  Human potential. The original title is “skills of current workforce”. It is calculated by the World Economic Forum within “The Global Competitiveness Report 2019” and reflects the level of education and training of personnel, qualification of labor resources, and digital skills of population;

•  Inequality-adjusted Human Development Index. This indicator is calculated by the UN (UNDP) within “Human Development Report 2019” and reflects the level of social justice during education, employment, career building, payment for work, etc.;

•  Poverty headcount ratio at $3.20/ day. This indicator is calculated by the UNDP and is a part of the indicators of “Sustainable Development Report” for 2019, reflecting the share of population below the poverty threshold of $3.2 per day ( the lower the indicator's value, the better );

•  Index of economic freedom. This indicator is calculated by the Heritage Foundation and is a part of the report “Country Rankings of Economic Freedom” for 2020, reflecting supremacy of law, level of bureaucratization of state management, effectiveness of regulation, and openness of markets ;

•  Prosperity index. This indicator is calculated by Legatum Institute Foundation and is a part of the report “ The Legatum Prosperity Index ™ 2019: Creating the Pathways from Poverty to Prosperity ” , reflecting achievements of countries from the point of view of their well-being and prosperity ;

•  Labor efficiency. The original title of this indicator is “GDP per hour worked”. It is calculated by the OECD and shows the cost of products manufactured by 1 worker per 1 hour of work;

•  GDP (PPP) per capita . The original title of this indicator is “Gross domestic product based on purchasing-power-parity per capita GDP, current international dollar”. It is calculated by the IMF within “World Economic Outlook Database” and shows purchasing power parity (PPP) value of all final goods and services produced within a country in a given year, divided by the average (or mid-year) population for the same year .

Advantages of the dataset of the Institute of Scientific Communications :

•  Consistency and authenticity: collection and systematization of the main statistical data in the data set, as well as their analysis with application of the proprietary methodology of calculation of the index of humanization of economic growth , which allows determining the potential of creation of social market economy in different countries and performing international comparisons;

•  Topicality and representativeness: the dataset contains the latest data, which form the basis of empirical studies in 2020;

•  Reliability and objectivity: the dataset contains the statistics of the respectable sources of statistical and expert & analytical data on the topic of social entrepreneurship – the World Economic Forum, the United Nations (UNDP), the International Monetary Fund, and the OECD ;

•  Informative value: the dataset presents the current international statistics in the Russian language;

•  Clarity of the structure: in order to make the work with the dataset simple, quick, and convenient for users, it consists of thematic parts;

•  Templates: the dataset offers two templates of data: countries of G7 (developed) and countries of BRICS (developing), countries of the CIS, countries of the EAEU, geographical templates of regions of the world – which allows for quick selection of the necessary data for economic experiments aimed at comparing countries of the main categories, in real-time mode;

•  Import of data: the dataset allows selecting the necessary information and importing it in Microsoft Excel for further analytics;

•  Interactivity: the dataset allows sorting and combining various data, unifying them in the general data array in the way that is required by each user, as well as building digital models of humanization of economic growth in different countries;

•  Ranking: the data has been used for compiling a ranking of humanization of economic growth in different countries in 2020;

•  Blockchain principle: firstly, the data are structured logically according to the blockchain principle; secondly, the dataset allows sharing, changing, and processing information as per users' queries'; the initial data remain unchanged, which is very convenient and secure.

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Wildlife is a heritage of humanity which determines conditions for the existence of humankind today and is of high value for succeeding generations. The wealth of the animal and plant world is a special category of sustainable development which encompasses biodiversity, agriculture, and food security. This category is presented in Goal 14 “Conservation of marine ecosystems”, which implies conservation and efficient use of oceans, seas and sea resources, and Goal 15 “Conservation of terrestrial ecosystems”, which implies protection and recovery of terrestrial ecosystems and assistance in their efficient use.

Recent research activities, in particular, the discovery of Indian scientists under the supervision of Rajan Patil, Associate Professor of Epidemiology at the SRM University (Chennai, India), show that biodiversity has a great impact on epidemic proportions and effects. The scientifically proven relation between the decrease in biodiversity and the increased risk of transmission of infection is particularly relevant amid the COVID-19 pandemic, since one of the main hypotheses of emergence of this infection is the mutation of the virus and its transmission from animals to humans. Against the background of recent events and scientific arguments, biodiversity is not only the beauty and wealth of the natural world, but also the basis for the health of humankind.

This category also includes Goal 2 “Liquidation of hunger”, which implies protection and development of food and agricultural sector, offering key solutions for development and being pivotal in the fight against hunger and poverty. According to the UN, about 815 million people across the globe (12.9% of population) are undernourished.

Solving the food security problem is a great challenge, since it requires increasing the productivity of agriculture (against the backdrop of diminished fertility of land due to environmental pollution and climate change in conjunction with reduction of areas under crops due to urbanization), and maintenance of affordability of food, as well as high quality and safety for its human health. This implies a focus on development of agriculture and “green” (ecologically clean) production.

In order to make a consistent presentation of the wealth of the animal and plant world as well as abovementioned logically interdependent goals of sustainable development, the Institute of Scientific Communications (ISC) created its proprietary dataset dealing with the problem of maintenance of biodiversity, agricultural development, and food security. The dataset includes the following indicators (all higher indicator values are better, except those indicators for which there is a contrary provision):

1. Biodiversity based on the data from the UNDP (in 2015-2019):

Conservation of terrestrial ecosystems:

•  Imported biodiversity threats (per million population). It represents the number of endangered species due to international trade (import) (a lower indicator value is better);

•  Permanent Deforestation (5 year average annual %). It represents the average annual percentage of permanent deforestation. Permanent deforestation is classified as removal of crown cover due to urbanization, commodity production and operation of certain types of small-scale agriculture, excluding the temporary loss of forest due to forestry or forest fires (a lower indicator value is better);

•  Red List Index of species survival (0–1) . It represents the change in the overall risk of extinction among groups of species, which is based on real changes in the number of species in each category of endangered species, from Red Data Book;

•  Mean area that is protected in freshwater sites important to biodiversity (%) . It represents the average percentage of area of the key fresh-water areas of biodiversity (areas that are important for global conservation of biodiversity), which are protected by the State;

•  Mean area that is protected in terrestrial sites important to biodiversity (%) . It represents the average percentage of area of terrestrial (on-shore) areas of biodiversity (areas that are important for global conservation of biodiversity), which are protected by the State;

Conservation of marine ecosystems:

•  Fish caught by trawling (%) . It represents the share of total amount of fish (in tons) captured by trawling – this is a fishing method, at which industrial fishing vessels drag large nets (trawls) along the seabed, which poses a major threat to biodiversity (a lower indicator value is better);

•  Percentage of fish stocks overexploited or collapsed by EEZ (%) . It represents the share of total amount of fish captured in the country in its exclusive economic zone, which consists of fish that were overexploited or collapsed, weighted by the quality of data on fish capture (a lower indicator value is better);

•  Ocean Health Index Goal – Clean Waters (0–100) . It represents water purity, showing the extent to which sea waters within national jurisdiction were contaminated with chemicals and excessive amount of nutrients (eutrophication), pathogenic microorganisms or utility waste (a lower indicator value is better);

•  Mean area that is protected in marine sites important to biodiversity (%) . It represents the average percentage of area of key sea (on-shore) areas of biodiversity (areas that are important for global conservation of biodiversity), which are protected by the State.

2. Agriculture based on the data from the World Bank (in 2017-2018):

•  Land under cereal production (hectares) . It represents the area that was allocated for cultivation of cereals – total harvested acreage, although some countries report only of areas under crops. Cereals include wheat, rice, maize, barley, oat, rye, panic grass, sorgo, buckwheat and mixed grains. Production data for cereals refer to crops that are harvested for dry grain only. Cereals that are harvested for hay or for food, fodder or silage and those used for cattle grazing are excluded. Data for 2017 are presented;

•  Rural population (% of total population) . It represents the difference between the total population and the urban population. Data for 2018 are presented;

•  Cereal yield (kg per hectare) . It represents grain harvest measured in kilograms per hectare of harvested acreage, and includes wheat, rice, maize, barley, oat, rye, panic grass, sorgo, buckwheat and mixed grains. Production data for cereals refer to crops that are harvested for dry grain only. Cereals that are harvested for hay or for food, fodder or silage and those used for cattle grazing are excluded. Data for 2017 are presented;

•  Agriculture, forestry, and fishery, value added (% of GDP) . It represents forestry, hunting and fishery, as well as cultivation of crops and livestock breeding. Added value is a net product of the sector after the summation of all results and deduction of intermediate incoming materials and raw material. It is calculated without considering deductions for depreciation of finished assets or depletion and degradation of natural resources. Data for 2018 are presented.

3. Food security based on the data from The Economist Intelligence Unit Limited (in 2019):

•  Food Security Index (Overall score, Baseline index) . It represents the overall level of national food security with account of international comparisons;

•  Food affordability . It represents financial backing of food security programs by the state, access to financing for farmers, as well as living standards of the population and affordability of food;

•  Food availability . It represents mean food availability, agricultural infrastructure, and loss of food, which determine affordability of food to great masses of population in quantitative terms (shortage risk);

•  Food quality & safety . It represents food standards, accessibility of mineral nutrients (food value, health benefit) and safety of food products, including the possibility of their safe storage.

Advantages of the dataset provided by the ISC:

•  Consistency and credibility: collection and systematization of basic statistical data in the common dataset, as well as data analysis with the use of the proprietary methodology for the calculation of the wealth index of the animal and plant world , which makes it possible to carry out digital monitoring of biodiversity, agriculture and food security across the world and make international comparisons;

•  Topicality and representativeness: the dataset contains most recent available data (according to the results for 2015-2019), which form the basis for empirical studies in 2020;

•  Reliability and objectiveness: the dataset combines statistics of reliable sources of statistical and expert analytical data on the subject from the UNDP, World Bank, and The Economist Intelligence Unit Limited;

•  Informativity: the dataset presents up-to-date international statistics in Russian;

•  Well-defined structure: in order to make working with the dataset more simple, fast and convenient for users, topic-based sections were distinguished in its structure;

•  Availability of patterns: the dataset offers data patterns: integration patterns : G7 countries (developed countries) and BRICS countries (developing countries), CIS countries, EEU countries, geographic patterns of parts of the world, as well as patterns of wealth of the animal and plant world , which enable accelerated selection of necessary data for economical experiments aimed at comparing countries of main categories in real-time mode;

•  Data import: the dataset allows selecting necessary information and importing it to Microsoft Excel for the subsequent analysis;

•  Interactivity: the dataset allows sorting and combining various data, uniting them in a comprehensive data array in exactly the way that each user needs, and automatically carrying out digital monitoring of biodiversity, agriculture and food security, and visualizing its results for clarity;

•  Ranking: the dataset was used as a basis for making the ranking of wealth of the animal and plant world across the world in 2020;

•  Operation on a blockchain principle: first, the data as such are logically structured on a blockchain principle; second, the dataset allows sharing information, changing and processing it upon the request of users; the original data remain unchanged at that, which is very convenient and safe.

The data set was developed by Elena G. Popkova, D.Sc. Economics, Professor, President of the Institute of Scientific Communications

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COVID-19 has posed a serious threat for the mankind and demonstrated the consistency of processes of economic activity, having created the “dominoes effect”. In late 2019 - early 2020, only one country faced the new infection for which no cure was available at the time – it was China. However, in the context of strong interrelation and interdependence of participants of international economic relations, in as little as 2 months (by March 2020), COVID-19 has distributed to other countries across the world, which caused the pandemic. The events in the first trimester of 2020 are indicative of a high degree of integration of economic systems in the context of globalization, when the problems of a particular country become universal and require solving through joint efforts of the entire world community on the terms of mutual aid, mutual support and cooperation.

The posterior events in the second trimester of 2020 were representative of another aspect of the systemic effects of COVID-19, according to which the negative events in the health care system (pandemic, aggravated threat virus) have catalyzed the economic crisis, are generally referred to in international research papers as the COVID-19 crisis. Therefore, the fields of economic activity are interrelated, just like countries across the world. In order to achieve stability and sustainability of economic systems, it is necessary to achieve maintain equilibrium and balanced development of all fields of economic activity. While prioritization in accordance with the strategic benchmarks of economic development is justified and appropriate, the practices of financing socially significant sectors of the economy, which include health care, with whatever funds remain, given the persistent shortage of resources, are unacceptable and inappropriate.

The dataset of the Institute of Scientific Communications (ISC) aims to support the research on COVID-19 and the 2020 economic crisis, and to create the informational and empirical background for scientific evidence of their systemic interrelation. The dataset contains statistics that allow determining, first, the causes of differences in morbidity and mortality due to COVID-19 in countries across the world, and, second, ramifications of the COVID-19 crisis for the economy and business. The dataset contains up-to-date data for the second trimester of 2020, allowing for timely research based on relevant statistics. The dataset is intended to create a statistical framework in search for the comprehensive elimination of the pandemic and the economic crisis for the stabilization of the global economic situation and sustainable development of the international economy.

The dataset includes the following indicators (all higher indicator values are better, except those indicators for which there is a contrary provision):

The COVID-19 pandemic according to the World Health Organization and the World Health Map in the second trimester of 2020:

•  COVID-19 incidence rate – the total number of cases of infection for the entire period of the pandemic (lower indicator value is better);

•  The number of fatal cases due to COVID-19 – the total number of fatal cases for the entire period of the pandemic (lower indicator value is better);

•  New cases of infection with COVID-19 for 1 day – the number of new cases (lower indicator value is better);

•  Cases of recovery from COVID-19 for 1 day – the number of recovered patients;

•  Cases of infection with COVID-19 per 1 million population – the number of cases per 1 million population (lower indicator value is better).

Measures to control the COVID-19 virus threat in the second trimester of 2020:

•  Self-isolation index – activity in the streets of the cities all over the world; the data for the capital city have been presented. This indicator is calculated on a daily basis and is presented in Yandex.Maps. It represents the degree of achievement of the maximum level of activity that could be observed until the wide distribution of the COVID-19 (lower indicator value is better) in terms of percentage points;

•  Coronavirus (COVID-19) Testing ( https://ourworldindata.org/coronavirus-testing#source-information-country-by-country, https://github.com/owid/covid-19-data/tree/master/public/data/testing ) – the total number of tests performed for the entire period of the pandemic. This indicator is calculated by the Our World in Data and updated daily;

•  Population with household expenditures on health >25% of total household expenditure or incomer (%) . This indicator is calculated by the World Health Organization, is presented in the annual report “World Health Statistics 2020: monitoring health for the sustainable development goals” and shows the burden of public expenditures for medical services and medicines (lower indicator value is better);

•  Average of 13 International Health Regulations core capacity score . This indicator is calculated by the World Health Organization, is presented in the annual report “World Health Statistics 2020: monitoring health for the sustainable development goals” and shows the average score of observance of the 13 main medical and sanitary regulations;

•  Density of medical doctors (per 10 000 population) . This indicator is calculated by the World Health Organization and is presented in the annual report “World Health Statistics 2020: monitoring health for the sustainable development goals”;

•  Density of nursing and midwifery personnel (per 10 000 population) . This indicator is calculated by the World Health Organization, is presented in the annual report “World Health Statistics 2020: monitoring health for the sustainable development goals” and shows the density of nursing and obstetric staff;

•  Total net official development assistance to medical research and basic health sectors per capita (US$), by recipient country . This indicator is calculated by the World Health Organization, is presented in the annual report “World Health Statistics 2020: monitoring health for the sustainable development goals” and shows the net worth of the official financial assistance for the development of medical studies and the main health care sectors per capita (USD) by recipient countries;

•  Age-standardized prevalence of obesity among adults (18+ years) (%) . This indicator is calculated by the World Health Organization, is presented in the annual report “World Health Statistics 2020: monitoring health for the sustainable development goals” and shows the prevalence of obesity among adults above 18 (lower indicator value is better).

Ramifications of the COVID-19 crisis for the economy and business in the second trimester of 2020 according to Statista:

•  Value of COVID-19 fiscal stimulus packages in G20 countries as of May 2020, as a share of GDP . It shows the package of tax incentives for COVID-19 in G20 countries as of May 2020 as a percentage of GDP;

•  Impact of coronavirus (COVID-19) on economic growth in the Asia Pacific region in 2020, by country . It shows the countrywise impact of coronavirus (COVID-19) on economic growth in the Asia Pacific region in 2020;

•  Percentage of respondents worldwide who felt COVID-19 posed a high level of threat to their job or business as of March 14, 2020, by country . It shows the countrywise percentage of respondents throughout the world who felt that COVID-19 posed a high level of threat to their job or business as of March 14, 2020;

•  Share of adults worldwide who agree business has a responsibility to ensure workers and the community are protected from COVID-19 in March 2020, by country . It shows the countrywise percentage of adults throughout the world who agree business has a responsibility to ensure workers and the community are protected from COVID-19 as of March 2020.

Benefits of the dataset provided by the ISC:

•  Consistency and credibility: collection and systematization of basic statistical data in the common dataset, as well as data analysis with the use of the proprietary methodology for the calculation of the COVID-19 crisis index , which makes it possible to assess the situation in countries across the world and to make international comparisons;

•  Topicality and representativeness: the dataset contains most recent available data (according to the results for the second trimester of 2020), which provide an opportunity to pursue research in a timely fashion and obtain consistent results;

•  Reliability and objectiveness: the dataset combines statistics of reliable sources of statistical and expert analytical data on the subject, which include the World Health Organization, the World Health Map, Yandex.Maps, Our World in Data, and Statista;

•  Informativity: the dataset presents up-to-date international statistics in Russian;

•  Well-defined structure: in order to make working with the dataset more simple, fast and convenient for users, topic-based sections were distinguished in its structure;

•  Availability of patterns: the dataset offers data patterns: integration patterns: G7 countries (developed countries) and BRICS countries (developing countries), CIS countries, EEU countries, geographic patterns of parts of the world, as well as patterns of susceptibility to the COVID-19 crisis , which enable accelerated selection of necessary data for economical experiments aimed at comparing countries of main categories in real-time mode;

•  Data import: the dataset allows selecting necessary information and importing it to Microsoft Excel for the subsequent analysis;

•  Interactivity: the dataset allows creating and visualizing the ratio between the epidemiological situation and the extent of efforts aimed at controlling COVID-19 in countries across the world with the presentation of their category;

•  Intelligent assistance for research activities: the dataset provides an opportunity to perform automatic ranking according to the criterion of activity (as compared to the global practices) in terms of measures taken to control COVID-19 in countries across the world, as well as to visualize the results in the form of a polygon of factors of elimination of the pandemic for clarity;

•  Ranking: the dataset was used as a basis for making the ranking of countries across the world from the perspective of their situation in the context of the COVID-19;

•  Operation on a blockchain principle: first, the data as such are logically structured on a blockchain principle; second, the dataset allows sharing information, changing and processing it upon the request of users; the original data remain unchanged at that, which is very convenient and safe.

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