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Dataset “Big Data for digital monitoring of biodiversity, agriculture and food security – 2020”

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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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Datset ISC

Dataset “Big Data of the Modern Global Economy: Digital Platform for Data Mining – 2020”

The dataset contains indicators for the most relevant lines of economic research: industry 4.0, knowledge economy, economic growth and sustainable development. The values of indicators in the countries of the world for 2019 are given, forecasts of indicators for 2020-2024 are compiled. The dataset generates Big Data of the modern global economy and constitutes a digital platform for data mining. Author of the dataset: doctor of economics, professor E.G. Popkova

DataSet “Interactive Statistics and Intelligent Analytics of the Balanced State of the Regional Economy of Russia in Terms of Big Data and Blockchain – 2020”

The dataset contains statistics on the topic of balancing the Russian regional economy for 2005-2019 and forecast for 2020-2024. It reflects the level and potential of the socio-economic development of Russian regions, presents their “funnel of backwardness” calculated according to the author’s method of doctor of economics, professor E.G. Popkova. The matrix “TRMS” and the rating of socio-economic development of the regions are compiled. An interactive map of the regions and federal districts of Russia is presented, it is possible to automatically build blockchain polygons of their socio-economic development. Author of the data set: doctor of economics, professor E.G. Popkova

Dataset “Social Entrepreneurship in the World Economy: a Path from Virtual Scores to Big Data – 2020”

The dataset contains statistics on the topic of the socially responsible business and its evaluation using a specifically developed index. The values ​​of indicators of social responsibility of the business and indicators of non-profit activities of the business in 2020 have been presented. A rating of the socially responsible business in the countries of the world in 2020 has been compiled. The dataset allows creating and visualizing interactive profiles of countries all over the world based on the level of development of the socially responsible business. Authors of the dataset: Doctor of Economics, Professor Elena G., Doctor of Economics, Professor Bruno S. Sergi.

DataSet “Epidemics and Pandemics: Big Data for the Scientific Analytics of the Dynamics of Infectious Diseases throughout the World and their Consequences”

The dataset integrates all relevant statistics on the topic of infectious diseases and their consequences. It gives consideration to the temporal dynamics of exacerbation of all infectious diseases distinguished by the World Health Organization throughout the world (from earlier than 1960 till 2020), as well as key indicators of the global economy based on data from the World Bank (for 1960-2018) and the International Monetary Fund (forecasts for 2019-2020). Firstly, the ordered data on the topic of infectious diseases in the world are presented, secondly, countrywide statistics on the incidence rate of infections that are most relevant today, and thirdly, statistics on the key indicators of the economy and health care, statistics on the key indicators of the potential economic impact of epidemics and pandemics, as well as countrywide and worldwide statistics on preparedness for epidemics and pandemics. COVID- 2019 incidence patterns have been made for various countries of the world. The opportunity was provided to automatically create and visualize virtual profiles of countries against the background of the COVID-2019 pandemic in 2020 The ranking of countries against the background of the COVID-2019 pandemic in 2020 was made;The part of the reported study written by Agnessa O. Inshakova was performed by a grant from the Russian Science Foundation (Grant No. 20-18-00314). Author of the dataset: doctor of economics, professor E.G. Popkova

Data set “Corporate social responsibility, sustainable development, and fighting climate change: imitation modeling and neural network analysis in regions of the world – 2020”

The dataset contains the indicators on the implemented practices of corporate social responsibility, sustainable development, and fighting climate changes (2020). The index of corporate fight against climate change is provided. The models of categories of countries by the criterion of sustainable development and fighting climate change are presented. A sustainable development ranking and fighting climate changes ranking are compiled based on corporate social and ecological responsibility in countries of the world in 2020 The cognitive map shows the results of imitation modeling and neural network analysis of the ratio of regulatory and market factors in provision of corporate and ecological responsibility, achievement of sustainable development, and fighting climate changes in geographical regions of the world in 2020 The data has been developed at the Chair “Economic policy and public-private partnership” of Moscow State Institute of International Relations (MGIMO). Authors of the dataset: Elena B. Zavyalova (Ph.D., associate professor, head of the Chair), Tatyana G. Krotova (expert of the Center for Applied Research), and Elena G. Popkova (professor, doctor of economics, leading researcher of the Center for Applied Research).

Dataset “Humanization of economic growth in the global economy: Big Data and digital modeling – 2020”

The data set was developed by Elena G. Popkova, D.Sc. Economics, Professor, President of the Institute of Scientific Communications and by Natalya N. Chubaeva, post-graduate student of MGIMO University

Dataset “Big Data for digital monitoring of biodiversity, agriculture and food security – 2020”

The dataset includes biodiversity indicators from the perspective of conservation of terrestrial ecosystems and marine ecosystems based on the data from the UN for 2015-2019, agriculture based on the data from the World Bank for 2017- 2018, and food security based on the data from The Economist Intelligence Unit Limited for 2019 The proprietary methodology was used to classify and make the models of countries according to the criterion of conservation of biodiversity, agricultural development, and food security; in addition, the wealth index of the animal and plant world was calculated, and the ranking of countries of the world was made according to the value of this index in 2020 The dataset makes it possible to carry out digital monitoring of biodiversity, agriculture and food security across the world with automatic construction of graphs. Author of the dataset: Doctor of Economics, Professor Elena G. Popkova.

Dataset “COVID-19 and the 2020 Crisis: Healthcare System Capabilities and Ramifications for the Economy and Business all Over the World”

The dataset includes the indicators of the COVID-19 pandemic according to the World Health Organization and the World Health Map, measures to control the COVID-19 virus threat, as well as ramifications of the COVID-19 crisis for the economy and business according to Statista. All the data are up- to-date as of the second trimester of 2020 The proprietary methodology has been used to classify and create country profiles according to the criterion of susceptibility to the COVID-19 crisis, the COVID-19 crisis index has been calculated, and the global ranking of countries across the world from the perspective of their situation in the context of the COVID-19 crisis. The dataset provides an opportunity to perform automatic ranking according to the criterion of activity 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. Author: Prof. Elena G. Popkova.

Public repository for datasets

Data set of tension of the global oil market under the influence of the COVID-19 pandemic in 2020 Update 10.10.2020 Download file Data set of fluctuation of currency markets during the COVID-19 pandemic in 2020 Update 12.10.2020 Download file Dataset on Development of IT Companies in Russia Amid the COVID-19 Crisis Update 14.10.2020 Download file

         Вятский государственный университет Ставропольский государственный аграрный университет