To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

In "Sample Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described (CrP Sample Dataset, Glycolytic Dataset, Oxidative Dataset). Additionally, there are three sheets with sample graphs created using one of the three datasets (CrP Sample Graph, Glycolytic Graph, Oxidative Graph). Each dataset and graph pairs are from different subjects. · CrP Sample Dataset and CrP Sample Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Glycolytic Dataset and Glycolytic Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Oxidative Dataset and Oxidative Graph: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a sustained, light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.

The City of Oslo has collected a number of statistical areas in a common statistics bank. The tables in StatBank provide the ability to generate graphs and Excel files. The areas with underlying themes are (as of 27.01.2017): Population (People, Births, Death, Relocation, Country background, Households, Population projections) Income, living conditions and social conditions (Income, Social services, Child protection, Education level, Trangbodness, Crime and legal conditions, Other living goals) Kindergarten, School and higher education (Children’s gardens, Primary school, Primary school, upper secondary school, Adult education, Higher education) Oslohelsa (Health status, Health-related behaviour, community health services, Care and care services) Social life (Elective results, Social contact, Trivsel i Oslo, Faith, life and ceremonies, Culture and sports) Housing, construction, labour and business (Næringsliv, Employment and unemployed, commuting, Housing, Building activities) Environment and transport (Climate and energy, Air quality, Traffic noise and quiet areas, travel media distribution, Car and public transport, Air and ship traffic, Nature and biodiversity, Areas and free areas, Water and watercourses, Waste and recycling, Polluted land, Radon) Municipal economy and management (Economy and accounting, Municipal employees) The City of Oslo has collected a number of statistical areas in a common statistics bank. The tables in StatBank provide the ability to generate graphs and Excel files. The areas with underlying themes are (as of 27.01.2017): Population (People, Births, Death, Relocation, Country background, Households, Population projections) Income, living conditions and social conditions (Income, Social services, Child protection, Education level, Trangbodness, Crime and legal conditions, Other living goals) Kindergarten, School and higher education (Children’s gardens, Primary school, Primary school, upper secondary school, Adult education, Higher education) Oslohelsa (Health status, Health-related behaviour, community health services, Care and care services) Social life (Elective results, Social contact, Trivsel i Oslo, Faith, life and ceremonies, Culture and sports) Housing, construction, labour and business (Næringsliv, Employment and unemployed, commuting, Housing, Building activities) Environment and transport (Climate and energy, Air quality, Traffic noise and quiet areas, travel media distribution, Car and public transport, Air and ship traffic, Nature and biodiversity, Areas and free areas, Water and watercourses, Waste and recycling, Polluted land, Radon) Municipal economy and management (Economy and accounting, Municipal employees)