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.

• About Dataset Safa S. Abdul-Jabbar, Alaa k. Farhan

• Context This is the first Dataset for various ordinary patients in Iraq. The Dataset provides the patients’ Cell Blood Count test information that can be used to create a Hematology diagnosis/prediction system. Also, this Data was collected in 2022 from Al-Zahraa Al-Ahly Hospital. These data can be cleaned & analyzed using any programming language because it is provided in an excel file that can be accessed and manipulated easily. The user just needs to understand how rows and columns are arranged because the data was collected as images(CBC images) from the laboratories and then stored the extracted data in an excel file.  Content This Dataset contains 500 rows. For each row (patient information), there are 21 columns containing CBC test features that can be described as follows:

• ID: Patients Identifier

• WBC: White Blood Cell, Normal Ranges: 4.0 to 10.0, Unit: 10^9/L.

• LYMp: Lymphocytes percentage, which is a type of white blood cell, Normal Ranges: 20.0 to 40.0, Unit: %

• MIDp: Indicates the percentage combined value of the other types of white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 1.0 to 15.0, Unit: %

• NEUTp: Neutrophils are a type of white blood cell (leukocytes); neutrophils percentage, Normal Ranges: 50.0 to 70.0, Unit: %

• LYMn: Lymphocytes number are a type of white blood cell, Normal Ranges: 0.6 to 4.1, Unit: 10^9/L.

• MIDn: Indicates the combined number of other white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 0.1 to 1.8, Unit: 10^9/L.

• NEUTn: Neutrophils Number, Normal Ranges: 2.0 to 7.8, Unit: 10^9/L.

• RBC: Red Blood Cell, Normal Ranges: 3.50 to 5.50, Unit: 10^12/L

• HGB: Hemoglobin, Normal Ranges: 11.0 to 16.0, Unit: g/dL

• HCT: Hematocrit is the proportion, by volume, of the Blood that consists of red blood cells, Normal Ranges: 36.0 to 48.0, Unit: %

• MCV: Mean Corpuscular Volume, Normal Ranges: 80.0 to 99.0, Unit: fL

• MCH: Mean Corpuscular Hemoglobin is the average amount of haemoglobin in the average red cell, Normal Ranges: 26.0 to 32.0, Unit: pg

• MCHC: Mean Corpuscular Hemoglobin Concentration, Normal Ranges: 32.0 to 36.0, Unit: g/dL

• RDWSD: Red Blood Cell Distribution Width, Normal Ranges: 37.0 to 54.0, Unit: fL

• RDWCV: Red blood cell distribution width, Normal Ranges: 11.5 to 14.5, Unit: %

• PLT: Platelet Count, Normal Ranges: 100 to 400, Unit: 10^9/L

• MPV: Mean Platelet Volume, Normal Ranges: 7.4 to 10.4, Unit: fL

• PDW: Red Cell Distribution Width, Normal Ranges: 10.0 to 17.0, Unit: %

• PCT: The level of Procalcitonin in the Blood, Normal Ranges: 0.10 to 0.28, Unit: %

• PLCR: Platelet Large Cell Ratio, Normal Ranges: 13.0 to 43.0, Unit: %

• Acknowledgements We thank the entire Al-Zahraa Al-Ahly Hospital Hospital team, especially the hospital manager, for cooperating with us in collecting this data while maintaining patients' confidentiality.

Additional file 4: Results of the replicated PCR experiments to test for normality of Ct distribution. An Excel file that lists the 9 GPCRs that were used for replicated PCR experiments on genomic DNA as well as the results of the statistical analysis conducted to determine if Ct distributions displayed on Additional files 5, 6, 7, 8, 9, 10, 11, 12 and 13 are Gaussian (XLS 12 KB)

Materials and Methods The study was held in the Oral and Maxillofacial Surgery department and Kasturba Hospital, Manipal, from November 2019 to October 2021 after approval from the Institutional Ethics Committee (IEC: 924/2019). The study included patients between 18-70 years. Patients with associated diseases like cysts or tumors of the jaw bones, pregnant women, and those with underlying psychological issues were excluded from the study. The patients were assessed 8-12 weeks after surgical intervention. A data schedule was prepared to document age, sex, and fracture type. The study consisted of 182 subjects divided into two groups of 91 each (Group A: Mild to moderate facial injury and Group B: Severe facial injury) based on the severity of maxillofacial fractures and facial injury. Informed consent was obtained from each of the study participants. We followed Facial Injury Severity Scale (FISS) to determine the severity of facial fractures and injuries. The face is divided horizontally into the mandibular, mid-facial, and upper facial thirds. Fractures in these thirds are given points based on their type (Table 1). Injuries with a total score above 4.4 were considered severe facial injuries (Group A), and those with a total score below 4.4 were considered mild/ moderate facial injuries (Group B). The QOL was compared between the two groups. Meticulous management of hard and soft tissue injuries in our state-of-the-art tertiary care hospital was implemented. All elective cases were surgically treated at least 72 hours after the initial trauma. The facial fractures were adequately reduced and fixed with high–end Titanium miniplates and screws (AO Principles of Fracture Management). Soft tissue injuries were managed by wound debridement, removal of foreign bodies, and layered wound closure. Adequate pain-relieving medication was prescribed to the patients postoperatively for effective pain control. The QOL of the subjects was assessed using the 'Twenty-point Quality of life assessment in facial trauma patients in Indian population' assessment tool. This tool contains 20 questions and uses a five-point Likert response scale. The Twenty – point quality of life assessment tool included two zones: Zone 1 (Psychosocial impact) and Zone 2 (Functional and esthetic impact), with ten questions (domains) each (Table 2). The scores for each question ranged from 1- 5, the higher score denoting better Quality of life. Accordingly, the score in each zone for a patient ranged from 10 -50, and the total scores of both zones were recorded to determine the QOL. The sum of both zones determined the prognosis following surgery (Table 2). The data collected was entered into a Microsoft Excel spreadsheet and analyzed using IBM SPSS Statistics, Version 22(Armonk, NY: IBM Corp). Descriptive data were presented in the form of frequency and percentage for categorical variables and in the form of mean, median, standard deviation, and quartiles for continuous variables. Since the data were not following normal distribution, a non-parametric test was used. QOL scores were compared between the study groups using the Mann-Whitney U test. P value < 0.05 was considered statistically significant.

This metadata record contains the results from 11 bioassays conducted with 2 species of Antarctic marine microalgae. Seven tests were conducted with Phaeocystis antarctica (Prymnesiophyceae), assessing the toxicity of copper, cadmium, lead, zinc and nickel. Four tests were conducted with Cryothecomonas armigera (Incertae sedis), assessing the toxicity of copper only.

Test conditions for both algae are described in the excel spreadsheets. In summary, tests for P. antarctica and C.armigera, were carried out at 0 plus or minus 2 degrees C, 20:4 h light:dark (150-200 micro mol/m2/s, cool white 36W/840 globes), in natural filtered (0.45 microns for P.antarctica and 0.22 microns filtered for C. armigera) seawater (salinity - 35 ppt, pH - 8.1 plus or minus 0.2). For both species, filtered seawater was supplemented with 1.5 mg/L NO3- and 0.15 mg/L of PO43-. All tests were carried out in silanised 250-mL glass flasks, with glass lids. Test volumes for P.antartica and C.armigera were 50 mL and 80 mL, respectively.

All tests consisted of 3-5 metal treatments, with 3 replicates per treatment, alongside 3 replicate controls (natural filtered seawater). Seawater was spiked with metal solutions to achieve required concentration. Concentrations tested are recorded in excel datasheets. The following replicate toxicity tests were completed for P. antarctica: - 5 tests with copper (1-20 micro g/L) - 4 tests with lead (10-500 micro g/L) - 3 tests with cadmium (100-2000 micro g/L) - 3 tests with zinc (100-2000 micro g/L) - 3 tests with nickel (200-1000 micro g/L) For C. armigera, 1 rangefinder test was carried out testing 6 concentrations (1-100 micro g/L), and 3 definitive test, with 5 concentrations (15-100 micro g/L).

The age of P. antarctica and C.armigera at test commencement was 8-12 days, and 25-30 days, respectively. Algal cells were centrifuged and washed to remove nutrient rich media, and test flasks were inoculated with between 1-3 x103 cells/mL.

Cell densities in all toxicity tests were determined by flow cytometry. The flow cytometer was also used to simultaneously measure change sin chlorophyll a fluorescence intensity, cell size and internal cell granularity.

Toxicity tests were continued until cell densities in the control treatments had increased 16-fold. Toxicity tests with P. antarctica were carried out over 10 days, with cell densities in each replicate flask measured every 2 days. Toxicity tests with C. armigera were carried out over 23-24 days, with cell densities determined twice a week. The growth rate (cell division; u) was calculated as the slope of the regression line from a plot of log10 (cell density) versus time (h). Growth rates for all treatments were expressed as a percentage of the control growth rates.

The pH in all treatments was measured on the first and last day of the test, as well as on day 6 for P. antarctica tests and an additional two times per week for C. armigera tests. Sub-samples (5 mL) for analysis of dissolved metal concentrations were taken from each treatment on days 0, 6 and 10 for P. antarctica tests, and on days 0, 7, 14, 21, and 24 for C. armigera tests. Sub-samples were filtered through an acid washed (10% HNO3, Merck) 0.45-micron membrane filter and syringe, and acidified to 0.2% with Tracepur nitric acid (Merck). All toxicity test results were calculated using measured dissolved metal concentrations, which were determined using inductively coupled plasma-atomic emission spectrometry (ICP-AES; Varian 730-ES) for Cu, Cd, Pb, Ni and Zn and using inductively coupled plasma-mass spectrometry (ICP-MS; Agilent 7500CE) for lowest concentration Cu samples (nominal concentration 1 micro g/L). Detection limits for Cu, Cd, Pb, Ni and Zn were 1, 0.12, 1.7, 1.2 and 0.1 micro g/L, respectively (ICP-AES) and 0.05 micro g/L (ICP-MS) for low concentration Cu samples. The specific growth rates (u) and corresponding measured metal concentrations were used to calculate toxicity test values using Toxcalc (Version 5.0.23, TidePool Scientific Software, San Francisco, CA, USA). Data were tested for normal distribution using Shapiro-Wilk's test (p greater than 0.01); and equal variances using Bartlett's test (p = 0.09). The inhibitory concentration which reduced population growth rate by x% (ICx) compared to controls was calculated using linear interpolation. The Dunnett's multiple comparison test was used to determine which treatments were significantly different to the control (2 tailed, p less than or equal to 0.05), and to calculate the no observable effect concentration (NOEC) and the lowest observable effect concentration (LOEC).

Data for each toxicity test are provided in individual excel spreadsheets, identified by the species tested, the test number for that species and the date the test started. A summary table of details for the 11 tests is provided in the file: Summary table.xlsx. The first worksheet for each test file is titled "Test Conditions". This sheet provides information on the toxicity test e.g. species and metals tested, dates, test conditions, as well as explanation of abbreviations, definitions of toxicity values etc. The second worksheet includes the raw cell densities determined in each flask, the calculated growth rates, and the measured pH and metal concentrations. For C. armigera data sheets, there is an additional worksheet, "Measured Cu and pH" which includes all measured pH values and metal concentrations across the 24-day period. Following the growth rate sheets are the statistical outputs for each metal, which were all generated using Toxcalc. Finally, if additional cellular parameters were measured (Chlorophyll a fluorescence, cell size and internal cell granularity), the raw data for each parameter is include in a worksheet, "Metal cellular parameters".

Data were collected in an Australian laboratory (CSIRO Land and Water, Centre for Environmental Contaminants Research, Lucas Heights, 2234, NSW) during May 2013 - April 2014. The tests used microalgal strains that had been previously collected from the Southern Ocean and are cultured within the microalgal collection at the Australian Antarctic Division (AAD). Daughter daughter cultures were transferred to CSIRO, where they were cultured for this work.

IntroductionChildhood illnesses, such as acute respiratory illness, fever, and diarrhoea, continue to be public health problems in low-income countries. Detecting spatial variations of common childhood illnesses and service utilisation is essential for identifying inequities and call for targeted actions. This study aimed to assess the geographical distribution and associated factors for common childhood illnesses and service utilisation across Ethiopia based on the 2016 Demographic and Health Survey.MethodsThe sample was selected using a two-stage stratified sampling process. A total of 10,417 children under five years were included in this analysis. We linked data on their common illnesses during the last two weeks and healthcare utilisation were linked to Global Positioning System (GPS) information of their local area. The spatial data were created in ArcGIS10.1 for each study cluster. We applied a spatial autocorrelation model with Moran’s index to determine the spatial clustering of the prevalence of childhood illnesses and healthcare utilisation. Ordinary Least Square (OLS) analysis was done to assess the association between selected explanatory variables and sick child health services utilisation. Hot and cold spot clusters for high or low utilisation were identified using Getis-Ord Gi*. Kriging interpolation was done to predict sick child healthcare utilisation in areas where study samples were not drawn. All statistical analyses were performed using Excel, STATA, and ArcGIS.ResultsOverall, 23% (95CI: 21, 25) of children under five years had some illness during the last two weeks before the survey. Of these, 38% (95%CI: 34, 41) sought care from an appropriate provider. Illnesses and service utilisation were not randomly distributed across the country with a Moran’s index 0.111, Z-score 6.22, P