This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

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.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

Complete annotations for the tabular data are presented below. Tab Fig 1: (A) The heatmap data of G protein family members in the hippocampal tissue of 6-month-old Wildtype (n = 6) and 5xFAD (n = 6) mice; (B) The heatmap data of G protein family members in the cortical tissue of 6-month-old Wildtype (n = 6) and 5xFAD (n = 6) mice; (C) The data in the overlapping part of the Venn diagram (132 elements); (D) The data information for creating volcano plot; (E) The data information for creating heatmap of GPCR-related DEGs; (F) Expression of Gnb5 in the large sample dataset GSE44772; Control, n = 303; AD, n = 387; (H) Statistical analysis of Gnb5 protein levels from panel G; Wildtype, n = 4; 5xFAD, n = 4; (J) Statistical analysis of Gnb5 protein levels from panel I; Wildtype, n = 4; 5xFAD, n = 4; (L) Quantitative analysis of Gnb5 fluorescence intensity in 5xFAD and Wildtype groups; Wildtype, n = 4; 5xFAD, n = 4. Tab Fig 2: (D) qPCR data of Gnb5 knockout in hippocampal tissue; Gnb5F/F, n = 6; Gnb5-CCKO, n = 6; (E–I, L–N) Animal behavioral tests in mice, Gnb5F/F, n = 22; Gnb5-CCKO, n = 16; (E) Total distance traveled in the open field experiment; (F) Training curve in the Morris water maze (MWM); (F-day6) Data from the sixth day of MWM training; (G) Percentage of time spent by the mouse in the target quadrant in the MWM; (H) Statistical analysis of the number of times the mouse traverses the target quadrant in the MWM; (I) Latency to first reach the target quadrant in the MWM; (L) Baseline freezing percentage of mice in an identical testing context; (M) Percentage of freezing time of mice during the Context phase; (N) Percentage of freezing time of mice during the Cue phase. Tab Fig 3: (D–F, H) MWM tests in mice; Wildtype+AAV-GFP, n = 20; Wildtype+AAV-Gnb5-GFP, n = 23; 5xFAD + AAV-GFP, n = 23; 5xFAD + AAV-Gnb5-GFP, n = 26; (D) Training curve in the MWM; (D-day6) Data from the sixth day of MWM training; (E) Percentage of time spent in the target quadrant in the MWM; (F) Statistical analysis of the number of entries in the target quadrant in the MWM; (H) Movement speed of mice in the MWM; (I–K) The contextual fear conditioning test in mice; 5xFAD + AAV-GFP, n = 23; 5xFAD + AAV-Gnb5-GFP, n = 26; (I) Baseline freezing percentage of mice in an identical testing context; (J) Percentage of freezing time of mice during the Context phase; (K) Percentage of freezing time of mice during the Cue phase; (L) Total distance traveled in the open field test; (M) Percentage of time spent in the center area during the open field test. Tab Fig 4: (B, C) Quantification of Aβ plaques in the hippocampus sections from Wildtype and 5xFAD mice injected with either AAV-Gnb5 or AAV-GFP; Wildtype+AAV-GFP, n = 4; Wildtype+AAV-Gnb5-GFP, n = 4; 5xFAD + AAV-GFP, n = 4; 5xFAD + AAV-Gnb5-GFP, n = 4; (B) Quantification of Aβ plaques number; (C) Quantification of Aβ plaques size; (F, G) Quantification of Aβ pylaques from indicted mice lines; WT&Gnb5F/F&CamKIIa-CreERT+Vehicle, n = 4; 5xFAD&Gnb5F/F&CamKIIa-CreERT+Vehicle, n = 4; 5xFAD&Gnb5F/F&CamKIIa-CreERT+Tamoxifen, n = 4; (F) Quantification of Aβ plaque size; (G) Quantification of Aβ plaque number. Tab Fig 5: (B) Overexpression of Gnb5-AAV in 5xFAD mice affects the expression of proteins related to APP cleavage (BACE1, β-CTF, Nicastrin and APP); Statistical analysis of protein levels; n = 4, respectively; (D) Tamoxifen-induced Gnb5 knockdown in 5xFAD mice affects APP-cleaving proteins; Statistical analysis of protein levels; n = 4, respectively; (F) Gnb5-CCKO mice show altered expression of APP-cleaving proteins; Statistical analysis of protein levels; n = 6, respectively. Tab Fig 7: (C, D) Quantification of Aβ plaques in the overexpressed full-length Gnb5, truncated fragments, and mutant truncated fragment AAV in 5xFAD mice; n = 4, respectively; (C) Quantification of Aβ plaques size; (D) Quantification of Aβ plaques number; (F) Effect of overexpressing full-length Gnb5, truncated fragments, and mutant truncated fragment viruses on the expression of proteins related to APP cleavage process in 5xFAD; Statistical analysis of protein levels; n = 3, respectively. (XLSX)

Abstract

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

Geographic coverage

National Coverage

Analysis unit

The main statistical unit to be used for the survey is the establishment. For simple businesses that undertake a single activity at a single location there is a one-to-one relationship between the establishment and the enterprise. For large and complex enterprises, however, it is desirable to separate each activity of an enterprise into establishments to provide the most detailed information possible for industrial analysis. The business register will need to be developed in such a way that records the links between establishments and their parent enterprises. The business register will be created from administrative records and may not have enough information to recognize all establishments of complex enterprises. Large businesses will be contacted prior to the survey post-out to determine if they have separate establishments. If so, the extended structure of the enterprise will be recorded on the business register and a questionnaire will be sent to the enterprise to be completed for each establishment.

SBS has decided to follow the New Zealand simplified version of its statistical units model for the 2009 BAS. Future surveys may consider location units and enterprise groups if they are found to be useful for statistical collections.

It should be noted that while establishment data may enable the derivation of detailed benchmark accounts, it may be necessary to aggregate up to enterprise level data for the benchmarks if the ongoing data used to extrapolate the benchmark forward (mainly VAGST) are only available at the enterprise level.

Universe

The BAS's covered all employing units, and excluded small non-employing units such as the market sellers. The surveys also excluded central government agencies engaged in public administration (ministries, public education and health, etc.). It only covers businesses that pay the VAGST. (Threshold SAT$75,000 and upwards).

Kind of data

Sample survey data [ssd]

Sampling procedure

-Total Sample Size was 1240 -Out of the 1240, 902 successfully completed the questionnaire. -The other remaining 338 either never responded or were omitted (some businesses were ommitted from the sample as they do not meet the requirement to be surveyed) -Selection was all employing units paying VAGST (Threshold SAT $75,000 upwards)

WILL CONFIRM LATER!!

OSO LE MEA E LE FAASA...AEA :-)

Mode of data collection

Mail Questionnaire [mail]

Research instrument

1. General instructions, authority for the survey, etc;
2. Business demography information on ownership, contact details, structure, etc.;
3. Employment;
4. Income;
5. Expenses;
6. Inventories;
7. Profit or loss and reconciliation to business accounts' profit and loss;
8. Fixed assets - purchases, disposals, book values
9. Thank you and signature of respondent.

Supplementary Pages Additional pages have been prepared to collect data for a limited range of industries. 1.Production data. To rebase and redevelop the Industrial Production Index (IPI), it is intended to collect volume of production information from a selection of large manufacturing businesses. The selection of businesses and products is critical to the usefulness of the IPI. The products must be homogeneous, and be of enough importance to the economy to justify collecting the data. Significance criteria should be established for the selection of products to include in the IPI, and the 2009 BAS provides an opportunity to collect benchmark data for a range of products known to be significant (based on information in the existing IPI, CPI weights, export data, etc.) as well as open questions for respondents to provide information on other significant products. 2.Tourism. There is a strong demand for estimates of tourism value added. To estimate tourism value added using the international standard Tourism Satellite Account methodology requires the use of an input-output table, which is beyond the capacity of SBS at present. However, some indicative estimates of the main parts of the economy influenced by tourism can be derived if the necessary data are collected. Tourism is a demand concept, based on defining tourists (the international standard includes both international and domestic tourists), what products are characteristically purchased by tourists, and which industries supply those products. Some questions targeted at those industries that have significant involvement with tourists (hotels, restaurants, transport and tour operators, vehicle hire, etc.), on how much of their income is sourced from tourism would provide valuable indicators of the size of the direct impact of tourism.

Cleaning operations

Partial imputation was done at the time of receipt of questionnaires, after follow-up procedures to obtain fully completed questionnaires have been followed. Imputation followed a process, i.e., apply ratios from responding units in the imputation cell to the partial data that was supplied. Procedures were established during the editing stage (a) to preserve the integrity of the questionnaires as supplied by respondents, and (b) to record all changes made to the questionnaires during editing. If SBS staff writes on the form, for example, this should only be done in red pen, to distinguish the alterations from the original information.

Additional edit checks were developed, including checking against external data at enterprise/establishment level. External data to be checked against include VAGST and SNPF for turnover and purchases, and salaries and wages and employment data respectively. Editing and imputation processes were undertaken by FSD using Excel.

Sampling error estimates

NOT APPLICABLE!!

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

The study data consists of four main components: The Questionnaire in Arabic and English Includes a set of questions designed to collect information related to the study topic. Aims to measure participants' opinions and attitudes based on specific variables. Statistical Analysis Results of Means, Frequencies, and Percentages Present the overall distribution of the sample responses. Provide a quantitative overview of prevailing trends and tendencies among participants. Statistical Analysis Results of ANOVA and T-Test Used to test statistically significant differences between different groups. Determine whether there are substantial differences among the study variables according to the proposed hypotheses. Excel File Containing the Sample Data Entry Includes the raw data extracted from the sample responses. Used for statistical analysis and ensuring the accuracy of results. Significance of These Data These data form the scientific foundation of the study, enabling an accurate understanding of the studied phenomenon by analyzing responses and drawing relevant statistical inferences.

Excel spreadsheet containing, in separate sheets, the underlying data used for the statistical analysis and description of variables.

Created a multi-tab Excel statistical project where I generated synthetic normally-distributed data, built random sample extraction logic, calculated descriptive and inferential statistics, analysed variable correlations and performed linear regression with visualisation.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

The provided dataset is the result of many gears simulations based on complex gears models. In this file, 40000 statistical tolerance analysis results based on Monte-Carlo simulation (MC sample size = 1000000). The following data are labeled via different colors and detailed as follows: (1). 15 inputs parameters (colored in light blue in the Excel file), some are tolerances defined by designers and applied on the gears characteristics as bounders to meet, and some are shifts on key parameters of these gears mainly due to their manufacturing. The last input parameter is the Kinematic Transmission Error (KTE). (2). Three outputs are available (colored in gold in the provided file): the number of non- conformity of each type of produced gears and the most important output the amount of final conformity rate (the most expensive ones).

Description: This dataset consists of field data (arthropods, nematodes and NDVI) collected over the course of 6 field excursions in 2015 and 2016 near TyTy, GA, in a field used for growing Miscanthus x giganteus. It also includes interpolated values of soil measurements collected in 2015 and meteorological data collected on an adjacent farm. Point-in-time measurements include all meteorological, NDVI, arthropod and nematode measurements and their derivatives. Fixed values were measurements that were held constant across all sampling dates, including location, terrain and soils measurements and their derivatives. Dawn Olson and Jason Schmidt collected and processed arthropod count data. Jason Schmidt collected and processed spider count data and computed spider diversity. Richard Davis collected and processed nematode count data. Alisa Coffin collected and processed NDVI data and positional locations. Tim Strickland collected and processed soils data and Alisa Coffin interpolated soils values using kriging to derive values at arthropod sample locations. David Bosch collected and processed meteorological data. Lynne Seymour provided statistical expertise in deriving any estimated values (phloem feeders, parasitoids, spiders, and natural enemies). Alisa Coffin derived terrain data (elevation, slope, aspect, and distances) from publicly available datasets, transformed values (SI, WI, etc), carried out the geographically weighted regression analysis and calculated C:SE values, harmonized the full dataset, and compiled it using Esri's ArcGIS Pro 2.5. Methods for most data are published in the accompanying paper and associated supplements. Questions about dataset development and management should be directed to Alisa Coffin (alisa.coffin@usda.gov). This work was accomplished as a joint USDA and University of Georgia project funded by a cooperative agreement (#6048-13000-026-21S). This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. At request of the author, the data resources are under embargo. The embargo will expire on Fri, Jan 01, 2021. Resources in this dataset:Resource Title: Spreadsheet of data. File Name: GibbsMisFarm_Arthrop_Env_DepVar_201516_final.xlsxResource Description: This workbook contains all of the data used in this analysis. The first worksheet contains data dictionary information.Resource Software Recommended: Microsoft Excel, Office 365,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GeoJSON. File Name: MiscanthusXGiganteusGeoJSON.json

Establishing a robust strategy to account for unintended processing contamination in microplastics research is of interest to the microplastic community who are currently focussed on developing harmonised methods, and to environmental managers who are calling for accurate risk assessment wrt microplastics. Six commonly used 'core' data correction methods were assessed for their suitablity to microplastics research: a) No correction; b) Subtraction; c) Subtraction of mean; d) Spectral Similarity; e) Limits of detection/ limits of quantification (LOD/LOQ) and f) Statistical analysis. An additional 45 variant methods based on these 6 core methods (n=51 in total) were used to correct a dummy microplastics dataset using control data. The dummy microplastics dataset (n=10 identical samples) was created in the laboratory to mimic the laboratory contamination which may arise throughout sample processing and handling. These were free from ‘sample’ matrix but contained processing solutions and MilliQ water as the surrogate sample matrix. Microplastics processing was conducted following AIMS Microplastics SOPs, and polymer type identified by FTIR and confirmed against the Nicodom Polymer Library. Data was analysed in Excel and R. Bayesian analysis was also assessed for suitablity. This work informs work practices for the IMOS long-term microplastic monitoring project, and for all projects conducted by the AIMS microplastics group. This work will also inform the wider microplastics community by starting the conversation towards harmonisation of microplastics data analysis and reporting.

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Households who are rearing livestock or kept poultry

Universe

Livestock or poultry household

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and 3 Livestock/poultry farming housing units were systematically selected and canvassed.

Sampling deviation

No Deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC livestock and poultry questionnaire was divided into the following sections: - Identification/description of holdings - Funds, employment and earnings/wages - Livestock - Poultry - Fixed assets - Sales - Stock - Subsidy

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already enterd data. The completed questionnaires were collected and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

The response rate at EA level was 100 percent, while 99.3 percent was recorded at housing units level.

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

The General Practice Workforce series of Official Statistics presents a snapshot of the primary care general practice workforce. A snapshot statistic relates to the situation at a specific date, which for these workforce statistics is now the last calendar day each month. This monthly snapshot reflects the general practice workforce at 30 November 2024. These statistics present full-time equivalent (FTE) and headcount figures by four staff groups, (GPs, Nurses, Direct Patient Care (DPC) and administrative staff), with breakdowns of individual job roles within these high-level groups. For the purposes of NHS workforce statistics, we define full-time working to be 37.5 hours per week. Full-time equivalent is a standardised measure of the workload of an employed person. Using FTE, we can convert part-time and additional working hours into an equivalent number of full-time staff. For example, an individual working 37.5 hours would be classed as 1.0 FTE while a colleague working 30 hours would be 0.8 FTE. The term “headcount” relates to distinct individuals, and as the same person may hold more than one role, care should be taken when interpreting headcount figures. Please refer to the Using this Publication section for information and guidance about the contents of this publication and how it can and cannot be used. England-level time series figures for all job roles are available in the Excel bulletin tables back to September 2015 when this series of Official Statistics began. The Excel file also includes Sub-ICB Location-level FTE and headcount breakdowns for the current reporting period. CSVs containing practice-level summaries and Sub-ICB Location-level counts of individuals are also available. Please refer to the Publication content, analysis, and release schedule in the Using this publication section for more details of what’s available. We are continually working to improve our publications to ensure their contents are as useful and relevant as possible for our users. We welcome feedback from all users to PrimaryCareWorkforce@nhs.net.

The data presented here have been collected in the context of the EU LIFE EuroLargeCarnivores Outreach Project (LIFE16 GIE/DE/000661). The data set provided is part of a much larger set of data assembled during two different online stakeholder surveys conducted in late 2018/early 2019 (Baseline) and 2021 (Outcome Survey, last year of the project) in 14 countries participating in the project. The data selected are the basis for the analysis and results presented and discussed in the Research Article “Did we achieve what we aimed for? Assessing the outcomes of a human-carnivore conflict mitigation and coexistence project in Europe” by Carol Grossmann and Laszló Pátkó, published in Wildlife Biology in 2024. The dataset is provided as an excel sheet displaying anonymized numerical respondent IDs (rows), and coded answers to selected questions (columns) of these two surveys. The table includes full explanatory wording for all codes used. The data set provided contains n=1262 individual data-subsets from the Baseline Survey and n=1056 individual data subsets from the Outcome Survey in 2021. Part of the questions are identical in both survey sets for direct comparison. Cross references are provided for questions posed in both surveys for comparison but denominated with different numbers in the respective surveys. Part of the questions were posed only in the 2021 survey. Some questions/answers serve as filters for a differentiated analysis according to stakeholder categories, engagement in networking activities, or stakeholder participation and non-participation in project interventions. For more details about the methods of data collection and analysis see Grossmann et al. 2020 and Grossmann and Patko 2024. The reuse potential of this data set lies in the opportunity to assess project outcomes with further stakeholder categories in correlation with respondents’ (non-)participation in project interventions. No further legal or ethical considerations need to be taken, as all individual respondent sets have been fully anonymized. Methods We conducted two online stakeholder surveys in the 14 project partner countries, within the European outreach project "EuroLargeCarnivores". We used google forms for the questionnaires, as mandated from ELC project lead. In late 2018 and early 2019, we conducted a baseline survey (t0) and in 2021 (t0+3), an ‘endline’ survey to assess changes over the project’s lifetime on the stakeholder level. The baseline survey ‘Large Carnivores in Europe 2018’ took place during the first year of the project in all fourteen countries. In 2021, the second comparative stakeholder perception survey ‘Monitoring the Impact’ was launched during the final year of the outreach project in the same distribution range applying the same distribution method. The Forest Research Institute of Baden-Württemberg (FVA) designed, provided, and coordinated both survey questionnaires and data collection procedures, while staff of the regional project partners provided additional preparations, such as translation of the English master questionnaires into the twelve regional languages, as well as the actual data collection. We used a prearranged multi-channel and pyramid distribution system (Atkinson and Flint 2004, Dillman et al 2014, Grossmann et al. 2020). The links to the surveys were distributed via the partners’ systematically updated distribution lists, individual in-person interviews, websites, and social media propagation, offering survey respondents further distribution of the survey through a snowball system, thereby reaching out to as many stakeholders in the 14 project partner countries as possible (Atkinson and Flint 2004, Dillman et al. 2014, Grossmann et al. 2020). After the closure of the surveys, the country datasets were aggregated, re-translated, cleaned and fully coded for analysis. The 2018 survey received n = 1262 returns, the 2021 survey resulted in n = 1056 data, a delta of 16%. Due to the strict enforcement of the European Union’s General Data Protection Regulation (GDPR), we could not address the respondents of Survey 2018 directly again. Additionally, due to the open accessibility of the survey on social media, no concise distribution list of the recipient population is available. We still assumed a comparability of the two datasets for the research questions at hand (Grossmann et al. 2019, 2020). The statistical analysis used descriptive statistics and the X² Test, including Cramer V and Post Hoc tests (differences in standardized residuals) (Cohen 1988, Agresti 2007) for comparing the samples from 2018 and 2021, as well as subsamples of the 2021 sample for a more focused analysis. The analyses were performed using the statistics programs SPSS and Microsoft Excel. For more details about the methods of data collection and analysis see Grossmann et al. 2020 and Grossmann and Patko 2024.