Descriptive statistics for the numerical variables and the p-value corresponding to categorical variables (n = 1000).

IntroductionEffective diabetes self-management and collaborative responsibility sharing with parents are imperative for pediatric patients with type 1 diabetes mellitus, particularly as they gradually assume more self-care responsibilities. The primary goal of this study was to assess differences in adherence to self-care activities regarding sociodemographics and clinical characteristics in pediatric patients with type 1 diabetes. The secondary goal of this study was to understand the level of parental involvement in diabetes management and to assess the pediatric patients’ behaviors (independent or dependent on disease self-management) that relate to sociodemographic and clinical characteristics.MethodsThis was a comparative cross-sectional and correlational study. The study sample included 182 children and adolescents who had been diagnosed with type 1 diabetes at least 3 months prior. Data collection instruments included a sociodemographic and questionnaire about Adherence to self-care activities and parental involvement in diabetes self-management, as well as a documentation sheet for recording clinical data.ResultsA majority of participants (71%) exhibited non-adherence to self-care tasks, despite 78.0% asserting their independence in diabetes self-management. Notably, insufficient parental involvement in administering insulin therapy significantly predicted severe hypoglycemic episodes.ConclusionsPediatric patients dealing with type 1 diabetes demonstrate a substantial degree of autonomy in managing their condition, paradoxically coupled with self-reported non-adherence to critical self-care responsibilities. Notably, children (aged 8–12) rely more heavily on parental support, especially concerning insulin therapy administration. The study underscores the crucial role of parental engagement in insulin therapy, as its deficiency significantly predicts the likelihood of severe hypoglycemic episodes.

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.

STAD-R is a set of R programs that performs descriptive statistics, in order to make boxplots and histograms. STAD-R was designed because is necessary before than the thing, check if the dataset have the same number of repetitions, blocks, genotypes, environments, if we have missing values, where and how many, review the distributions and outliers, because is important to be sure that the dataset is complete and have the correct structure for do and other kind of analysis.

Most studies in the life sciences and other disciplines involve generating and analyzing numerical data of some type as the foundation for scientific findings. Working with numerical data involves multiple challenges. These include reproducible data acquisition, appropriate data storage, computationally correct data analysis, appropriate reporting and presentation of the results, and suitable data interpretation.Finding and correcting mistakes when analyzing and interpreting data can be frustrating and time-consuming. Presenting or publishing incorrect results is embarrassing but not uncommon. Particular sources of errors are inappropriate use of statistical methods and incorrect interpretation of data by software. To detect mistakes as early as possible, one should frequently check intermediate and final results for plausibility. Clearly documenting how quantities and results were obtained facilitates correcting mistakes. Properly understanding data is indispensable for reaching well-founded conclusions from experimental results. Units are needed to make sense of numbers, and uncertainty should be estimated to know how meaningful results are. Descriptive statistics and significance testing are useful tools for interpreting numerical results if applied correctly. However, blindly trusting in computed numbers can also be misleading, so it is worth thinking about how data should be summarized quantitatively to properly answer the question at hand. Finally, a suitable form of presentation is needed so that the data can properly support the interpretation and findings. By additionally sharing the relevant data, others can access, understand, and ultimately make use of the results.These quick tips are intended to provide guidelines for correctly interpreting, efficiently analyzing, and presenting numerical data in a useful way.

Descriptive statistics of the numeric variables (using all 30372 patents).

Descriptive statistics (number of participants (N), percentages (%), means and Standard Deviations (SD) and) for the three time points (T0-2), changes between two time points (T1-T0, T2-T1) and the time-effects.

Numerical data underlying graphs and summary statistics presented in the main figures.

aSex coded as: 1 =  male, 2 =  female.bBased on a total of 60 items.cBased on a total of 20 items.dBased on a total of 12 correct prices.

Data Description:

Customer ID: --- Customer/Buyers Identity Number *Age: * --- Age of the customer Age Group: --- Age Category Postcode: --- Customer Location Gender: ---- Sex Favourite Cookie: --- Cookie Purchased Cookies bought each week: --- No. of Cookies bought each week.

The data quality monitoring system (DQMS) developed by the Satellite Oceanography Program at the NOAA National Centers for Environmental Information (NCEI) is based on the concept of a Rich Inventory developed by the previous NCEI Enterprise Data Systems Group. The principal concept of a Rich Inventory is to calculate the data Quality Assurance (QA) descriptive statistics for selected parameters in each Level-2 data file and publish the pre-generated images and NetCDF-format data to the public. The QA descriptive statistics include valid observation number, observation number over 3-sigma edited, minimum, maximum, mean, and standard deviation. The parameters include sea surface height anomaly, significant wave height, altimeter, and radiometer wind speed, radiometer water vapor content, and radiometer wet tropospheric correction from Jason-3 Level-2 Final Geophysical Data Record (GDR) and Interim Geophysical Data Record (IGDR) products.

BAT = Balance and Tone; 1× RT = once-weekly resistance training; 2× RT = twice- weekly resistance training; BMI = weight in kilograms/height in square meters; Baseline Stroop and Trial completion Stroop performance = Stroop color words condition subtracted by Stroop coloured x's condition; Δ in Stroop = Stroop Baseline subtracted by Trial completion Stroop; Δ in Sub-total fat mass = Final fat mass subtracted by Baseline fat mass (a positive number represents an increase in fat mass and a negative number represents a decrease in fat mass); Δ in Sub-total lean mass = Final lean mass subtracted by Baseline lean mass (a positive number represents an increase in lean mass and a negative number represents a decrease in lean mass).

The data informs from the ballistic strength training feasibility study. Data includes descriptive statistics and pre-test post-test difference reports. The data was analysed with statistical support using R software. A data collection spreadsheet is also included for the scoping review completed as part of the dissertation.

Stack Overflow Dataset Preprocessing

Preprocessing Steps

1. Initial Cleaning:

   • Removed unnecessary 'Unnamed: 0' column.
   • Handled missing values in 'HaveWorkedWith' by replacing them with 'None'.
   • Removed duplicate rows.

2. Encoding Categorical Variables:

   • Applied one-hot encoding to the following categorical columns: 'Age', 'Accessibility', 'EdLevel', 'Gender', 'MentalHealth', 'MainBranch', and 'Country'. This significantly increased the number of columns in the dataset.

3. Text Data Processing:

   • Transformed the 'HaveWorkedWith' column, which contains technologies or tools, into binary features. Each unique technology/tool mentioned in the column was converted into a separate binary feature, indicating whether a respondent has worked with that technology/tool.

4. Normalization/Standardization:

   • Standardized the numerical columns ('YearsCode', 'YearsCodePro', 'PreviousSalary', 'ComputerSkills') to have a mean of 0 and a standard deviation of 1. This is a common practice in data preprocessing for machine learning algorithms.

Additional Notes

• The dataset is now in a format suitable for various data analysis and machine learning applications.
• The original dataset columns have been significantly transformed, with many new columns added due to one-hot encoding and the processing of the 'HaveWorkedWith' column.

Descriptive Statistics for Numerical Columns:

Employment:

Most respondents are employed (mean close to 1).

YearsCode

Respondents have about 14 years of coding experience, ranging from 0 to 50 years.

YearsCodePro

Average professional coding experience is around 9 years.

PreviousSalary

The average previous salary is approximately $67,751, but there's significant variation (standard deviation is around $49,488).

ComputerSkills

Average score is 13.43, ranging from 0 to 107.

Distribution of Key Categorical Variables:

Age

Majority of respondents are under 35 years old.

Gender

A smaller proportion of respondents are women (3,518).

Country

The dataset includes respondents from widely-ranging countries, with varying frequencies.

Education Level (EdLevel)

Various education levels are represented, but exact counts are not displayed here.

Employment

Most respondents (64,871) are employed.

• Automated parametric analysis workflow built using R Studio.
• Demonstrates core statistical analysis methods on numerical datasets.
• Includes step-by-step R scripts for performing t-tests, ANOVA, and summary statistics.
• Provides visual outputs such as boxplots and distribution plots for better interpretation.
• Designed for students, researchers, and data analysts learning statistical automation in R.
• Useful for understanding reproducible research workflows in data analysis.
• Dataset helps in teaching how to automate statistical pipelines using R programming.

ScienceCentral is a free or open access full-text archive of scientific society journal literature hosted by the Korean Federation of Science and Technology Societies. It was launched in December 2013. We analyzed the number of articles deposited, page views by period, country of visitors, number of visitors, and entry point of visits. Descriptive statistics were presented. We also hypothesized that visitors accessed ScienceCentral mostly through Google and Google Scholar since ScienceCentral allows Googlebot to index it. The number of deposited articles was 19,419 from 124 journals in December 2016. The number of page views per month was 20,228 in December 2016. The top countries of visitors were South Korea (39.9%), the United States (13.26%), India (4.2%), China (3.4%), and Russia (3.2%). The average number of page views per article a month in December 2016 was 1.0. Google and Google Scholar were powerful referral sites to ScienceCentral. Except for direct visits to ScienceCentral, seven out of the top ten access sites to ScienceCentral were Google or Google Scholar sites from a variety of countries. Although the number of visitors and page views has increased continuously, the average number of page views per article a month has not increased.

This database of selected borehole records from the Yamal Peninsula, Russia, contains environmental descriptions (textual and numerical) of the units on the index map, and relevant borehole data. The Index Map of Yamal Peninsula (VSEGINGEO-Earth Cryosphere Institute SB RAS; PI - Prof.E.S.Melnikov) was originally compiled at a scale of 1 to 1,000,000, as 'The Map of Natural Complexes of West Siberia for the Purpose of Geocryological Prediction and Planning of Nature-Protection Measures for the Mass Construction, 1 to 1 mln' (1991) by E.S.Melnikov and N.G.Moskalenko (eds.). It was taken as a base map for nature-protection regionalization. Environmental 'regions', 'sub-regions', 'landscapes' and localities' shown on a landscape map are merged into the nature-protection regions. The map was compiled by interpreting more than 1000 satellite images and aerial photos as well as from analysis of field data from several institutions. Dominating components of the landscape, composition of the surface deposits, geocryological conditions and natural protection of ground water were considered while distinguishing the Nature-Protection Regions within the limits of Environmental Regions (Melnikov, 1988). The map is supplied with relevant databases, containing the following information - number of regions and landscape type; category of resiliency; category of the ground water protection; vegetation type; geological and geocryological structure to the depth of 10-15 m; ice content (of lenses and of macro-inclusions separately); thickness of seasonally frozen and seasonally thawed layers; ground temperature; contemporary exogenic geological (periglacial) processes; and the area affected by these processes.The 55 nature-protection regions of Yamal Peninsula generalize information. To approve the ranges of geocryological and cryolithological characteristics, 160 boreholes were retrieved out of the database containing more than 4000 boreholes data obtained in 1977-1990 by Fundamentproekt Design Institute (Moscow, Russia; PI - Dr.sci.M.A.Minkin) at Kharasavey and Bovanenkovo gas fields and along the pipelines Yamal-Ukhta and Yamal-Uzhgorod. The boreholes have reference to geographical coordinates (latitude and longitude), as well as to the nature-protection region numbers shown on the Index Map. A total of 21 units are covered by borehole data, 5-8 boreholes in each unit, covering most typical conditionsThe original database consisted of 3 relational tables. The first table includes category of resiliency; locality type description; landscape type description; ground-ice content, water saturation, cryogenic structure, macro-ground-ice content; vegetation types; seasonally frozen and seasonally thawed layer depths; ground temperature at 10 m; exogenic geological processes an their paragenesis and combinations; and degree of the surface disturbance. The second relational table contains layer-by-layer description of the lithological section types. The third table for the boreholes includes the description of topography around the borehole; types of geological profiles through the active layer and depths down to the permafrost table; ground temperature at 10-m depth (close to the depth of zero annual amplitude in the area); macro-ice content; and salinity of permafrost. These data are presented on the CAPS Version 1.0 CD-ROM, June 1998.

Key Table Information.Table Title.Information: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2251BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesRange indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, Combined Statistical Area, Metropolitan and Micropolitan Statistical Area, Metropolitan Division, Consolidated City, County (and equivalent), and Economic Place (and equivalent; incorporated and unincorporated) levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels and selected 7-digit 2022 NAICS-based code levels. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data Dictionary..Data-Specific Notes.Data users who create their own estimates us...

Standardized test descriptive statistics: number of participants, mean, standard deviation, median and interquartile range.

GWAS on EBV serostatus/total number of siblings on UKBiobank participants Mendelian randomisation identifies priority groups for prophylactic EBV vaccination. Marisa D. Muckian, MSc; James. F Wilson, DPhil; Graham S. Taylor, PhD; Helen R. Stagg, PhD; Nicola Pirastu, PhD We mapped the complex evidence from the literature prior to this study factors associated with EBV serostatus (as a proxy for infection) into a causal diagram to determine putative risk factors for our study. Using data from the UK Biobank of 8,422 individuals genomically deemed to be of white British ancestry between the ages of 40 and 69 at recruitment between the years 2006 and 2010, we performed a genome wide association study (GWAS) of EBV serostatus and total number of sibligns, followed by a Two Sample MR to determine which putative risk factors were causal.