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.

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.

This report from the GLA Intelligence Unit compares 2011 census estimates of the population aged 0-18 to the following alternative data sources:

• ONS 2010 based sub-national population projections (SNPP);

• GLA 2011 round population projections;

• General Practitioner registrations; and

• Child benefit claims.

The report is available to download here.

An Excel file containing the data behind charts and tables in the report is available to download here

This repository describes the dataset used for intraspecific (among individuals) and intraspecific (between species) comparisons, and data for female-entrance analysis.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

The dataset includes a PDF file containing the results and an Excel file with the following tables: Table S1 Results of comparing the performance of MetaFetcheR to MetaboAnalystR using Diamanti et al. Table S2 Results of comparing the performance of MetaFetcheR to MetaboAnalystR for Priolo et al. Table S3 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool using Diamanti et al. Table S4 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool for Priolo et al. Table S5 Data quality test results for running 100 iterations on HMDB database. Table S6 Data quality test results for running 100 iterations on KEGG database. Table S7 Data quality test results for running 100 iterations on ChEBI database. Table S8 Data quality test results for running 100 iterations on PubChem database. Table S9 Data quality test results for running 100 iterations on LIPID MAPS database. Table S10 The list of metabolites that were not mapped by MetaboAnalystR for Diamanti et al. Table S11 An example of an input matrix for MetaFetcheR. Table S12 Results of comparing the performance of MetaFetcheR to MS_targeted using Diamanti et al. Table S13 Data set from Diamanti et al. Table S14 Data set from Priolo et al. Table S15 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Diamanti et al. Table S16 Results of comparing the performance of MetaFetcheR to CTS using LIPID MAPS identifiers available in Diamanti et al. Table S17 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. Table S18 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. (See the "index" tab in the Excel file for more information) Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. Lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power and large delays in delivery of results. We developed MetaFetcheR, an open-source R package that links metabolite data from several small-compound databases, resolves inconsistencies and covers a variety of use-cases of data fetching. We showed that the performance of MetaFetcheR was superior to existing approaches and databases by benchmarking the performance of the algorithm in three independent case studies based on two published datasets. The dataset was originally published in DiVA and moved to SND in 2024.

Purpose – The conversion between calibrated airspeed (CAS) and equivalent airspeed (EAS) is relatively cumbersome, because it involves the calculation of incompressible flow, for which the equations are quite long. If calculations on the computer are required, conversions with equations are necessary. In contrast, this project calculates a CAS to EAS Compressibility Correction Chart, which allows to convert CAS to EAS very quickly by reading the correction from a graph. --- Methodology – In Excel, compressibility correction is achieved through flight mechanics formulas. The correction is calculated with two distinct functions, one based on Mach Number and the other on pressure altitude. These functions are graphed individually and then integrated to produce the Compressibility Correction Chart. --- Findings – The Compressibility Correction Chart was successfully recreated as a 2-D graph. Upon comparison with other correction charts, the EAS-CAS-results demonstrate a mere 0% deviation, proving the accuracy of the findings and validating their near-perfect alignment. --- Research Limitations – Due to a limitation in Excel, which allows for 255 series for plotting, the range of input parameters had to be adjusted accordingly. The iterations of altitude span 1000 ft intervals, while those for Mach Number span 0.05 intervals. --- Practical Implications – Pilots can easily use the Compressibility Correction Chart for quick and highly accurate calculations when needed. --- Originality – CAS-EAS Compressibility Correction Charts are available in other sources. This paper represents a recreation of the 2-D Correction Chart by the combination of plots: one as function of Mach Number and the other of pressure altitude, using the Excel Software.

All Comparisons of Differentially Expressed Genes - excel sheet containing the annotations and fold change values of the all the differentially expressed genes between the different clone comparisonsFinal List of Common Genes - excel sheet containing the list of genes that were commonly differentially expressed between all the aphid clone comparisons. Also contains table and bar chart presenting the number of times each candidate gene selected from previous literature was found in each aphid clone comparison.Non-direct and Direct Competition - excel sheet containing number of nymphs produced by all 6 clones on the 3 host plants in the non-direct competition, and the number of nymphs produced by the two clones NS and Viola in the direct competition experiment.sterror - excel sheet containing the means and standard error values of the 6 grouped resistant and susceptible clones in the non-direct competition experiment, used to make the bar plot for the non-direct competition experiment.sterror2 - excel sheet containing the means and standard error values of the resistant clone Viola and susceptible clone NS in the direct competition experiment, used to make the bar plot for the direct competition experiment.cabbagettest - excel sheet containing the number of nymphs produce by the 6 grouped resistant and susceptible clones on the 3 host plants, used to conduct the unpaired t tests to compare the reproductive performance of resistant and susceptible clones on the 3 different host plants when in not in competitiondirectcompetition - excel sheet containing the number of nymphs produce by the resistant clone Viola and susceptible clone NS on the 3 host plants, used to conduct the unpaired t tests comparing the reproductive performance of resistant and susceptible clones on the 3 different host plants when in direct competitionAPHID HOST SHIFT DISS Rscript - R script containing all my statistical tests: unpaired t tests of resistant and susceptible clones on the 3 host plants when in direct and non direct competition, and kruskal Wallis tests and post hoc Dunns test to identify significant differences between individual and resistant and susceptible clones on the different host plants. Also contains all my code for my bar charts for the non-direct and direct competition experiments and the code for my box plots showing the significant differences between individual clones and resistant and susceptible clones on the different host plants.Up and Down-regulated Genes Graph - excel sheet containing the number of and and down regulated genes in each aphid clone comparison and the bar graph generated from this data.