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.

Excel sheets in order: The sheet entitled “Hens Original Data” contains the results of an experiment conducted to study the response of laying hens during initial phase of egg production subjected to different intakes of dietary threonine. The sheet entitled “Simulated data & fitting values” contains the 10 simulated data sets that were generated using a standard procedure of random number generator. The predicted values obtained by the new three-parameter and conventional four-parameter logistic models were also appeared in this sheet. (XLSX)

This resource, a MS Excel refresher, extends the level for this Data Nugget. Students are given an Excel workbook with the data and asked to graph and calculate diversity using Excel functions (rather than drawing graphs by hand as in the original data nugget). The data set used is the same. I use this activity in an upper division Environmental Science course for majors that focuses on Restoration Ecology. The simplicity of the data set and the comparisons of reptile diversity among urban, non-urban and urban rehabilitated lend for a great example for doing calculations in spreadsheets.

The algorithm for determining Cutpoints and simulating data using MS Excel. (XLS 2362Â kb)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. An R script (basin_boxplot.R) is provided as an example on how to create a wrapper function that will automate the generation of boxplots of change factors for all Florida HUC-8 basins. The wrapper script sources the file create_boxplot.R and calls the function create_boxplot() one Florida basin at a time to create a figure with boxplots of change factors for all durations (1, 3, and 7 days) and return periods (5, 10, 25, 50, 100, 200, and 500 years) evaluated as part of this project. An example is also provided in the code that shows how to generate a figure showing boxplots of change factors for a single duration and return period. A Microsoft Word file documenting code usage is also provided within this data release (Documentation_R_script_create_boxplot.docx). As described in the documentation, the R script relies on some of the Microsoft Excel spreadsheets published as part of this data release. The script uses HUC-8 basins defined in the "Florida Hydrologic Unit Code (HUC) Basins (areas)" from the Florida Department of Environmental Protection (FDEP; https://geodata.dep.state.fl.us/datasets/FDEP::florida-hydrologic-unit-code-huc-basins-areas/explore) and their names are listed in the file basins_list.txt provided with the script. County names are listed in the file counties_list.txt provided with the script. NOAA Atlas 14 stations located in each Florida basin or county are defined in the Microsoft Excel spreadsheet Datasets_station_information.xlsx which is part of this data release. Instructions are provided in code documentation (see highlighted text on page 7 of Documentation_R_script_create_boxplot.docx) so that users can modify the script to generate boxplots for basins different from the FDEP "Florida Hydrologic Unit Code (HUC) Basins (areas)."

Raw data outputs 1-18 Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis. Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study. Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones. Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs. Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining. Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell. Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

This dataset contains the valuation template the researcher can use to retrieve real-time Excel stock price and stock price in Google Sheets. The dataset is provided by Finsheet, the leading financial data provider for spreadsheet users. To get more financial data, visit the website and explore their function. For instance, if a researcher would like to get the last 30 years of income statement for Meta Platform Inc, the syntax would be =FS_EquityFullFinancials("FB", "ic", "FY", 30) In addition, this syntax will return the latest stock price for Caterpillar Inc right in your spreadsheet. =FS_Latest("CAT") If you need assistance with any of the function, feel free to reach out to their customer support team. To get starter, install their Excel and Google Sheets add-on.

The dataset for the article "The current utilization status of wearable devices in clinical research".Analyses were performed by utilizing the JMP Pro 16.10, Microsoft Excel for Mac version 16 (Microsoft).The file extension "jrp" is a file of the statistical analysis software JMP, which contains both the analysis code and the data set.In case JMP is not available, a "csv" file as a data set and JMP script, the analysis code, are prepared in "rtf" format.The "xlsx" file is a Microsoft Excel file that contains the data set and the data plotted or tabulated using Microsoft Excel functions.Supplementary Figure 1. NCT number duplication frequencyIncludes Excel file used to create the figure (Supplemental Figure 1).・Sfig1_NCT number duplication frequency.xlsxSupplementary Figure 2-5　Simple and annual time series aggregationIncludes Excel file, JMP repo file, csv dataset of JMP repo file and JMP scripts used to create the figure (Supplementary Figures 2-5).・Sfig2-5 Annual time series aggregation.xlsx・Sfig2 Study Type.jrp・Sfig4device type.jrp・Sfig3 Interventions Type.jrp・Sfig5Conditions type.jrp・Sfig2, 3 ,5_database.csv・Sfig2_JMP script_Study type.rtf・Sfig3_JMP script Interventions type.rtf・Sfig5_JMP script Conditions type.rtf・Sfig4_dataset.csv・Sfig4_JMP script_device type.rtfSupplementary Figures 6-11 Mosaic diagram of intervention by conditionSupplementary tables 4-9 Analysis of contingency table for intervention by condition JMP repot files used to create the figures(Supplementary Figures 6-11 ) and tables(Supplementary Tablea 4-9) , including the csv dataset of JMP repot files and JMP scripts.・Sfig6-11 Stable4-9 Intervention devicetype_conditions.jrp・Sfig6-11_Stable4-9_dataset.csv・Sfig6-11_Stable4-9_JMP script.rtfSupplementary Figure 12. Distribution of enrollmentIncludes Excel file, JMP repo file, csv dataset of JMP repo file and JMP scripts used to create the figure (Supplementary Figures 12).・Sfig12_Distribution of enrollment.jrp・Sfig12_Distribution of enrollment.csv・Sfig12_JMP script.rtf

NaiveBayes_R.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given recidivism (P(x_ij│R)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|R): This tab contains probabilities of feature attributes among recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. Recidivated_Train: This tab contains re-coded features of recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|R) tab. NaiveBayes_NR.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given non-recidivism (P(x_ij│N)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|N): This tab contains probabilities of feature attributes among non-recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. NonRecidivated_Train: This tab contains re-coded features of non-recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given non-recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|N) tab. Training_LnTransformed.xlsx: Figures in each cell are log-transformed ratios of probabilities in NaiveBayes_R.xlsx (P(Xi|R)) to the probabilities in NaiveBayes_NR.xlsx (P(Xi|N)). TestData.xlsx: This Excel file includes the following tabs based on the test data: P(Xi|R), P(Xi|N), NIJ_Recoded, and Test_LnTransformed (log-transformed P(Xi|R)/ P(Xi|N)). Training_LnTransformed.dta: We transform Training_LnTransformed.xlsx to Stata data set. We use Stat/Transfer 13 software package to transfer the file format. StataLog.smcl: This file includes the results of the logistic regression analysis. Both estimated intercept and coefficient estimates in this Stata log correspond to the raw weights and standardized weights in Figure 1. Brier Score_Re-Check.xlsx: This Excel file recalculates Brier scores of Relaxed Naïve Bayes Classifier in Table 3, showing evidence that results displayed in Table 3 are correct. *****Full List***** NaiveBayes_R.xlsx NaiveBayes_NR.xlsx Training_LnTransformed.xlsx TestData.xlsx Training_LnTransformed.dta StataLog.smcl Brier Score_Re-Check.xlsx Data for Weka (Training Set): Bayes_2022_NoID Data for Weka (Test Set): BayesTest_2022_NoID Weka output for machine learning models (Conventional naïve Bayes, AdaBoost, Multilayer Perceptron, Logistic Regression, and Random Forest)

The hectares of habitat protected and the number of adults and children fed in one year were calculated for each of the six crop types for Canada and United States. The calculations were based on the 50th centile of the cumulative frequency distributions of change in crop yield due to pesticide treatment for each crop type. An editable interactive table was created using Microsoft Excel that would allow individuals to determine how pesticide treatment in their selected jurisdiction (province in Canada or state in the United States) and crop translates into habitat saved, calories produced, and mouths fed. This table allows the user to choose the country (Canada or United States), whether to include the organic agriculture correction factor, their state or province of interest, crop, and whether a young child, adolescent child, adult women, or adult man is being fed. The table will then calculate the hectares of habitat saved, added number of calories produced (kcal), the number of individual fed in one day, and the number of individual fed in one year. Due to the variability in yield results between crops and studies, the Excel user form allows individuals to set whichever yield increase they anticipate observing or use the 50th centile of yield increase from the cumulative frequency distribution for each crop.

The data presented here are experimental results on the two-photon absorption properties of cyanine dye IR780.

The file 2PA_IR780_slopes_2.xlsx features the raw data of the logarithm of the integrated fluorescence vs. the logarithm of the peak intensity and the lines of best fit for each plot. This Excel file is organized into several tabs, each labelled with the excitation wavelength used to irradiate our samples (Rhodamines 6G and B as well as IR780); these data were featured in the Data in Brief manuscript entitled "Experimental and Computational Data on Two-Photon Absorption and Spectral Deconvolution of the Upper Excited States of Dye IR780".

The file deconvolution_N_peaks.xlsx features the raw data of the deconvolution of the absorption spectrum of the upper excited states of IR780 with 5, 6, 7, 8 and 9 Gaussian functions. This Excel file contains several tabs, the first one of them being the experimental absorption spectrum of IR780; the rest of the tabs are labelled by the number of Gaussian functions employed in the deconvolution; these data were featured in the Data in Brief manuscript mentioned above. The information contained in this file shows the assignment of the bands pertaining to electronic states S2-S6; the 8-peak and 9-peak fits show suitable R-Square and Reduced Chi-Sqr values that demonstrate the coexistence of said electronic states in the spectral region 18350 - 35980 cm-1.

Purpose – Simple equations and more extended models are developed to determine characteristic engine parameters: Specific fuel consumption (SFC), engine mass, and engine size characterized by engine length and diameter. SFC (c) is considered a linear function of speed: c = c_a * V + c_b. --- Methodology – Data from 718 engines is collected from various open sources into an Excel spreadsheet. The characteristic engine parameters are plotted as function of bypass ratio (BPR), date of entry into service (EIS), take-off thrust, and typical cruise thrust. Engine length and diameter are plotted versus engine mass. Linear and nonlinear regression functions are investigated. Moreover, Singular Value Decomposition (SVD) is used to establish relations between parameters. SVD is used with Excel and MATLAB. The accuracy of all equations and models is compared. --- Findings – SFC should be calculated as a linear function of speed. This is especially important, when SFC is extrapolated to unconventional (low) cruise speeds for jet engines. The two parameters c_a and c_b are best estimated from a logarithmic or power function of bypass ratio (BPR). SFC and c_b clearly improved over the years. Engine mass, diameter, and length are proportional to take-off thrust. Characteristic engine parameters can also be obtained from SVD with comparable accuracy. However, SVD is more complicated to set up than using a simple equation. --- Practical implications – Engine characteristics need to be estimated based on only a few known parameters for aircraft preliminary sizing, conceptual design, and aircraft optimization as well as for practical quick calculations in flight mechanics. This thesis provides the tools. --- Social implications – Most engine characteristics like SFC are considered company secrets. The availability of open access engine data is the first step, but wisdom is retrieved only with careful analysis of the data as done here. Openly available aircraft engineering knowledge helps to democratize the discussion about the ecological footprint of aviation. --- Originality/value – Simple equation for jet engine SFC, mass, and size deduced from a large engine database are offered. This approach delivered equations as a function of BPR with an error of only 6%, which is the same accuracy as more complex equations from literature.

Usage-based linguistics postulates that multi-word expressions constitute a substantial part of language structure and use, and are formed through repeated chunking and stored as exemplar wholes. They are also re-used to produce new sequences by means of schematization. While there is extensive research on multi-word expressions in many spoken languages, little is known about the status of multi-word expressions in the mainstream U.S. variety of American Sign Language (ASL). This paper investigates recurring multi-word expressions, or sequences of multiple signs, that involve a high-frequency sign of visual perception glossed as LOOK and the family of ‘look’ signs. The LOOK sign exhibits two broad functions: LOOK/‘vision’ references literal or metaphorical vision and LOOK/‘reaction’ signals a person’s reaction to a visual stimulus. Data analysis reveals that there are recurring sequences in distinct syntactic environments associated with the two functions of LOOK, suggesting that LOOK is in the process of grammaticalization from a verb of visual perception to a stance verb. The sequences demonstrate the emergence of linguistic structure from repeated use through the domain-general cognitive process of chunking in ASL. Methods This dataset was collected by an arbitrary sampling of assorted videos and video blogs (vlogs) in American Sign Language from the internet. These videos were coded for all tokens of the family of 'look' and 'see' signs and the five signs preceding the target sign and the five signs following it. The target sign was also analyzed for their function in the phrasal context. The coding was entered as English glosses in a Microsoft Excel spreadsheet. Then the data was sorted for their functions and also sorted for recurring n-grams.

The PDI ("Peters-Durner-Iden") model system represents a robust framework for parameterizing soil hydraulic properties, i.e. the water retention curve and the hydraulic conductivity curve, across the entire soil moisture spectrum. This model accounts for water retention and hydraulic conductivity in completely and partially-filled pores, including adsorption and film-flow. It was developed in stages and a comprehensive overview of the model development and the model equations is provided in Peters et al. (2024). In this repository, we provide a Python file named “pdi.py†which can be used to compute the various submodels (PDI-VG, PDI-KOS, PDI-FX, ...) of the PDI model system. One MS Excel file is provided for easy access to one PDI model, the PDI-VG. The PYTHON functions contained in "pdi.py" can be used to calculate the water retention curve, the unsaturated hydraulic conductivity curve, the specific water capacity function, and the soil water diffusivity function. In addition, we prov..., The dataset consists of the data shown in Figure 3 of the article in Vadose Zone Journal (Peters et al., 2014). This data was measured in the soil physics lab in the Institute of Geoecology at TU Braunschweig, Germany, with the evaporation method using the HYPROP measurement system. The python codes and the Excel spreadsheet were developed by Dr. Sascha C. Iden., , # Data from: The PDI model system for parameterizing soil hydraulic properties

https://doi.org/10.5061/dryad.z34tmpgnk

This dataset consists of the data shown in Figure 3 of Peters et al. (2024, https://doi.org/10.1002/vzj2.20338), six PYTHON scripts, one MS Excel file, and a short tutorial as pdf file. The main script named "pdi.py" provides all models of the PDI model system discussed in Peters et. al. (2024). Five additional PYTHON scripts exemplify the call to the various PDI functions. Notably, "pdi.py" incorporates a utility function, 'export_hydrus_materin', which generates an ASCII file named "MATER.IN". This file serves as input for simulations with Hydrus-1D and Hydrus-2D3D, offering seamless integration with these simulation platforms. One MS Excel file is provided for easy access to one PDI model, the PDI-VG.

Description of the data and file structure

The dataset consists of one data file...

Excel file containing the full dataset of the paper "Sediment Respiration Pulses in Intermittent Rivers and Ephemeral Streams". The first sheet contains a description of the variables. The second sheet contains the data. These data were used together with the R code (Code S1 file) to generate teh results presented in the paper.

There are two datasets. First, a dataset for the PROPS models (i.e. “US-PROPS_v2_models_May30_2019.xlsx,” which describe the parameters for the PROPS models for the 1503 species that were included in the study. Metadata for this data is provided in the excel spreadsheet. Second, is a spreadsheet of the “critical load functions” (CLF) that are derived from the PROPS models (i.e. “PROPS-CLF_results_May30_2019.xlsx”). Metadata for this dataset are also provided in the spreadhseet.

Excel spreadsheet containing raw data, organized by figure.

The first Excel Shee1 is the energy spectrum data measured by the HPGe detecto in the X/γ reference radiation field;The first Excel Sheet2 is the dose rate data measured by a PTW ionization chamber in the X/γ reference radiation field;The first Excel Sheet3 is the G-factor solved by the Convolution of complete energy spectrum to dose rate method;The first Excel Sheet4 is the dose rate response test result of the G-function method;The first Excel Sheet5 is the energy response test result of the G-function method;The first Excel Sheet6 is the angular response test result of the G-function method(complete data in the second and third excel files)The first Excel Sheet7 is an application test of the G function method and common dosimeters under low dose rate conditions.(complete data in the 4th excel files)The first Excel Sheet8 is radionuclides' information.The first Excel Sheet9 is the theoretical dose rate of radionuclides.