Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

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.

A spreadsheet was designed for weighing lysimeter raw relative water storage data analysis and reduction to values of evapotranspiration (ET), dew and frost accumulation, precipitation, irrigation, and drainage tank emptying. A new version of the spreadsheet uploaded in April 2025 includes more facilities for visualization, error checking, and validation of ET values. Algorithms in the spreadsheet automatically identify precipitation, and dew and frost accumulations in the 5-minute data, and places flags appropriately (“P” or “DW”) in a column that is referenced by formulae that separately calculate values for these. Noise can, however, cause false identification of precipitation or frost and dew accumulations, so another column is made available in which the user can enter flags to either nullify (“NO”) false automatic identification, or conversely, identify precipitation or dew and frost accumulations (“P” or “DW”) not automatically identified. This column also serves for entry of flags identifying irrigation, drainage tank emptying, counterweight adjustments, etc. Algorithms in other columns act upon these flags to correct the original raw relative storage values so that the adjusted relative storage reflects only evapotranspiration, while simultaneously computing 5-min values for precipitation, irrigation, dew and frost accumulation, and drainage tank emptying.

With a step-by-step approach, learn to prepare Excel files, data worksheets, and individual data columns for data analysis; practice conditional formatting and creating pivot tables/charts; go over basic principles of Research Data Management as they might apply to an Excel project. Avec une approche étape par étape, apprenez à préparer pour l’analyse des données des fichiers Excel, des feuilles de calcul de données et des colonnes de données individuelles; pratiquez la mise en forme conditionnelle et la création de tableaux croisés dynamiques ou de graphiques; passez en revue les principes de base de la gestion des données de recherche tels qu’ils pourraient s’appliquer à un projet Excel.

This repository contains the data supporting the manuscript "A Generic Scenario Analysis of End-of-Life Plastic Management: Chemical Additives" (to be) submitted to the Energy and Environmental Science Journal https://pubs.rsc.org/en/journals/journalissues/ee#!recentarticles&adv This repository contains Excel spreadsheets used to calculate material flow throughout the plastics life cycle, with a strong emphasis on chemical additives in the end-of-life stages. Three major scenarios were presented in the manuscript: 1) mechanical recycling (existing recycling infrastructure), 2) implementing chemical recycling to the existing plastics recycling, and 3) extracting chemical additives before the manufacturing stage. Users would primarily modify values on the yellow tab "US 2018 Facts - Sensitivity". Values highlighted in yellow may be changed for sensitivity analysis purposes. Please note that the values shown for MSW generated, recycled, incinerated, landfilled, composted, imported, exported, re-exported, and other categories in this tab were based on 2018 data. Analysis for other years can be made possible with a replicate version of this spreadsheet and the necessary data to replace those of 2018. Most of the tabs, especially those that contain "Stream # - Description", do not require user interaction. They are intermediate calculations that change according to the user inputs. It is available for the user to see so that the calculation/method is transparent. The major results of these individual stream tabs are ultimately compiled into one summary tab. All streams throughout the plastics life cycle, for each respective scenario (1, 2, and 3), are shown in the "US Mat Flow Analysis 2018" tab. For each stream, we accounted the approximate mass of plastics found in MSW, additives that may be present, and non-plastics. Each spreadsheet contains a representative diagram that matches the stream label. This illustration is placed to aid the user with understanding the connection between each stage in the plastics' life cycle. For example, the Scenario 1 spreadsheet uniquely contains Material Flow Analysis Summary, in addition to the LCI. In the "Material Flow Analysis Summary" tab, we represented the input, output, releases, exposures, and greenhouse gas emissions based on the amount of materials inputted into a specific stage in the plastics life cycle. The "Life Cycle Inventory" tab contributes additional calculations to estimate land, air, and water releases. Figures and Data - A gs analysis on eol plastic management This word document contains the raw data used to create all the figures in the main manuscript. The major references used to obtain the data are also included where appropriate.

The attached Excel spreadsheet is a codebook for our quantitative data analysis.

Sample data for exercises in Further Adventures in Data Cleaning.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Dataset Overview: This dataset pertains to the examination results of students who participated in a series of academic assessments at a fictitious educational institution named "University of Exampleville." The assessments were administered across various courses and academic levels, with a focus on evaluating students' performance in general management and domain-specific topics.

Columns: The dataset comprises 12 columns, each representing specific attributes and performance indicators of the students. These columns encompass information such as the students' names (which have been anonymized), their respective universities, academic program names (including BBA and MBA), specializations, the semester of the assessment, the type of examination domain (general management or domain-specific), general management scores (out of 50), domain-specific scores (out of 50), total scores (out of 100), student ranks, and percentiles.

Data Collection: The examination data was collected during a standardized assessment process conducted by the University of Exampleville. The exams were designed to assess students' knowledge and skills in general management and their chosen domain-specific subjects. It involved students from both BBA and MBA programs who were in their final year of study.

Data Format: The dataset is available in a structured format, typically as a CSV file. Each row represents a unique student's performance in the examination, while columns contain specific information about their results and academic details.

Data Usage: This dataset is valuable for analyzing and gaining insights into the academic performance of students pursuing BBA and MBA degrees. It can be used for various purposes, including statistical analysis, performance trend identification, program assessment, and comparison of scores across domains and specializations. Furthermore, it can be employed in predictive modeling or decision-making related to curriculum development and student support.

Data Quality: The dataset has undergone preprocessing and anonymization to protect the privacy of individual students. Nevertheless, it is essential to use the data responsibly and in compliance with relevant data protection regulations when conducting any analysis or research.

Data Format: The exam data is typically provided in a structured format, commonly as a CSV (Comma-Separated Values) file. Each row in the dataset represents a unique student's examination performance, and each column contains specific attributes and scores related to the examination. The CSV format allows for easy import and analysis using various data analysis tools and programming languages like Python, R, or spreadsheet software like Microsoft Excel.

Here's a column-wise description of the dataset:

Name OF THE STUDENT: The full name of the student who took the exam. (Anonymized)

UNIVERSITY: The university where the student is enrolled.

PROGRAM NAME: The name of the academic program in which the student is enrolled (BBA or MBA).

Specialization: If applicable, the specific area of specialization or major that the student has chosen within their program.

Semester: The semester or academic term in which the student took the exam.

Domain: Indicates whether the exam was divided into two parts: general management and domain-specific.

GENERAL MANAGEMENT SCORE (OUT of 50): The score obtained by the student in the general management part of the exam, out of a maximum possible score of 50.

Domain-Specific Score (Out of 50): The score obtained by the student in the domain-specific part of the exam, also out of a maximum possible score of 50.

TOTAL SCORE (OUT of 100): The total score obtained by adding the scores from the general management and domain-specific parts, out of a maximum possible score of 100.

This dataset contains one Excel sheet and five Word documents. In this dataset, Simulation.xlsx describes the parameter values used for the numerical analysis based on empirical data. In this Excel sheet, we calculated the values of each capped call-option model parameter. Computation of Table 2.docx and other documents show the results of the comparative statistics.

Operational Analysis is a method of examining the current and historical performance of the operations and maintenance investments and measuring that performance against an established set of cost, schedule, and performance parameters. The Operational Analysis template is used as a guide in preparing and documenting SSA's Operational Analyses.

A spreadsheet was designed for weighing lysimeter raw relative water storage data analysis and reduction to values of evapotranspiration, dew and frost accumulation, precipitation, irrigation, and drainage tank emptying. Algorithms in the spreadsheet automatically identify precipitation, and dew and frost accumulations in the 5-minute data, and places flags appropriately (“P” or “DW”) in a column that is referenced by formulae that separately calculate values for these. Noise can, however, cause false identification of precipitation or frost and dew accumulations, so another column is made available in which the user can enter flags to either nullify (“NO”) false automatic identification, or conversely, identify precipitation or dew and frost accumulations (“P” or “DW”) not automatically identified. This column also serves for entry of flags identifying irrigation, drainage tank emptying, counterweight adjustments, etc. Algorithms in other columns act upon these flags to correct the original raw relative storage values so that the adjusted relative storage reflects only evapotranspiration, while simultaneously computing 5-min values for precipitation, irrigation, dew and frost accumulation, and drainage tank emptying.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.

This is a dataset analysis regarding our previous research and the current research. it is the result of our observations over 3 years of monitoring and is provided briefly within our 1st publication: https://doi.org/10.5281/zenodo.10407923.

This database studies the performance inconsistency on the biomass HHV ultimate analysis. The research null hypothesis is the consistency in the rank of a biomass HHV model. Fifteen biomass models are trained and tested in four datasets. In each dataset, the rank invariability of these 15 models indicates the performance consistency.

The database includes the datasets and source codes to analyze the performance consistency of the biomass HHV. These datasets are stored in tabular on an excel workbook. The source codes are the biomass HHV machine learning model through the MATLAB Objected Orient Program (OOP). These machine learning models consist of eight regressions, four supervised learnings, and three neural networks.

An excel workbook, "BiomassDataSetUltimate.xlsx," collects the research datasets in six worksheets. The first worksheet, "Ultimate," contains 908 HHV data from 20 pieces of literature. The names of the worksheet column indicate the elements of the ultimate analysis on a % dry basis. The HHV column refers to the higher heating value in MJ/kg. The following worksheet, "Full Residuals," backups the model testing's residuals based on the 20-fold cross-validations. The article (Kijkarncharoensin & Innet, 2021) verifies the performance consistency through these residuals. The other worksheets present the literature datasets implemented to train and test the model performance in many pieces of literature.

A file named "SourceCodeUltimate.rar" collects the MATLAB machine learning models implemented in the article. The list of the folders in this file is the class structure of the machine learning models. These classes extend the features of the original MATLAB's Statistics and Machine Learning Toolbox to support, e.g., the k-fold cross-validation. The MATLAB script, name "runStudyUltimate.m," is the article's main program to analyze the performance consistency of the biomass HHV model through the ultimate analysis. The script instantly loads the datasets from the excel workbook and automatically fits the biomass model through the OOP classes.

The first section of the MATLAB script generates the most accurate model by optimizing the model's higher parameters. It takes a few hours for the first run to train the machine learning model via the trial and error process. The trained models can be saved in MATLAB .mat file and loaded back to the MATLAB workspace. The remaining script, separated by the script section break, performs the residual analysis to inspect the performance consistency. Furthermore, the figure of the biomass data in the 3D scatter plot, and the box plots of the prediction residuals are exhibited. Finally, the interpretations of these results are examined in the author's article.

Reference : Kijkarncharoensin, A., & Innet, S. (2022). Performance inconsistency of the Biomass Higher Heating Value (HHV) Models derived from Ultimate Analysis [Manuscript in preparation]. University of the Thai Chamber of Commerce.

Excel spreadsheet which only contains numeric data from a set of confusion matrices (one sheet per matrix).It is the same quantitative data stored in a field of a table in the database. Only is provided as a complement to the database in order to access to the quantitative data in a more convenient format.

The global spreadsheet software market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions and the rising demand for data analysis tools across various industries. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $150 billion by the end of the forecast period. This growth is fueled by several key factors. Firstly, the increasing reliance on data-driven decision-making across businesses, irrespective of size, necessitates efficient data management and analysis capabilities provided by spreadsheet software. Secondly, the proliferation of cloud-based spreadsheet applications offers enhanced collaboration, accessibility, and scalability, making them attractive to organizations of all sizes. Finally, continuous advancements in features like advanced analytics, data visualization, and integration with other business applications enhance the overall utility and appeal of these tools. Major players like Microsoft, Google, and Zoho are continuously innovating, adding new features and improving user experience to maintain their market leadership. However, the market also faces challenges. Security concerns related to data storage and access in cloud-based solutions, and the need for continuous training and upskilling to leverage advanced features, pose limitations to wider adoption. Despite these challenges, the long-term outlook for the spreadsheet software market remains positive. The increasing digitization of businesses and the expanding adoption of big data analytics will propel demand for sophisticated spreadsheet tools. The emergence of niche players focusing on specific industry needs and specialized functionalities will also contribute to market expansion. Competition will remain fierce among established players and newcomers, prompting innovation and improvement in the overall product offerings. The market will witness consolidation through mergers and acquisitions, and a shift towards subscription-based models, further driving market growth and shaping the competitive landscape. The geographic distribution of the market will see continued growth in developing economies, driven by increasing internet penetration and smartphone adoption.