Time-Series Matrix (TSMx): A visualization tool for plotting multiscale temporal trends TSMx is an R script that was developed to facilitate multi-temporal-scale visualizations of time-series data. The script requires only a two-column CSV of years and values to plot the slope of the linear regression line for all possible year combinations from the supplied temporal range. The outputs include a time-series matrix showing slope direction based on the linear regression, slope values plotted with colors indicating magnitude, and results of a Mann-Kendall test. The start year is indicated on the y-axis and the end year is indicated on the x-axis. In the example below, the cell in the top-right corner is the direction of the slope for the temporal range 2001–2019. The red line corresponds with the temporal range 2010–2019 and an arrow is drawn from the cell that represents that range. One cell is highlighted with a black border to demonstrate how to read the chart—that cell represents the slope for the temporal range 2004–2014. This publication entry also includes an excel template that produces the same visualizations without a need to interact with any code, though minor modifications will need to be made to accommodate year ranges other than what is provided. TSMx for R was developed by Georgios Boumis; TSMx was originally conceptualized and created by Brad G. Peter in Microsoft Excel. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 TSMx sample chart from the supplied Excel template. Data represent the productivity of rice agriculture in Vietnam as measured via EVI (enhanced vegetation index) from the NASA MODIS data product (MOD13Q1.V006). TSMx R script: # import packages library(dplyr) library(readr) library(ggplot2) library(tibble) library(tidyr) library(forcats) library(Kendall) options(warn = -1) # disable warnings # read data (.csv file with "Year" and "Value" columns) data <- read_csv("EVI.csv") # prepare row/column names for output matrices years <- data %>% pull("Year") r.names <- years[-length(years)] c.names <- years[-1] years <- years[-length(years)] # initialize output matrices sign.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) pval.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) slope.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) # function to return remaining years given a start year getRemain <- function(start.year) { years <- data %>% pull("Year") start.ind <- which(data[["Year"]] == start.year) + 1 remain <- years[start.ind:length(years)] return (remain) } # function to subset data for a start/end year combination splitData <- function(end.year, start.year) { keep <- which(data[['Year']] >= start.year & data[['Year']] <= end.year) batch <- data[keep,] return(batch) } # function to fit linear regression and return slope direction fitReg <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(sign(slope)) } # function to fit linear regression and return slope magnitude fitRegv2 <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(slope) } # function to implement Mann-Kendall (MK) trend test and return significance # the test is implemented only for n>=8 getMann <- function(batch) { if (nrow(batch) >= 8) { mk <- MannKendall(batch[['Value']]) pval <- mk[['sl']] } else { pval <- NA } return(pval) } # function to return slope direction for all combinations given a start year getSign <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) signs <- lapply(combs, fitReg) return(signs) } # function to return MK significance for all combinations given a start year getPval <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) pvals <- lapply(combs, getMann) return(pvals) } # function to return slope magnitude for all combinations given a start year getMagn <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) magns <- lapply(combs, fitRegv2) return(magns) } # retrieve slope direction, MK significance, and slope magnitude signs <- lapply(years, getSign) pvals <- lapply(years, getPval) magns <- lapply(years, getMagn) # fill-in output matrices dimension <- nrow(sign.matrix) for (i in 1:dimension) { sign.matrix[i, i:dimension] <- unlist(signs[i]) pval.matrix[i, i:dimension] <- unlist(pvals[i]) slope.matrix[i, i:dimension] <- unlist(magns[i]) } sign.matrix <-...

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

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

According to Cognitive Market Research, the global Graph Analytics market size will be USD 2522 million in 2024 and will expand at a compound annual growth rate (CAGR) of 34.0% from 2024 to 2031. Market Dynamics of Graph Analytics Market

Key Drivers for Graph Analytics Market

Increasing Recognition of the Advantages of Graph Databases- One of the main reasons for the Graph Analytics market is the increasing recognition of the advantages of graph databases. Unlike traditional relational databases, graph databases excel at handling complex relationships and interconnected data, making them ideal for use cases such as fraud detection, recommendation engines, and social network analysis. Businesses are leveraging these capabilities to uncover insights and patterns that were previously difficult to detect. The rise of big data and the need for real-time analytics are further driving the adoption of graph databases, as they offer enhanced performance and scalability for large-scale data sets. Additionally, advancements in artificial intelligence and machine learning are amplifying the value of graph databases, enabling more sophisticated data modeling and predictive analytics.
Growing Uptake of Big Data Tools to Drive the Graph Analytics Market's Expansion in the Years Ahead.

Key Restraints for Graph Analytics Market

Limited Awareness and Understanding pose a serious threat to the Graph Analytics industry.
The market also faces significant difficulties related to data security and privacy.

Introduction of the Graph Analytics Market

The Graph Analytics Market is rapidly expanding, driven by the growing need for advanced data analysis techniques in various sectors. Graph analytics leverages graph structures to represent and analyze relationships and dependencies, providing deeper insights than traditional data analysis methods. Key factors propelling this market include the rise of big data, the increasing adoption of artificial intelligence and machine learning, and the demand for real-time data processing. Industries such as finance, healthcare, telecommunications, and retail are major contributors, utilizing graph analytics for fraud detection, personalized recommendations, network optimization, and more. Leading vendors are continually innovating to offer scalable, efficient solutions, incorporating advanced features like graph databases and visualization tools.

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).

Spreadsheets and graphs are powerful tools that make data come alive and tell a story. Now, use maps to see the story from another perspective. ArcGIS Maps for Office enables Microsoft Excel and PowerPoint users worldwide to ask location-related questions of data, get powerful insights, and make the best decisions. You can:Map your spreadsheet data – whether you want to see customer locations, ZIP code aggregations, custom sales territories and more – you can see it all.Add geographic context to your spreadsheet data and communicate these insights via interactive maps in PowerPoint.Gain insight into demographic, spending, behavior, and landscape information, among many more.Use the authoritative content on the ArcGIS platform to supplement your location data and add context to the locations in your spreadsheet.Securely share your maps with colleagues and stakeholders.Bring the power of the ArcGIS platform into your spreadsheets and presentations. To use ArcGIS Maps for Office you need an ArcGIS Online paid or trial subscription or a Portal for ArcGIS Named User License and Microsoft Office 2010, 2013, or 2016. Visit the online documentation for information on how to use this app.

This data publication is part of the 'P³-Petrophysical Property Database' project, which was developed within the EC funded project IMAGE (Integrated Methods for Advanced Geothermal Exploration, EU grant agreement No. 608553) and consists of a scientific paper, a full report on the database, the database as excel and .csv files and additional tables for a hierarchical classification of the petrography and stratigraphy of the investigated rock samples (see related references). This publication here provides a hierarchical interlinked stratigraphic classification according to the chronostratigraphical units of the international chronostratigraphic chart of the IUGS v2016/04 (Cohen et al. 2013, updated) according to international standardisation. As addition to this IUGS chart, which is also documented in GeoSciML, stratigraphic IDs and parent IDs were included to define the direct relationships between the stratigraphic terms. The P³ database aims at providing easily accessible, peer-reviewed information on physical rock properties relevant for geothermal exploration and reservoir characterization in one single compilation. Collected data include hydraulic, thermophysical and mechanical properties and, in addition, electrical resistivity and magnetic susceptibility. Each measured value is complemented by relevant meta-information such as the corresponding sample location, petrographic description, chronostratigraphic age and, most important, original citation. The original stratigraphic and petrographic descriptions are transferred to standardized catalogues following a hierarchical structure ensuring intercomparability for statistical analysis, of which the stratigraphic catalogue is presented here. These chronostratigraphic units are compiled to ensure that formations of a certain age are connected to the corresponding stratigraphic epoch, period or erathem. Thus, the chronostratigraphic units are directly correlated to each other by their stratigraphic ID and stratigraphic parent ID and can thus be used for interlinked data assessment of the petrophysical properties of samples of an according stratigraphic unit.

In "Sample Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described (CrP Sample Dataset, Glycolytic Dataset, Oxidative Dataset). Additionally, there are three sheets with sample graphs created using one of the three datasets (CrP Sample Graph, Glycolytic Graph, Oxidative Graph). Each dataset and graph pairs are from different subjects. · CrP Sample Dataset and CrP Sample Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Glycolytic Dataset and Glycolytic Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Oxidative Dataset and Oxidative Graph: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a sustained, light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.

Spreadsheet Software Market Size And Forecast

Spreadsheet Software Market size was valued at USD 10.05 Billion in 2023 and is expected to reach USD 14.55 Billion by 2031, with a CAGR of 7.8% from 2024-2031.

Global Spreadsheet Software Market Drivers

The market drivers for the Spreadsheet Software Market can be influenced by various factors. These may include:

Increasing Data Volume: As organizations generate and collect more data, the need for efficient data analysis and management tools, such as spreadsheet software, grows. Rising Demand for Data Visualization: Users increasingly seek sophisticated tools to visualize data for better insights. Spreadsheet software can provide charts and graphs, making data interpretation easier.

Global Spreadsheet Software Market Restraints

Several factors can act as restraints or challenges for the Spreadsheet Software Market, These may include:

Market Saturation: Many organizations already use established spreadsheet software such as Microsoft Excel or Google Sheets. The reliance on these platforms can make it difficult for new entrants or alternative solutions to capture market share. High Competition: The market is highly competitive, with numerous players offering similar features and functionalities. This can lead to price wars and reduced profit margins for software providers.

In the attached Excel file, "Example Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described. Additionally, there are three sheets with sample graphs created using one of the three datasets. · Sheets 1 and 2: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Sheets 3 and 4: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Sheets 5 and 6: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.

With the user manual provided at the end of the research manuscript, and the Graph Input Data Example.xlsx as a reference, the user provides all the graph semantic data required to evaluate all the performance criteria for the system.These criteria include the probability that the principal target can be reached, and the costs, elapsed times and total vulnerability resulting from a penetration attempt by one or more intruders.This performance computation is accurate and efficient, requiring an insignificant amount of computation time.It also resolves all the statistical dependencies and probabilistic uncertainties believed to be an important challenge to a risk manager and his or her analysts.User enters the Graph Topological data in this excel file, thereby creating a topological model.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-07-06 to 2025-07-02 about stock market, average, industry, and USA.

This is the datasets used for Linked Open Data instant level quality analysis for cultural heritage (2020). 7Z and ZIP versions are available for both Excel 2006 and R 4.0.3. The compressed files include, Excel spreadsheets (.xlsx, .csv), VBA scripts (.bas), and R scripts (.r). Please read the full documentation in Linked_Open_Data_Instance_Level_Analysis_Procedure.pdf.

Mobile Application Coverage: The 30% Curse and Ways Forward## Purpose In this artifact, we provide the information about our benchmarks used for manual and tool exploration. We include coverage results achieved by tools and human analysts as well as plots of the coverage progression over time for analysts. We further provide manual analysis results for our case study, more specifically extracted reasons for unreachability for the case study apps and extracted code-level properties, which constitute a ground truth for future work in coverage explainability. Finally, we identify a list of beyond-GUI exploration tools and categorize them for future work to take inspiration from. We are claiming available and reusable badges; the artifact is fully aligned with the results described in our paper and comprehensively documented.## ProvenanceThe paper preprint is available here: https://people.ece.ubc.ca/mjulia/publications/Mobile_Application_Coverage_ICSE2025.pdf## Data The artifact submission is organized into five parts:- 'BenchInfo' excel sheet describing our experiment dataset- 'Coverage' folder containing coverage results for tools and analysts (RQ1) - 'Reasons' excel sheet describing our manually extracted reasons for unreachability (RQ2)- 'ActivationProperties' excel sheet describing our manually extracted code properties of unreached activities (RQ3)- 'ActivationProperties-Graph' pdf which presents combinations of the extracted code properties in a graph format.- 'BeyondGUI' folder containing information about identified techniques which go beyond GUI exploration.The artifact requires about 15MB of storage.### Dataset: 'BenchInfo.xlsx'This file list the full application dataset used for experiments into three tabs: 'BenchNotGP' (apps from AndroTest dataset which are not on Google Play), 'BenchGP' (apps from AndroTest which are also on Google Play) and 'TopGP' (top ranked free apps from Google Play). Each tab contains the following information:- Application Name- Package Name- Version Used (Latest)- Original Version- # Activities- Minimum SDK- Target SDK- # Permissions (in Manifest)- List of Permissions (in Manifest)- # Features (in Manifest)- List of Features (in Manifest)The 'TopGP' sheet also includes Google-Play-specific information, namely:- Category (one of 32 app categories)- Downloads- Popularity RankThe 'BenchGP' and 'BenchNotGP' sheets also include the original version (included in the AndroTest benchmark) and the source (one of F-Droid, Github or Google Code Archives).### RQ1: 'Coverage'The 'Coverage' folder includes coverage results for tools and analysts, and is structured as follows:- 'CoverageResults.xlsx": An excel sheet containing the coverage results achieved by each human analysts and tool. - The first tab described the results over all apps for analysts combined, tools combined, and analysts + tools, which map to Table II in the paper. - Each of the following 42 tab, one per app in TopGP, marks the activities reached by Analyst 1, Analyst 2, Tool 1 (ape) and Tool 2 (fastbot), with an 'x' in the corresponding column to indicate that the activity was reached by the given agent.- 'Plots': A folder containing plots of the progressive coverage over time of analysts, split into one folder for 'Analyst1' and one for 'Analyst2'. - Each of the analysts' folder includes a subfolder per benchmark ('BenchNotGP', 'BenchGP' and 'TopGP'), containing as many png files as applications in the benchmark (respectively 47, 14 and 42 image files) named 'ANALYST_[X]_[APP_PACKAGE_NAME]'.png.### RQ2: 'Reasons.xslx'This file contains the extracted reasons for unreachability for the 11 apps manually analyzed. - The 'Summary' tab provides an overview of unreached activities per reasons over all apps and per app, which corresponds to Table III in the paper. - The following 11 tabs, each corresponding to and named after a single application, describe the reasons associated with each activity of that application. Each column corresponds to a single reason and 'x' indicates that the activity is unreached due to the reason in that column. The top row sums up the total number of activities unreached due to a given reason in each column.- The activities at the bottom which are greyed out correspond to activities that were reached during exploration, and are thus excluded from the reason extraction.### RQ3: 'ActivationProperties.xslx'This file contains the full list of activation properties extracted for each of the 185 activities analyzed for RQ2.The first half of the columns (columns C-M) correspond to the reasons (excluding Transitive, Inconclusive and No Caller) and the second half (columns N-AD) correspond to properties described in Figure 5 in the paper, namely:- Exported- Activation Location: - Code: GUI/lifecycle, Other Android or App-specific - Manifest- Activation Guards: - Enforcement: In Code or In Resources - Restriction: Mandatory or Discretionary- Data: - Type: Parameters, Execution Dependencies - Format: Primitive, Strings, ObjectsThe rows are grouped by applications, and each row correspond to an activity of that application. 'x' in a given column indicates the presence of the property in that column within the analyzed path to the activity. The third and fourth rows sums up the numbers and percentages for each property, as reported in Figure 5.### RQ3: 'ActivationProperties-Graph.pdf'This file shows combinations of the individual properties listed in 'ActivationProperties.xlsx' in a graph format, extending the combinations described in Table IV with data (types and format) and reasons for unreachability.### BeyondGUIThis folder includes:- 'ToolInfo.xlsx': an excel sheet listing the identified 22 beyond-GUI papers, the date of publication, availability, invasiveness (Source code, Bytecode, framework, OS) and their targeting strategy (None, Manual or Automated).- ToolClassification.pdf': a pdf file describing our paper selection methodology as well as a classication of the techniques in terms of Invocation Strategy, Navigation Strategy, Value Generation Strategy, and Value Generation Types. We fully introduced these categories in the pdf file.## Requirements & technology skills assumed by the reviewer evaluating the artifactThe artifact entirely consists of Excel sheets which can be opened with common Excel visualization software, i.e., Microsoft Excel, coverage plots as PNG files and PDF files. It requires about 15MB of storage in total.No other specific technology skills are required of the reviewer evaluating the artifact.

This dataset (CSV) shows prices of iPhone 3GS mobile mobile phone including data in Australia in July 2009. An Excel version is linked below. Fields include: telco, price per month, data per month, price per GB of data (calculated), excess price per GB, description, link to URL. The iPhone 3GS is used as a comparison across several telco's pricing. Prices are included for several types of plans. Graphs of this dataset in an Microsoft Excel version of this data are linked below.

Graph and download economic data for NASDAQ 100 Index (NASDAQ100) from 1986-01-02 to 2025-07-01 about NASDAQ, stock market, indexes, and USA.

Graph and download economic data for Canadian Dollars to U.S. Dollar Spot Exchange Rate (EXCAUS) from Jan 1971 to May 2025 about Canada, exchange rate, currency, rate, and USA.